A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

Abstract

Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey encompassing multispectral and hyperspectral images, focusing on their applications for classification challenges in diverse agricultural areas, including plants, grains, fruits, and vegetables. By meticulously examining primary studies, we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use. Additionally, our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context. The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture. Nevertheless, we also shed light on the various issues and limitations of working with spectral images. This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

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DOI: 10.32604/cmc.2024.045101
REVIEW
A Systematic Literature Review of Machine Learning and Deep Learning
Approaches for Spectral Image Classification in Agricultural Applications
Using Aerial Photography
Usman Khan1, Muhammad Khalid Khan1, Muhammad Ayub Latif1, Muhammad Naveed1,2,*,
Muhammad Mansoor Alam2,3,4,SalmanA.Khan
1and Mazliham Mohd Su’ud2,*
1College of Computing and Information Sciences, Karachi Institute of Economics and Technology, Karachi, 75190, Pakistan
2Faculty of Computing and Informatics (FCI), Multimedia University, Cyberjaya, 63100, Malaysia
3Faculty of Computing, Riphah International University, Islamabad, 46000, Pakistan
4FacultyofEngineeringandInformationTechnology,SchoolofComputerScience,UniversityofTechnologySydney,Sydney,
Australia
*Corresponding Authors: Muhammad Naveed. Email: naveed@kiet.edu.pk; Mazliham Mohd Su’ud.
Email: mazliham@mmu.edu.my
Received: 17 August 2023 Accepted: 10 November 2023 Published: 26 March 2024
ABSTRACT
Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of
these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial
Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine
learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As
a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across
various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey
encompassing multispectral and hyperspectral images, focusing on their applications for classification challenges
in diverse agricultural areas, including plants, grains, fruits, and vegetables. By meticulously examining primary
studies, we delve into the specific agricultural domains where multispectral and hyperspectral images have found
practical use. Additionally, our attention is directed towards utilizing machine learning techniques for effectively
classifying hyperspectral images within the agricultural context. The findings of our investigation reveal that
deep learning and support vector machines have emerged as widely employed methods for hyperspectral image
classification in agriculture. Nevertheless, we also shed light on the various issues and limitations of working with
spectral images. This comprehensive analysis aims to provide valuable insights into the current state of spectral
imaging in agriculture and its potential for future advancements.
KEYWORDS
Machine learning; deep learning; unmanned aerial vehicles; multi-spectral images; image recognition; object
detection; hyperspectral images; aerial photography

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1Introduction
An image constitutes an array of pixels originating from diverse sources, such as standard
specialized cameras or mobile phones. The significance of imaging extends across various domains,
particularly in detection and recognition tasks. Image processing techniques are the initial step for
computational methods by extracting valuable information from the provided image. By utilizing
this extracted information, computers can attain heightened intelligence in tackling real-world and
intricate challenges [1]. One of the limitations commonly found in traditional image processing tech-
niques is the inability to acquire spatial and spectral information for various objects [2]. Spectroscopy
investigates the behavior of light within an object, enabling the identification of materials through their
distinct spectral signatures.
Nevertheless, traditional imaging methods face limitations in covering a large volume since they
cannot extract spectral information from such images. As a result, there is an ongoing demand for
novel technologies and sensors to facilitate the automatic detection and recognition of diverse objects
[3]. Over the past two decades, notable progress has been made in the application and enhancement
of image sensors and imaging techniques, with particular emphasis on the fields of agriculture and
food [4]. Technological advancements have enabled researchers to classify food products based on
color, quality, size, and weight [1]. An interesting feature of spectral cameras is that they are capable of
seeing more than “just” colors. They capture the image within the selective wavelength ranges through
the electromagnetic spectrum.
Furthermore, they can also measure light in a small number of spectral bands and a large
number of spectral bands, typically between three and several hundred. The separation of wavelengths
can be done by filters or using instruments that measure specific wavelengths, including light from
different frequencies above or belowthe visible light range, such as infrared. Some cameras can capture
hundreds of spectral bands, and this phenomenon is known as hyper-spectral imaging [5,6].
The human eye can perceive electromagnetic waves with wavelengths ranging from 380 to 780
nanometers in the visible spectrum. Any electromagnetic waves with wavelengths beyond this range,
such as infrared, remain invisible to humans [7,8]. Therefore, spectral imaging enables acquiring
additional information that surpasses the human eye’s capabilities. There are three feasible methods
for collecting spectral image information: (i) A camera with optical elements, (ii) A continuous filter
like the filter wheel, and (iii) A complementary metal oxide (CMOS) sensor with a filter. The spectrum
essentially characterizes the amount of light in each wavelength, providing valuable insights into the
emission, transmission, or reflection of light from an object. Over the past two decades, various image-
sensing technologies have emerged, with hyperspectral and multispectral imaging being two highly
effective methods [9,10]. The primary distinction between hyperspectral and multispectral imaging is
the number of distinct wavebands they capture. Multispectral imaging typically involves fewer than 15
bands, while hyperspectral imaging can encompass hundreds of bands. Additionally, hyperspectral
imaging offers a complete spectrum for each pixel, whereas multispectral imaging provides only
isolated data points [11–15]. Spectral cameras with optical elements consist of a prism, a sensor,
gratings, and lenses. The light enters the camera through a slit. The prism and gratings refract the
light. The line scan sensor can then generate a multispectral image, line by line. The filter wheel must
be moved to the desired filter. This results in a reduced scanning speed, so UAVs are not usable for
moving targets. Moreover, there are also CMOS sensors with band filters integrated into the sensor’s
layout. Compared with the other scanning types, cameras with spectral filters on CMOS sensors
have no optical parts that require alignment. They can capture objects with different wavelengths
in one single shot. A snapshot spectral camera captures multiple ultra-violet, visible, and infrared

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images. Each image with a multispectral camera is passed through a filter to keep light to a specific
wavelength or colour. Spectral imaging can extract features for additional information that the human
eye cannot recognize with its receptors for RGB. Spectral images are one of the most critical images
attained by remote sensing. Remote sensing radiometers categorized the spectrum into various bands.
Recent research trends with the help of Multispectral Images are in the fields of Printed Circuit
Board (PCB), counterfeit detection on banknotes [12–14], skin characterization in dermatology [15],
food inspection in the agriculture sector, etc. [16–20]. The existing systematic literature review on
multispectral and hyperspectral imagery provides the details related to the specific domain. A review is
done by Recetin et al. [21] on anomaly detection-related studies using remote sensing applications with
the help of hyperspectral while Aloupogianni et al. [22] focused on the main area of tumor diseases
using both images, hyper and multispectral imagery. The health sector is again the point of attention by
Ortega et al. [23], where digital and computation pathology and hyper and multispectral imagery are
reviewed. The spectral resolution can be increased with the help of a new pan-sharpening technique
for multispectral images. Different pan-sharpening techniques are reviewed for multispectral images
in [20]. The comparison of SVM and convolutional neural networks is studied by Kaul et al. [24].
They also reviewed the most popular dataset for the same type of problems. Datta et al. [25], discussed
the challenges and future scope using hyperspectral along with the benefits of using machine learning
and deep learning. Deep learning using hyperspectral is studied by Ozdemir et al. [26] with future
directions. This study offers a wealth of detailed information, encompassing comprehensive answers
to all the research questions. For a summarized overview of the relevant systematic literature review,
please refer to Table 1.
Table 1: Comparison of the proposed research with the existing literature
Studies Trends Health
sectors
ML&DL
analysis
Popular data
sets
Identifying
agriculture sec
Future
directions
[21]XX X X 
[22]XX X 
[23]XXX X 
[20]XX X X X
[24]XX X
[25]XX X
[26]XXX XX
Our paper X  
The research questions formulated for this study are structured to comprehensively address
all inquiries about multispectral and hyperspectral images and their applications in the agriculture
industry. Moreover, the study concentrates on elucidating the methodologies and various techniques
employed to address agricultural challenges effectively through the utilization of hyperspectral or
multispectral images. The addressed research questions (RQ) for this study are:
RQ1 .In what diverse research areas within agriculture have Multispectral or Hyperspectral images
found applications?
RQ2 .Which Machine Learning and Deep Learning Techniques have been employed to tackle
agricultural problems using Multispectral and Hyperspectral images?

