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One of the important challenges of agricultural sector is comprehensive management of pests and diseases. That needs precision and repeated observations of plant. Because of hydrometric condition and temperature of greenhouse, it is necessary the fast decision making to control the pests and diseases for purpose of avoiding of permanent and catchin...
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... The applications of common methods for diagnosing some of these internal diseases is destructive, difficult, or even impossible, because the diseases do not have any visible symptoms. Early detection of defects and diseases, in order to separate the products before storage leads to the prevention of disease transmission and increased marketability [6]. The study area of the present paper, in the West of Iran, is one of the most susceptible areas for potato production due to its temperate climate. ...
Potatoes are one of the most demanded products due to their richness in nutrients. However, the lack of attention to external and, especially, internal defects greatly reduces its marketability and makes it prone to a variety of diseases. The present study aims to identify healthy-looking potatoes but with internal defects. A visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectrometer was used to capture spectral data from the samples. Using a hybrid of artificial neural networks (ANN) and the cultural algorithm (CA), the wavelengths of 861, 883, and 998 nm in Vis/NIR region, and 1539, 1858, and 1896 nm in the SWIR region were selected as optimal. Then, the samples were classified into either healthy or defective class using an ensemble method consisting of four classifiers, namely hybrid ANN and imperialist competitive algorithm (ANN-ICA), hybrid ANN and harmony search algorithm (ANN-HS), linear discriminant analysis (LDA), and k-nearest neighbors (KNN), combined with the majority voting (MV) rule. The performance of the classifier was assessed using only the selected wavelengths and using all the spectral data. The total correct classification rates using all the spectral data were 96.3% and 86.1% in SWIR and Vis/NIR ranges, respectively, and using the optimal wavelengths 94.1% and 83.4% in SWIR and Vis/NIR, respectively. The statistical tests revealed that there are no significant differences between these datasets. Interestingly, the best results were obtained using only LDA, achieving 97.7% accuracy for the selected wavelengths in the SWIR spectral range.
... The spectroscopy technique does not provide spatial information. On the other hand, computer vision is incapable of inspecting samples of the same color and predicting their chemical components [5]. Thus, by integrating the main advantages of spectroscopy and imaging, the hyperspectral imaging technique can simultaneously obtain spectral and spatial information, which is crucial for predicting the quality of agricultural and food products [6]. ...
Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network–imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network–harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the K-nearest-neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network–biogeography-based optimization (ANN-BBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a t-test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen.
... Plants 2020, 9, 1718 2 of 18 such as Japan and the European Union have set strict standards for the quality and health of imported food products. Therefore, in order to gain more share in the global export market, steps should be taken in order to adopt new developments in post-harvest technology [2,3]. ...
Non-destructive estimation of the internal properties of fruits and vegetables is very important, because better management can be provided for subsequent operations. Researchers and scientists around the world are focusing on non-destructive methods because if they are developed and commercialized, there will be an impressive change in the food industry. In this regard, this paper aims to present a non-destructive method based on Vis-NIR spectral data. The different stages of the proposed algorithm are: (1) Collection of samples of Gala apples, (2) Spectral data extraction by spectroscopy, (3) Pre-processing of spectral data, (4) Measurement of chemical properties of titratable acidity (TA) and taste index, (5) Selection of key wavelengths using hybrid artificial neural network-firefly algorithm (ANN-FA), (6) Non-destructive estimation of the properties using two methods of hybrid ANN- Particle swarm optimization algorithm and partial least squares regression. For considering the reliability of methods for estimating the chemical properties, the prediction operation was executed in 300 iterations. The results represented that the mean and standard deviation of the correlation coefficient and the root mean square error of hybrid ANN-PSO and PLSR for TA were 0.9095 ± 0.0175, 0.0598 ± 0.0064, 0.834 ± 0.0313 and 0.0761 ± 0.0061 respectively. These values for taste index were 0.918 ± 0.02, 3.2 ± 0.39, 0.836 ± 0.033 and 4.09 ± 0.403, respectively. Therefore, it can be concluded that the hybrid ANN-PSO has a better performance for non-destructive prediction of the two mentioned chemical properties than the PLSR method. In general, the proposed method can predict the chemical properties of TA and taste index non-destructively, which is very useful for mechanized harvesting and management of post-harvest operation.
... In recent years, researchers have tendeed to use online and non-destructive methods in the food industry (Pourdarbani et al. [7]; Mesa et al. [8]; Sirisomboon et al. [9]; Arendse et al. [10]). Some non-destructive methods include infrared spectroscopy (Magwaza and Opara [11]; Marcone [12]; Huang et al. [6]; Pourdarbani and Rezaei [13]); x-ray (Brecht et al. [14]); nuclear magnetic resonance imaging (Zhou et al. [15]); and Visible-Near Infrared (Vis-NIR) spectroscopy (Cavaco et al. [16]; Jamshidi [17]). Some researchers have studied the changes in the physical and chemical properties of different fruits during ripening (Arendse et al. [18]; Cavaco et al. [16]; Rungpichayapichet et al. [19]; Santagapita et al. [20]). ...