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RQ3 .What are the prevalent datasets extensively utilized for spectral classification purposes?
RQ4 .What are the challenges, limitations, and potential future developments concerning Hyperspec-
tral and Multispectral images in conjunction with Deep Learning and Machine Learning Models?
The systematic literature review methodology was selected to categorize studies that explore
the convergence of spectral images and agriculture fields. This study comprehensively classifies the
research questions concerning multispectral and hyperspectral images. The exponential advancement
of spectral images has significantly enhanced the accuracy of classification problems. The primary
goal of this paper is to review the wide-ranging applications of spectral images and the frequently
employed techniques alongside them. Additionally, we focus on the commonly used datasets in spectral
applications, their limitations, and potential areas for future research.
The paper is structured as follows: Section 2 delves into the background of Multispectral imaging,
Hyperspectral imaging, Machine Learning, and Deep Learning. In Section 3, we explain the materials
and methods used for the systematic literature review. In Section 4, we present the results, offering
comprehensive and in-depth answers to all the research questions posed, and Section 5 presents
the challenges and future directions of our study. Finally, in Section 6, we conclude the paper by
summarizing our findings and suggesting potential areas for future research.
2Background
2.1 Multispectral Imaging
Multispectral imaging is a new and emerging area with diverse application domains such as
agriculture, health and sciences, geo monitoring, and environmental changes. Recent research trends
indicate that the synergy of multispectral images with other fields has generally proven effective
in urban and regional planning [27]. This section discusses some of the recent research around
multispectral images. One crucial problem is soil analysis, particularly soil salinization, where soil
degradation is a prime issue. Hu et al. [6] attempted to solve this problem with an unmanned aerial
vehicle (UAV) borne multispectral imager. Remote sensing is very useful for collecting data for
agriculture applications. Zheng et al. [3] used different modeling algorithms, including parametric and
non-parametric algorithms, for estimating leaf nitrogen content (LNC) in winter wheat. This was done
with the help of multispectral images using UAVs. With the help of classification algorithms, including
linear regression (LR), random forests (RF), and support vector machine (SVM) alongside UAV
multispectral images, the prediction of canopy nitrogen weight in Corn was discussed by Lee et al. [28].
They reported that RF and SVM are better than LR for the problem. In weed monitoring, the
main issue is determining weed amount and location. For this problem, three methods are proposed
by Osorio et al. [29] using deep learning and multispectral images with the comparison of visual
estimations from the experts. The proposed methods are SVM, You Only Look Once Version 3
(YOLOV3), and Region-based convolutional neural network (R-CNN). The F1 scores were 88%, 94%,
and 94%, respectively. Another area where multispectral images are used is related to oil pollution,
a severe environmental issue. Ozigis et al. [30] worked on detecting oil pollution and its impact
on biological elements. They used multispectral and multi-frequency images, fuzzy logic, and RF
methods. An overall accuracy of 75% for dense areas of vegetation was obtained. Furthermore,
the Cropland and grassland areas had an accuracy of 59.4% and 65% respectively. Lima et al. [31]
developed a system for early-stage supervision of the status of organic fertilization on tomato plants.
The proposed system was developed using a multispectral camera with five lenses including green,
red, red edge, near-infrared, and RGB alongside with computation image processing mechanism.
One of the successful mechanisms of monitoring the characteristics of vegetation by exploiting the

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Normalized Difference Vegetation Index (NDVI). With the help of multispectral images, Cao et al. [32]
identified that using wide-dynamic-range vegetation index (WDRVI) derived from UAV multispectral
image monitoring of sugar beet growth indicator is very useful as sugar beet has very large ground
biomass.
Monitoring several crops (cultivated areas) is very challenging since identifying different tops
is very costly. Sicre et al. [33] attempted to find different ways to use satellite images (both optical
and radar) to classify the land covers. Their suggested method was dependent on the wavelength and
penetration depth of the signal providing the images. Lei Ma et al. [23] reviewed the land cover object-
based classification in different research publications. The findings indicate that remote sensing-based
imagery was the dataset most frequently used.
UAV can monitor leaf area index (LAI). Qi et al. [34] proposed new ways to find the high-precision
LAI prediction system with the help of multispectral images. Multispectral images are also useful in
classifying potato defects [35–37]. In addition, detecting bruises on apples is also a very challenging
classification task when using multispectral images. Additionally, classifying bruises is very important
when discussing the automatic apple classification system. With the help of principal component
analysis (PCA), hyperspectral imaging and multispectral imaging systems for selective bands were
developed by Huang et al. [16], which achieved 90% accuracy. Powdery mildew (PM) is a fungal
disease that can cause powdery growth on the surface of plants, leaves, and fruits. Early detection of
powdery mildew is very crucial. Chandel et al. [17] studied using multispectral images for PM detection
and mapping, achieving 77% accuracy. Bhargava et al. proposed a binary classifier [18] for sorting
mono and bi-color apples. They achieved different accuracy on different datasets using multispectral
imaging. Feng et al. showed the results in [19] that indicate that fruit recognition using multispectral
images can be very effective. The current state of the art and new applications of remote sensing in
agriculture were discussed by Khanal et al. [38]. Applications related to plant disease detection, yield
estimation, water stress monitoring, etc., were discussed.
2.2 Hyperspectral Imaging
The hyperspectral image consists of several bands that have unique classifications. Each segment
of the pixel can be treated as a unique label that identifies a specific target class. After image acquisition,
a hypercube is formed, which contains information regarding both spectral and spatial data. As the
hypercube has three dimensions, the image’s resolution helps in two dimensions, while several bands
assist in-depth, which is the 3rd dimension. Ranjan et al. [39] used principal component analysis (PCA)
for feature extraction for hyperspectral image classification, and with the help of K-means clustering,
they formed different clusters. After that, all clusters were trained using SVM. The accuracy of this
approach was recorded to be 90%, 77%, and 88% for three different types of classes. They concluded
that accuracy was better when data was initially divided into different groups before classification.
Research on crop height for maize biomass by Zhang [40] used UAV hyperspectral imagery. Prediction
of Aboveground biomass (ABG) using stepwise regression and XGBoost regression model with
the highest accuracy of R-Squared (R2) of 0.81 and Root Mean Square Error (RMSE) of 0.27
was obtained, showing that hyperspectral imagery can play a vital role in the estimation of maize
aboveground biomass with better accuracy. Rubio-Delgado et al. [41] proved that hyperspectral images
effectively estimate the determination coefficient between spectral data and leaf nitrogen content. They
used a full spectrum range for vegetation indices (VI) and Partial Least Square Regression (PSLR)
models and achieved a large R2value. Results can be improved by using temporal information on
leaf nitrogen content. Messina et al. provided a comprehensive research summary of remote sensing’s
contribution to the growth of olive farm management in the last two decades [42].

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Yao et al. provided a complete review of remote sensing applications using UAVs [43]. Selected
applications were from precision agriculture and vegetation, urban environment and management,
and disaster hazards and rescue. The advantages and selected areas were also highlighted in the
conclusion. Gautam et al. studied the water status of horticultural crops using different remote sensing
applications [44]. Remote measurements of water status, such as soil moisture, canopy 3D structure,
etc., remote sensing is the best tool for planning and managing different applications for larger areas.
Yan g et a l. [ 45] found that airborne hyperspectral imagery is used in mapping cotton yield. With
stepwise regression’s help, 61% and 69% variability for two yield fields were observed. They concluded
that due to narrow bands, hyperspectral imagery is much better than multispectral imagery, which uses
broad bands. Zhang et al. [46] used hyperspectral images to observe water quality and proposed that
a deep learning model could monitor water quality concentration. Mean absolute percentage error
(MAPE) was 8.78% and 12.36%, and R2was 0.81 to 0.93. They suggested using cloud stations for
a reduction in time and costs. Coverage using UAVs can also be much broader so that more data
can be collected quickly. It is very important to use an optical sensor to detect crop stress, which can
specifically find spectral wavelength affected by stress factors. It is also essential to consider which
spectral resolution is required to prevent it. Mewes et al. [47] discovered that the problem is reducing
the spectral resolution and spectrum angle mapper. The proposed method for this problem was SVM.
The results show that only a few hyperspectral bands are enough to detect fungal infection in wheat.
In contrast, the authors also suggested using the derived result for feature selection in agriculture.
Cen et al. [48] examined the internal defects of pickling cucumbers using hyperspectral images. The
performance measure was minimum redundancy–maximum relevance (MRMR) with 95% and 94%
accuracy on two different conveyor belt speeds. They primarily worked on identifying several optimal
wavebands for PCA for fast and efficient algorithms and light sources. Further study can be done in
discrimination between slightly and severely defective labels.
2.3 Machine Learning
Machine Learning is a branch of Artificial Intelligence that deals with problems related to predic-
tion and classification. The phrase ‘learning’ relates to the amount of data given to an algorithm, and
more data would mean more learning. For any task (T), if the algorithm’s performance (P) increases
with respect to experience (E), then the algorithm is said to be a machine learning algorithm. Machine
Learning is fundamentally classified into three types: (1) Supervised Learning, (2) Unsupervised
Learning, and (3) Reinforcement Learning. In supervised learning, the model is trained with labelled
data and predicts the labels from new data. In supervised learning, learning is done from a dataset,
making predictions iteratively, and with each iteration, the parameters of the algorithms are adjusted
accordingly. Supervised learning includes LR, Logistic Regression, Nearest Neighbors, Decision Tree,
RF, SVM, etc. In unsupervised learning, the model is trained with unlabeled data and groups the data
based on feature similarity. Unsupervised learning tries to find the patterns in the dataset without
any pre-existing target labels. It helps in finding insights into data. Sometimes, unsupervised learning
performs complex tasks better than supervised learning. Clustering, association, and dimensional
reduction are the main approaches that use unsupervised learning [49]. In reinforcement learning,
the algorithm performs actions that maximize performance in particular conditions. Fig. 1 shows the
three types of machine learning and their differences.
Fig. 2 explains the overall flow of a machine-learning algorithm. In the first phase, the training
phase, the data or training examples go to the feature extractor for feature extraction.

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Figure 1: An overview of types of machine learning algorithms
Figure 2: An overview of machine learning model flow
After getting features from training examples, the model for machine learning is trained; once the
training model is ready for the prediction, the system takes a new test example and classifies/predicts
the output.
2.4 Deep Learning
Deep learning is a subset of machine learning inspired by the human brain, consisting of billions of
neurons. Deep learning uses Artificial Neural Network algorithms. The word deep refers to the number
of layers in a network. If the number of layers is unknown, the system automatically adjusts the weights
using an optimization function. The layer consists of several neurons. Since it mimics the human brain,
deep learning also requires much data for better accuracy [50]. Although deep learning evolved from

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machine learning, the two have some differences. The main difference is that deep learning requires
large amounts of data, and features are not extracted before applying the deep learning model. Deep
learning can automatically extract the features that further process the data for classification. The
concept of deep learning is illustrated in Fig. 3 for further clarification.
Figure 3: Deep learning model flow showing feature extraction and model prediction
3Materials and Methods
A systematic literature review (SLR) was carried out according to the approach suggested by
Kitchenham [51]. We performed the following steps to get the answers to our research questions:
(1) Search, (2) Exclusion and Inclusion Criteria, (3) Publisher Selection, (4) Study Selection, and (5)
Search Strategy.
3.1 Search
The databases used in the search were Springer, Science Direct, IEEE Xplore, and ACM. The
timeline for the manuscripts was between 2017 and 2022 because of the most recent publication in
multispectral and hyperspectral images. We have used four search strings for the survey.
Search String 1. (Multispectral Images) AND (Agriculture) AND (Deep Learning)
Search String 2. (Multispectral Images) AND (Agriculture) AND (Machine Learning)
Search String 3. (Hyperspectral Images) AND (Agriculture) AND (Deep Learning)
Search String 4. (Hyperspectral Images) AND (Agriculture) AND (Machine Learning)
3.2 Exclusion and Inclusion Criteria
The exclusion and inclusion criteria were straightforward. We selected the research from 2017
onwards and did not include any articles before 2017 in our primary studies. The reason for selecting
the last five years is to provide the recent developments in hyperspectral and multispectral images. All

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the selected articles are peer-reviewed, and all articles are searched using the mentioned string. All
articles were written in English, and articles in other languages that appeared during the search were
not considered. Fig. 4 depicts the inclusion and exclusion criteria.
Figure 4: Exclusion and inclusion criteria for the selection of primary studies
3.3 Publisher Selection
We search the articles from the following databases: SPRINGER, SCIENCEDIRECT, IEEE
XPLORE, and ACM. Surprisingly, we did not find papers in IEEE Xplore despite trying the search
with each of the four strings. Fig. 5 shows the well-known publishers from where articles are selected.
Figure 5: Selected databases for this research