Non-destructive assessment of the physicochemical properties of food products, especially fruits, makes it possible to examine the internal quality without any damage. This is applicable at different stages of fruit growth, harvesting stage, and storage as well as at the market stage. In this regard, the present study aimed to estimate the total chlorophyll content using three types of data: color data, spectral data, and spectral data related to the most effective wavelengths. The most important steps of the proposed algorithms include extracting spectral and color data from each sample of Fuji cultivar apple, selecting the most effective wavelengths at the range of 660–720 nm using hybrid artificial neural network–particle swarm optimization (ANN-PSO), non-destructive assessment of the chemical property of total chlorophyll content based on color data, and spectral data using hybrid artificial neural network-Imperialist competitive algorithm (ANN-ICA). In order to assess the reliability of the hybrid ANN-ICA, 1000 iterations were performed after selecting the optimal structure of the artificial neural network. According to the results, in the best training mode and using spectral data and the most effective wavelength, total chlorophyll content was predicted with the R2 and RMSE of 0.991 and 0.0035, 0.997 and 0.001, 0.997 and 0.0006, respectively,
... The quality of a fruit is determined by both its external and internal properties. Shape, size, color, and overall appear-ance are the main apparent qualitative parameters [1,2] while the amount of soluble solids (SSC), titratable acidity (TA), soluble solids to titratable acidity ratio (SSC/TA), pH, starch, carotenoids, sugars, ascorbic acid, and firmness, are indicators of internal quality properties [3][4][5] and are among the non-destructive quality parameters that are widely used to evaluate fruit quality. Visible range (Vis)/near-infrared (NIR) and NIR spectroscopy are among the most popular methods. ...
measurement of physicochemical properties of fruits during maturation stages can help having proper fruit management. Spectroscopy data analyzing and processing is among the commonly used methods that enable non-destructive accurate property estimation. Non-destructive linear (partial least squares regression, PSLR) and non-linear (artificial neural network, ANN) regression estimation of different physicochemical properties including firmness, acidity (pH) and starch content of 160 Fuji (Malus pumila) apple fruit samples at various maturity stages using visible and short wave near infrared (VSWIR) spectroscopic data in wavelength range 400–1000 nm is investigated with the following steps: (1) harvesting 160 Fuji apple samples at four different maturation levels; (2) extracting spectral data in wavelength range of 400–1000 nm; extracting physicochemical properties of tissue firmness, acidity (pH) and starch content; (3) pre-processing the reflectance spectra from each sample; (4) selecting effective wavelength values for each chemical property; and (5) non-destructive estimation of tissue firmness, acidity (pH) and starch content using spectral data range 400–1000 nm and spectral data based on effective wavelengths, by means of an ensemble average artificial neural network method. Results show that the neural ensemble reached similar results when using VSWIR spectral data content (wavelength range) and fixed effective selected NIR wavelengths. Correlation coefficients estimating tissue firmness, acidity (pH), and starch content were 0.800, 0.919, and 0.940 for VSWIR spectral data (linear PLS regression), 0.826, 0.947, and 0.969 for VSWIR spectral data (non-linear ANN), 0.827, 0.946, and 0.969 for fixed NIR effective wavelengths (non-linear ANN). Mean ± std. Regression coefficients for tissue firmness, acidity (pH), and starch content were 0.717 ± 0.113, 0.786 ± 0.131, and 0.941 ± 0.013 for Vis/NIR spectral data (linear PLS regression), 0.849 ± 0.017, 0.930 ± 0.017, and 0.967 ± 0.007 for Vis/NIR spectral data (non-linear ANN), 0.852 ± 0.016, 0.929 ± 0.015, and 0.966 ± 0.006 for fixed effective NIR wavelengths (non-linear ANN).
Managing the production of greenhouse products requires knowledge of controlling many environmental factors and plant nutrition and fight against pests and plant diseases. Recognition pests and fight against them is one of the most important activities in the process of production of greenhouse products. Pre-knowledge of the demographic density of insect pests is one of the effective methods of pesticides and reduce their levels of use, especially for insect pest control toxins. Wireless Sensor Networking Technologies (WSN) is one of the new technologies used to sense the environment and collect and transmit information to the user or the central station to view and respond appropriately to an occurrence or phenomenon. In this study, the use of WSN in monitoring, timely diagnosis of greenhouse white flies, design and mapping of greenhouse contamination was investigated. For this goal, 3750 images of 15 sticky traps with white flies that attached to Melon greenhouse in Isfahan Agricultural Jihad Research Center were provide and transmitted online using a WSN to a computer located at a distance of 900 meters from the greenhouse. The color images of the sticky traps are acquired by using 15 digital cameras were converted to gray colored images using MATLAB software, then after image classification with Support Vector Machine (SVM) classifier based on their features, are divided into two categories of images: whiteflies affected image and whiteflies unaffected image. After identification of the white flies, number of pests was counted and infection maps of Greenhouse with ArcMap10.2 software was drawn up. Assessment of the system showed that accuracy of SVM algorithm for categorizing images of sticky traps was 97.73%, and the average values of statistic parameters of the Confusion matrix for 15 traps including sensitivity, accuracy, specificity and classification accuracy were 98.46%, 83.31%, 99.08% and 97.72% respectively. The overall accuracy of the system for detection and counting Greenhouse whitefly pests is 97.71%. The average root mean square error (RMSE) in estimating of the number of white flight by image processing and direct counting was between 1 and 5.03. Therefore, the system is suitable for detecting and tracing and counting the number of trapped white flies, and it is possible to design appropriate greenhouse poisoning plans to fight this pest.
Early pest detection and identification in agriculture is necessary for good quality and quantity of crop production. This will help to reduce the effect of pest and the use of pesticides. Detection of pest is necessary to get the information and location of the pest and this can be achieved only if temperature is controlled in the room like in the greenhouse. There are various conventional techniques which are used to detect the pest, but the proposed method is very fast, easy and convenient. This project proposes pest detection and movement by using the video processing. Video processing involves capturing the video and processing on it to find the presence and location of pest.