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Fig. 6 shows the initial frequency comparison of all the research articles from selective publishers.
We found that Google scholars have comparatively large numbers of papers compared to other
databases.
Figure 6: Search string result comparison
3.4 Search Strategy
The search strategy involved an automated search, as depicted in Fig. 7. The automated search
proved instrumental in locating and providing us with 30,100 research articles for the study, out of
which 27,900 papers were obtained from Google Scholar. Consequently, we excluded the articles
sourced from Google Scholar to avoid redundancy.
Figure 7: Preferred reporting items for systematic reviews and meta-analysis (PRISMA)

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Following the exclusion process, we extracted 2,200 articles for this study. Subsequently, we applied
inclusion and exclusion criteria based on titles, abstracts, year of publication, and keywords, resulting
in 185 articles remaining after the screening process. In the next phase, we applied the selection criteria
to the full-text studies, and we were left with 44 primary studies. Our selection criteria aim to include
multispectral or hyperspectral images or images with deep learning or machine learning in agriculture.
Fig. 8 shows the distribution of selected/primary studies for selected years after applying all the
mentioned criteria and selection mechanisms.
Figure 8: Articles year-wise number count
4Results
This section provides a concise and precise description of the experimental results, their inter-
pretation, and the experimental conclusions that can be drawn. The distribution of all the number
of primary studies is given in Tabl e 2 . We can see that in 2020, the number of articles increased
comparatively compared to others. The overall number of articles was found to be 44. We have
presented the analysis of results corresponding to the research questions outlined in the Introduction
section at the beginning of this paper. In Fig. 9, we have provided a detailed analysis of the articles,
including their respective publishers, to enhance clarity and understanding. Additionally, we present
a comparison of yearly articles based on publishers.
Table 2: Number of articles by year
Year Number of studies Articles
2017 1 [52]
2018 5 [53–57]
2019 9 [58–66]
2020 16 [67–83]
2021 10 [84–93]
2022 3 [94–96]

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Figure 9: Yearly distribution of articles categorized by publishers
4.1 Analytical Findings Corresponding to RQ1
RQ1. In what diverse research areas within agriculture have Multispectral or Hyperspectral images
found applications?
The selection of primary studies focused on hyperspectral and multispectral applications in
agriculture. To address our first research question (RQ1), we categorized all relevant agricultural works
into four main classes: grains, fruits, vegetables, and plant issues. The distribution of these classes is
illustrated in Fig. 10 using a Pie chart, revealing that 43% of the selected studies were related to fruits.
Figure 10: Classification of studies for major agriculture classes
These classes encompass a range of diverse subclasses. Under the grains class, we included research
studies on rice and various types of seeds. In the plant class, we focused on issues such as citrus greening
detection, coffee ripeness, and yield monitoring, identification of apple and rose flowers, detection of
different leaf diseases and deficiencies, crop water detection, mapping of agriculture, and vine disease
detection. The summarized structure is presented in Table 3. The vegetable class comprised primary
studies on defect detection and grading of potatoes and tomatoes and yield forecasting of carrots and
cucumbers. However, our primary emphasis was on fruits, which involved the detection and grading
of apples, estimation of mangoes’ maturity, identification of strawberry diseases and ripeness, early
detection of decayed oranges, assessment of pineapple quality, and detection of pears crown bruises,
along with bruise detection of various fruits and recognition of on-branch fruits.

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Table 3: Summarized structure of RQ1
S. no. Research area Detail Referenced article
1 Grains Rice lodging resistance, identification of
single herd seed, discrimination of
high-quality watermelon seed
[71,80,87,96]
2 Plant Coffee ripeness monitoring, apple flower
detection, coffee yield prediction, plant
leaf disease detection, nutrition
deficiencies in plants, crop water stress,
citrus fruits detection, agriculture
mapping, citrus greening detection, vine
disease detection, rose plant identification
[52,57,58,60,61,65,67,70,74,77,
83,85,92,95,97]
3 Vegetables Potato genotype, external defects on
tomatoes, bruise detection on different
vegetables, tomato discrimination and
grading, plant level tomato biomass and
yield, carrot yield forecasting, internal
defect detection of pickling cucumbers
[48,54,56,64,75,81,88,94]
4 Fruits Apple fruit sorting, maturity estimation of
mangoes, fruit detection in apple orchard,
quality control of apples, bruise detection
on different fruits, apple leaf condition,
strawberry disease, extraction of apple tree
crown information, strawberry ripeness,
fuji apple detection, blossom detection in
apple, defective apple detection, on branch
fruit recognition green mangoes detection,
strawberry maturity classification, early
decayed oranges, pineapple quality, apple
fruit disease, early bruise detection on the
surface of crown pears
[53,55,59,62,63,68,70,72,73,78,
79,82,84,86,89,90,91,93]
4.2 Analytical Findings Corresponding to RQ2
RQ2.Which Machine Learning and Deep Learning Techniques have been employed to tackle
agricultural problems using Multispectral and Hyperspectral images? To address RQ2, we thoroughly
examined all the selected articles, which involved 17 distinct algorithms. Notably, the most frequently
employed techniques were from deep learning, utilized in 27 instances, followed by SVM, used 13
times.
Fig. 11 illustrates all the machine learning and deep learning techniques employed with multi-
spectral and hyperspectral images. Certain techniques were applied to address specific agricultural
problems, and researchers recommended their continued use for similar challenges due to algorithmic
and data limitations. We have categorized these techniques into five groups: Deep Learning techniques,
SVM, K Nearest Neighbors, and Logistic Regression, and the remaining methods were grouped

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under “Other.” Fig. 12 illustrates the occurrence frequency of each technique in our selected studies.
Additionally, Tabl e 4 provides a study-wise classification of Machine and Deep Learning algorithms.
Figure 11: An overview and classification of machine and deep learning techniques
Figure 12: Machine learning and deep learning techniques for RQ2
Deep Learning, particularly Convolutional Neural Networks, has shown remarkable performance
in various spectral classification problems [53,55,61,62]. Studies suggested that Convolutional Neural
Networks are very well-suited for spectral image classification because they can automatically learn
discriminative features directly from the input data without the need for explicit feature engineering
[74–76,83,88–92]. They can capture complex patterns and relationships in spectral images, leading to
accurate classification results. Deep Learning models can handle large amounts of data and effectively
learn from diverse spectral bands, making them suitable for hyperspectral and multispectral image
analysis [52,58].
Conversely, in chosen studies, SVMs are extensively employed owing to their capacity to manage
high-dimensional feature spaces and effectively handle nonlinear data [57]. SVMs seek to find the
optimal hyperplane that maximally separates different classes in the feature space [69,70]. They can
handle the curse of dimensionality by utilizing the kernel trick, which implicitly maps the input data
to a higher-dimensional feature space, allowing for better separation of classes. SVMs have a strong
theoretical foundation, work well with small to medium-sized datasets, and have been successfully
applied to spectral image classification tasks [93]. Deep Learning was the most preferred choice of
researchers when working with multispectral and hyperspectral images in the agriculture domain.

CMC, 2024, vol.78, no.3 2981
Table 4: A task-based algorithmic summary of RQ2 based on individual studies
Ti Article Technique
Detection
[64] Decision tree
[61,66,69,80,93] Support vector machine
[53,55,61,62,69,72,74–76,83,91], Deep neural network
[93] Random forest
[61,69,90] Logistic regression
[71] You only look once
Detection and classification
[52] Decision tree
[52,58,65,67] Support vector machine
[52,58] Deep neural network
[33] K nearest neighbors
Classification and regression [56,67] Deep neural network
[67,70] Support vector machine
Classification
[57,86] Support vector machine
[54,63,78,86,88,89,94] Deep neural network
[58]K-means
[59,70] K nearest neighbors
[69] Adaboost
[60] Watershed segmentation
[85] You only look once
[61] Genetic algorithm
[61] Multiple linear regression
[57] Random forest
[80] Principal component analysis
[57] Back propagation neural network
[75] Naive bayes
Detection and segmentation [68,84] Deep neural network
Regression [70] Partial least square
[92] Ridge regression
Recognition [73,87] Deep neural network
[37] K nearest neighbors
Regarding different types of tasks, classification is the most widespread problem while working
with spectral images. We find that there are several reasons for the popularity. One of the primary
objectives in agricultural applications is to identify and classify different plants, diseases, or crops.
Classification provides specific interpretable results so agriculture experts can easily understand and
treat them accordingly. While regression techniques can be used for crop yield estimation, classification

2982 CMC, 2024, vol.78, no.3
can be a precursor to yield prediction. Identification of weeds and their management are essential parts
of agriculture. Weed detection can reduce its competition with crops. Thus, different classification
models on spectral images can be trained to assess the health of crops. The health of crops can include
stress signs, different deficiencies, or any disease that can affect crop growth. We have already stated
that SVMs and deep learning models have proven more effective while working in spectral image
analysis in the agriculture sector.
4.3 Analytical Findings Corresponding to RQ3
RQ3: What are the prevalent datasets extensively utilized for spectral classification purposes?
Since multispectral and hyperspectral imagery is an emerging field, many datasets can be found in
the literature. Here are some of the most extensively utilized datasets. The Xuzhou dataset was collected
in 2014 at the Xuzhou peri-urban site. This dataset consists of 500 ×260 pixels with 9 available classes.
The Salinas dataset is a widely used benchmark dataset for hyperspectral image analysis. It represents
an agricultural area in Salinas Valley, California, and contains hyperspectral imagery acquired by an
airborne sensor. The dataset consists of 512 ×217 pixels with 224 spectral bands of 16 classes. The
Pavia Centre imagery dataset is collected at the University of Pavia, Italy, using a reflective optics
system imaging spectrometer (ROSIS). Nine distinct land cover classes are accessible, with a spatial
resolution of 1096 ×1096. The Pavia University dataset was also captured in the University of Pavia
in 2001, having a spatial resolution of 610 ×640 and a total of 9 classes of urban environmental
construction. Houston dataset consists of 380–1050 nm spectral wavelength with 48 bands. This data
was provided by the IEEE GRSS data fusion contest and captured by the University of National
Center of Airborne Laser mapping on the premises of the University of Houston. The Kennedy Space
Centre dataset was collected in 1996 at Kennedy Space Center, Florida. In Tabl e 5 , we have compiled
a summary of the dataset, including the number of available classes, spectral size, and spatial size.
Table 5: Synopsis of the dataset, encompassing spatial resolution, spectral size, and the number of
classes
Name of dataset Spatial resolution Spectral size Number of classes
1Xuzhou 500 ×260 436 9
2Salinas 512 ×217 224 16
3Pavia Centre 1096 ×1096 102 9
4Pavia University 610 ×640 103 9
5Houston 380 ×1050 191 15
6Kennedy Space Centre 512 ×614 224 13
7Hekla 2084 ×614 157 22
8Trento 600 ×166 63 6
9Botswana 1476 ×256 145 14
10 Indian Pines 145 ×145 220 16
This data is collected with the help of JPL’s Airborne imaging spectrometer, which has 13 classes
and a spatial resolution of 512 ×614. The Hekla dataset has a spatial resolution of 2084 ×614 and
a spectral size of 157. There are t22 classes available in this dataset. The Trento dataset was collected
with the help of the AISA eagle sensor. The total number of classes is six, and the spatial resolution

CMC, 2024, vol.78, no.3 2983
is 600 ×166. Botswana data was collected using a Hyperion sensor over the Okavango Delta in
Botswana, South Africa, in 2001. This consists of 14 land cover labels with a spatial resolution of
1476 ×256. Indian Pines dataset was obtained by AVIRIS sensor in northwest Indiana, United States
of America. This dataset has 16 labels of land covers and 220 spectral bands in the range of 0.2 to
2.4 μm, having a spatial resolution of 145 ×145 pixels [95–97].
Indian Pines,Salinas,andHouston datasets are commonly utilized in various studies. Additionally,
the Pavia University dataset and the Pavia Centre and Kennedy Centre datasets hold a prominent
position among the most widely employed datasets in spectral imagery [98–100].
4.4 Analytical Findings Corresponding to RQ4
RQ4: What are the challenges, limitations, and potential future developments concerning Hyperspec-
tral and Multispectral images in conjunction with Deep Learning and Machine Learning Models?
Some primary studies suggest employing the same images with alternative classification algo-
rithms to compare results. Within our selected primary studies, various issues were addressed using
limited data, prompting the recommendation for larger datasets to achieve improved accuracy. How-
ever, due to environmental and camera bandwidth constraints, certain limitations persist. Interestingly,
some research asserts that multispectral images are suitable for classifying various types of fruits and
vegetables, while others advocate their use for flowers. To provide a comprehensive overview, we have
summarized these findings, including limitations and challenges, in Tabl e 6 . These findings pave the
way for future research in the expansive domain of multispectral and hyperspectral imagery.
Table 6: Limitations of the work, datasets description and working details
Article Issues/Challenges Data set Worked on Image type
[59] Comparative analysis of
other classification
algorithm for the same
problem
Leaves images of four
categories orange, pink,
red, white, 25 each with
a23MPcamera
Evaluate the
performance of k
nearest neighbor for
rose classification using
five features each from
histogram and texture
related characteristics
Other
[83] The learning process is
slow and can be separated
the for feature selection
process and this method
can be applied to other
fruits as well
Multispectral images of
(450,500,750,800) nm of
infected and healthy
apples, bi-color images
total of 3279 images
Deep neural networks
with a high number of
layers can improve the
accuracy of the apple
fruit disease diagnosis
Multi
(Continued)

2984 CMC, 2024, vol.78, no.3
Table 6 (continued)
Article Issues/Challenges Data set Worked on Image type
[68] Small size of training
samples can be improved
by enriching the dataset
with more disease samples
and can be tested on other
deep learning architecture
Visible and infrared
images 850 nm four
classes 70,560 patches
of size 32 ×32
Studying imaging
modalities in the visible
andinfraredspectral
domains, how can we
combine two types of
images to delineate
symptomatic areas
Hyper
[69] Application scenario is
restricted, can be tested on
different stages of growth,
hard to fit for every growth
stage, different
multispectral camera has
different bandwidth
7695324 samples of
citrus greening infected
and healthy images
Feature pre-processing,
feature extraction,
machine learning
models and accuracy
improvement on
unmanned aerial vehicle
multispectral images
Hyper
[70] Possibility of testing new
algorithm can lead to
better results; also, further
work can be done
including feature selection
and data fusion techniques
Multispectral images in
18 different wavelengths
from 450–970 nm
Mathematical model
basedondifferent
analytical instruments,
comparison of model
performance and
explore the capabilities
and limitations of tools.
Multi
[60] Can be improved with zero
tolerance, image processing
algorithm can be more
optimized by using a large
number of samples,
proposed method can be
applied to other fruits
Multispectral images of
seven different
wavelengths from
575–960 nm
Watershed
segmentation method
with multispectral
principal component
and image
reconstruction
Multi
[84] Accuracy can be
maximized by considering
the early growth of crop
yield
PASCAL visual object
class challenge dataset
for object class
recognition, raster
dataset using extract,
transform, and load
script, point cloud
dataset
Automated all the tasks
like high resolution,
multispectral imagery,
labeling, input dataset
preparation
Other
(Continued)

CMC, 2024, vol.78, no.3 2985
Table 6 (continued)
Article Issues/Challenges Data set Worked on Image type
[85] Method can be proposed
for flowers and fruits
which are hidden under the
leaves, better accuracy can
be achieved with a
high-resolution camera,
more images can be used
with a high number of
cameras
Ten rows of
strawberries, 3875
images with 70% frontal
overlap and 60% side
overlap. All images from
the Plant science
research and education
unit in the USA
Automated
classification of the
strawberry maturity
system was developed
Other
[71] Detection of different
cultivars by retraining the
model. And in different
orchards, different
cultivars, age, pruning style
Images were 544 ×544
pixels; batch size was 64
Unmanned aerial
vehicle images instead
of ground-based
photography for the
same cultivars
Other
[72] Using thermal time to
improve accuracy,
investigated the
performance of shape
model fitting based
method in the absence of
high temporal, and
monitoring the accuracy of
convolutional neural
network-based approach in
mono temporal imagery
63360 training,
validation 13580 testing,
20440 images
Mono-temporal images
of unmanned aerial
vehicles to estimate crop
phenology for a large
area after training
Other
[73] The proposed method can
be trained with large data
set, can use transfer
learning
RGB images of six
different categories
from local orchards of
Iran, different batch
sizes of 16, 32, and 64
with 50 epochs. Total
739 and new dataset
after augmentation
12443
Data augmentation for
creating more data with
original data by
applying some
transformation. New
images with old features
with certain changes,
because deep learning
needs a large number of
data
Other
(Continued)

2986 CMC, 2024, vol.78, no.3
Table 6 (continued)
Article Issues/Challenges Data set Worked on Image type
[74] Convolutional neural
networks can be combined
with mass spectrometry
imaging for the
illumination and detection
improvement
Fuji apples total 0f 300
after initial image
acquisition total of 3300
images and after
rotation with 3 different
angles total of 79200
images was formed
In contrast to other
convolutional neural
network models for the
same problem the speed
of the proposed model
is better (5 fruits/sec)
Other
[75] Results of the detection of
blossoms can be used with
depth information which
can further allow spatial
distribution of blossoms
and new rules can be
developed for image-based
removal systems.
Multiclass object detection
for canopy parameters
Data collection was
done in Washington
USA orchard in April
2018 and 2019 selective
period
Deep learning based
blossom detection
method
Other
[87] The same model can be
tested on a new test dataset
for enhancement and will
update the dataset with a
new model and different
sensors with different
combinations can also be
used for monitoring
500 RGB images which
further disease diversity
resulting in 2100 images
with 450 epochs for
training
Effective optimization
method for counting
lesions at canopy level
using majority voting
Alsoworkedonthe
relationship between the
actual score and the
estimated score
Other
[76] The model can be tested on
other fruits as there is no
specific information
relatedtotomatoesgiven
to the model during the
training phase
43843 images using
different sensors were
used
Only defect detection,
differentiate in defect
detection is not done
Other
(Continued)

CMC, 2024, vol.78, no.3 2987
Table 6 (continued)
Article Issues/Challenges Data set Worked on Image type
[62] Less amount of data is a
drawback when using any
neural network, also
results were affected due to
the RGB-D depth sensor
under sunlight. In the
future 3D fruit localization
can be done with the help
of 2D
Fuji database 967
multimodal images and
a total of 12839 images
of Fuji apples
Different channel
registration including 3
modalities RGB color,
depth, and intensity of
range corrected
Other
[77] More sample images with
the internal and external
quality of strawberries at
different stages of ripeness
Total of 500 images 336
and 144 grayscale
images 227 ×227 pixel
Strawberry fruits
ripeness at two different
stages (early ripe and
ripe) with hyperspectral
imaging data
Hyper
[90] Some models have the
same type of errors for all
features and need
improvement for feature
selection, also difference in
mean absolute percentage
error for a top month and
three months is very little
so only one month of data
can be used. In future
multispectral and
hyperspectral data can be
used for further
improvement
Data collection from
June 2017 and May
2018 for 144 trees in
Minas Geris Brazil
Seven different
regression methods were
used for the selection of
the best features
Other
[78] The model can be deployed
in an embedded system
using unmanned aerial
vehicle, also tree detection
and segmentation datasets
should be enriched before
training and different
methods for fruit tree
information can be used
50 images resolution of
5472 ×3648 unmanned
aerial vehicle images
Tree data acquisition
model for apple tree
data which consists of
counting, detecting, and
segmentation apple
trees and detection of
crown parameters of
apples
Other
(Continued)

2988 CMC, 2024, vol.78, no.3
Table 6 (continued)
Article Issues/Challenges Data set Worked on Image type
[94] The same system can be
agricultural Unmanned
Aerial Vehicle and
agricultural robots etc., for
more automation and
precision
480 images of
strawberry diseases,
2400 total images
Cloud service, mini
program development,
and deep learning
technology with the
help of a
self-supervision
mechanism
Other
[63] The same method can be
used for the classification
of other types of spectral
data
A total of 2700 images
of fruits and vegetables
of 13 classes, a total of
16 spectral bands
ranging from 470 to 630
nm
Added one or two layers
for compression of
hyperspectral
information down to 3
layers
Hyper
[54] The same minimum
redundancy and maximum
relevance can be used to
find which band is more
essential and effective in
monitoring after
man-made infection.
Training patches were
3000 validation 6000
testing 6000 (700 per
class overall)
Reduced unnecessary
bands from
hyperspectral sensor,
data abstraction due to
hierarchy in
convolutional neural
network and Fully
convolutional network.
Hyper
[91] To avoid false positive
results different
convolutional neural
networks can be trained
and combined for majority
voting
A total of 30 apples of
different grades were
used for RGB and
near-infrared cameras
and the same apples
were used for bruise
detection by doing some
damaging
Several convolutional
network models were
trained
Other
[64] A small dataset is a big
problem, with the help of a
large dataset the learning
process can be done
automatically instead of
manual selection. In the
future different ranges of
wavelength can be
considered for fruit
reflectance
3 trees from biologische
bundesanstalt,
bundessortenamt und
chemische industrie two
for training and one for
testing
Illumination condition
does not affect the
measurement and 3D
fruit local information
provided by the
proposed method
Other
(Continued)

CMC, 2024, vol.78, no.3 2989
Table 6 (continued)
Article Issues/Challenges Data set Worked on Image type
[65] The same method can be
used to detect defects in
cherries and heirloom
tomatoes. And also, can be
tested on other varieties of
tomatoes. The accuracy is
inversely proportional to
number of target classes
Total of 2000 images
which 1400 training and
600 testing
Calyx and stalk scar
detection technique
with an overall average
accuracy of 0.9515
Other
[55] The same method can be
used for other types of
fruits
Total of 147 images
using Canon 60D under
normal light conditions
Convolutional neural
network-based
classification algorithm
with the uncontrolled
environment and also
tested on the unseen
dataset
Hyper
[56] Other factors can be
considered while using the
same proposed method like
different orchards
comparison, seasons or
time of detection, and also
the limitation of
illumination Hyperspectral
camera is expensive so can
we do the same proposed
method with RGB or
multispectral camera
The data were collected
using three cultivars
B74 mango orchards
block A 10 rows of 494
treesblockB5rowsof
121 trees and block C 4
rows of 266 trees, A
total of 78 fruit were
selected
Using hyperspectral
sensing for the detection
of fruits and averaging
dry matter per tree
estimation
Other
[92] Transfer learning technique
can be applied for feature
extraction for different
domain phenotypes and
can be compared with the
proposed method result
80 RGB images and 400
spectral images of
different dates and
specific time slot
Feature extraction from
plant height and
vegetation index
calculation prediction
Other
[95] Vegetation indices
performance is dependent
on canopy volume and
crop yield
Data was collected on
four different dates, and
90 images were collected
for each flight
Unmanned aerial
vehicle mounted
cost-effective
multispectral camera
for coffee ripeness
Multi
(Continued)

2990 CMC, 2024, vol.78, no.3
Table 6 (continued)
Article Issues/Challenges Data set Worked on Image type
[66] Feature extraction can be
improved
15 citrus trees Low-altitude unmanned
aerial vehicle using a
multispectral camera
with five bands RGB,
Red Edge, and
near-infrared
Multi
[57] The proposed method can
be applied on other seeds
as well
500 Julong and 500
Xiali seeds were used
Uses of multispectral
imagery combined with
chemometrics
Both
[81] The generic algorithm can
be implemented using the
proposed method
414 samples from 24
different fields
performance
comparison of
vegetation indices
derived from both multi
and hyperspectral
imagery
Hyper
Some models display consistent errors across all features, emphasizing the necessity for improving
feature selection techniques. When conducting a comparative analysis of classification algorithms for
a particular problem, it becomes evident that the learning process tends to be sluggish. However,
this can be accelerated by isolating the feature selection process and applying the method to other
fruits. Enriching the dataset with additional disease samples and testing it on various deep-learning
architectures can help overcome the challenge of limited training samples.
5Challenges and Future Directions
Multispectral and hyperspectral images offer data spanning multiple bands or narrow spectral
channels, enabling the capture of extensive spectral information about the Earth’s surface or other
observed targets. However, the analysis and interpretation of such data can be daunting due to its high
dimensionality noise and artifacts. Machine learning and deep learning algorithms have demonstrated
promising outcomes in addressing these challenges. They efficiently tackle spectral data’s compu-
tational complexity and high dimensionality, facilitating automated analysis and interpretation. A
crucial aspect of hyperspectral image analysis is feature extraction and machine learning algorithms
excel in automatically learning and performing this task. This ability becomes particularly significant
due to the large number of spectral bands in hyperspectral data. Here are some challenges that we find
in our primary studies.
5.1 Small Datasets
The small size of datasets is indeed a standard limitation when working with spectral images in
the context of agricultural fruits [68,77,87]. Obtaining and annotating large-scale datasets specific to
agricultural fruits can be challenging due to various factors [73]. Here are some of the few reasons why
the datasets related to spectral images were often small.

CMC, 2024, vol.78, no.3 2991
5.1.1 Data Collection and Annotation
Acquiring high-quality spectral images of agriculture requires specialized equipment, such as
hyperspectral sensors or multispectral cameras. Conducting field data collection campaigns can be
time-consuming and resource-intensive [69]. Additionally, accurately label and annotate the collected
images with ground truth information, such as fruit type, ripeness, or disease status.
5.1.2 Variability and Diversity
Spectral images exhibit significant variability in size, shape, colour, and texture. Capturing this
variability across different cultivars, growing conditions, and stages of development requires a diverse
dataset [84,85]. However, collecting a representative sample of this variability is challenging, leading
to relatively small datasets that may not fully capture the entire agricultural population’s variation.
5.1.3 Privacy and Proprietary Concerns
Agricultural datasets are sometimes limited due to privacy concerns or proprietary reasons.
Agricultural companies or research institutions may hesitate to share their proprietary datasets due
to intellectual property protection or competitive advantages [62,64]. This can limit the availability of
large-scale datasets for public use. Cost constraints: Collecting and maintaining large-scale datasets
can be costly. The equipment, human resources, and infrastructure required for data collection,
storage, and annotation can impose financial constraints, particularly for research projects or organi-
zations with limited resources. We can employ techniques such as semi-supervised learning or transfer
learning. Semi-supervised learning leverages both labelled and unlabeled data for training. Transfer
learning involves pre-training a model on a related dataset with more data and then fine-tuning it on
the target dataset with limited labelled samples. We can also implement data augmentation techniques
to artificially increase the training dataset’s diversity. This can involve applying transformations such
as rotation, scaling, and brightness adjustment to account for variability.
5.2 Limited Environment and Camera Bandwidth
These factors can impact the quality and representativeness of the datasets. Here is a closer look
at these limitations.
5.2.1 Limited Environment of Data Collection
Spectral imaging in agricultural settings is often conducted in specific environments, such as
controlled greenhouse conditions or selected field sites [72]. This limited data collection environment
can affect the generalizability of the models trained on these datasets. Agricultural conditions, such as
lighting, soil types, weather patterns, and cultivation practices, can vary across different regions and
seasons, making it challenging to capture the full range of environmental factors that influence fruit
characteristics.
5.2.2 Camera Bandwidth Limitations
Spectral cameras for capturing multispectral or hyperspectral images have specific bandwidth
ranges for each spectral band. These bandwidth limitations can affect the spectral resolution and
accuracy of the captured images [78]. Narrow bandwidths may limit the ability to capture subtle
spectral variations and nuances related to fruit properties, such as ripeness, disease, or nutritional
content. This can lead to incomplete or less precise spectral information, which may impact the
performance of analysis and classification algorithms. Researchers often employ strategies such as

2992 CMC, 2024, vol.78, no.3
data augmentation to mitigate these limitations. By applying transformations or modifications to the
available dataset, data augmentation techniques can help increase the diversity and size of the training
data. This can help to some extent in compensating for the limited environment of data collection by
introducing variations that mimic different environmental conditions or scenarios.
Cross-validation and external validation can be employed to assess the generalizability of models
trained on limited datasets. This involves partitioning the dataset into subsets for training and
validation, ensuring that the models are evaluated on data they have not seen during training.
Additionally, external validation on independent datasets collected in different environments or
by different research groups can provide a better understanding of the model’s performance and
robustness.
Collaborative data collection among researchers, institutions, and stakeholders in the agricultural
domain can help overcome the limitations of limited data collection environments. By pooling
resources, sharing expertise, and conducting multi-site data collection campaigns, more extensive and
diverse datasets can be obtained, capturing a wider range of environmental conditions and fruit
characteristics. Camera and sensor advancements in spectral imaging technology, including camera
bandwidth and resolution improvements, can help mitigate the limitations imposed by limited camera
capabilities. Higher spectral resolution and broader bandwidths can provide more detailed and
accurate spectral information, enabling better discrimination of fruit properties and improving the
overall performance of analysis [56].
To overcome these, we can consider fusing spectral data with higher-resolution spatial data, such
as RGB or multispectral imagery, to combine the spectral richness of the data with the spatial detail
of higher-resolution imagery. Alternatively, explore super-resolution techniques to enhance spatial
resolution.
5.3 Data Scarcity
Data scarcity can pose significant challenges, particularly in machine learning. These are some
major aspects and issues that arise while facing data scarcity.
5.3.1 Limited Model Training
Data scarcity can make it challenging to train robust models [74], leading to overfitting (where the
model fits the training data too closely [87] and performs poorly on new data) or underfitting (where
the model cannot capture the underlying patterns in the data).
5.3.2 Reduced Model Performance
With limited data, it is often challenging to achieve high model performance. Models trained on
small datasets may not capture the complexities of the underlying problem, resulting in suboptimal
results [55,95].
5.3.3 Privacy and Security
Data scarcity can arise due to privacy concerns and regulations in sensitive domains, such as
healthcare or finance. Access to sufficient data for research or analysis may be restricted to protect
individuals’ privacy.

CMC, 2024, vol.78, no.3 2993
5.3.4 Transfer Learning Challenges
Machine learning techniques like transfer learning and domain adaptation are often used to
leverage knowledge from related domains. Data scarcity can limit the effectiveness of these techniques,
as they rely on having at least some data available in both the source and target domains [73,92].
Different strategies can be implemented to avoid data scarcity issues.
Data Augmentation: We can implement different data augmentation techniques to expand the
dataset through translation and rotation by adding some noise to the existing data.
Transfer Learning: Adapt models on larger, related datasets to the target task with limited data.
Active Learning: Choose those queries and label the most informative data.
Semi-Supervised Learning: Combine labelled data with unlabeled data to make the most available
information.
Domain Adaptation: Adapt models to perform well in the target domain by mitigating domain
shift issues.
Data Synthesis: Use generative models to create synthetic data resembling the target domain.
Ultimately, addressing data scarcity requires careful consideration of the specific domain and
problem at hand and creative solutions to make the most of the available data and resources.
5.4 Machine Learning and Deep Learning: Promises and Limitations
KNN was used for the classification of the rose plant in [59] and citrus greening detection in [69]
and in [70] for the regression assessment of ready-to-eat pineapple quality. The classification accuracy
was 65% and 81%, and the R-square value was 0.42, respectively. The challenge is to test different
algorithms on the same type of problems at different stages of growth of the particular plant.
Citrus greening detection [66–69], pineapple quality [70], strawberry ripeness [77], tomato grading
[65], and high-quality watermelon seeds classification were done by SVM with accuracy ranging from
79% to 98%. Testing new algorithms, refining feature selection, and more diverse sample images at
various ripeness stages can enhance results. It is noted that accuracy may decrease as the number
of target classes increases. Nevertheless, SVM holds potential for broader applications, including
analyzing different seeds and crops, making it a promising avenue for future research and development.
Deep Learning and different types of neural networks were used for the detection of fruit diseases
[83], vine disease detection [68], detection of green vegetation cover [84], detection of growth stages
of rice [72], fruit recognition [73], apple defects [74], blossom detection in apple trees [75], tomato’s
external defects [76] and strawberry disease classification [94]. The accuracy achieved was in the range
of 65% to 99%. One of the main issues was the slow learning process, which can be enhanced by
choosing the meta-heuristic techniques for feature selection. The other main challenge is adaptability.
We can test the same model on the new dataset. We can enhance the adaptability by updating the
dataset with new models on different agriculture problems.
6Conclusion
This paper investigates a wide range of applications based on spectral images. It focuses on relevant
studies from reputable databases, which were carefully chosen to address the four main research
questions of the study. The study examined various applications utilizing spectral images, particularly
agriculture. The findings reveal that Deep Learning is the most prevalent technique for spectral image

2994 CMC, 2024, vol.78, no.3
classification, followed by SVM as the second optimal approach. Furthermore, the study identified
popular datasets relevant to agricultural fruits, with Indian pines, Salinas, and Houston being the most
widely used.
Additionally, the Pavia University and Kennedy Centre datasets were frequently employed
in certain research. The study also acknowledged the limitations of the restricted data collection
environment and camera bandwidth. Despite the constraints of small datasets, researchers have made
notable strides in developing effective machine learning and deep learning models for agricultural
fruit analysis. As the availability and quality of spectral image datasets increase, the potential for
more accurate and robust analysis and applications in the agricultural sector also improves. Indeed,
the limited environment of data collection and camera bandwidth represents additional challenges
when working with spectral images in agricultural fruit analysis. Nevertheless, ongoing research and
technological advancements in spectral imaging and agricultural fruit analysis aim to address these
issues and enhance the dataset’s quality, diversity, and representativeness for machine learning and
deep learning applications.
Acknowledgement: We express our gratitude to the upper management of the Karachi Institute of
Economics and Technology (KIET) for furnishing the technical resources necessary for the execution
of this research. Additionally, we extend our appreciation to Multimedia University for their generous
financial backing of this research.
Funding Statement: The authors received no specific funding for this study.
Author Contributions: The authors confirm their contribution to the paper as follows: study conception
and design: Usman Khan, Muhammad Khalid Khan; data collection: Muhammad Ayub Latif;
analysis and interpretation of results: Usman Khan, Muhammad Khalid Khan; draft manuscript
preparation: Usman Khan, Muhammad Khalid Khan, Muhammad Ayub Latif, Muhammad Naveed,
Salman A. Khan, Muhammad Mansoor Alam, Mazliham Mohd Su’ud. All authors reviewed the
results and approved the final version of the manuscript.
Availability of Data and Materials: Data available within the article. The authors confirm that the data
supporting the findings of this study are available within the article.
Conflicts of Interest: The authors declare that they have no conf licts of interest to report regarding the
present study.
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