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The alternaria alternate disease image (i) corrupted using impulse noise density of 50% (ii) and image reconstruction from impulse noise (iii)
Source publication
Automatic segmentation of plant image’s leaf diseases has recently become a popular area of study worldwide. The suggested approach automatically segments various areas of leaf disease from images of the plant, which can then be combined with machine learning or deep learning techniques to improve system accuracy. Our suggested method consists of t...
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Though many researchers have studied plant leaf disease, the timely diagnosis of diseases in olive leaves still presents an indisputable challenge. Infected leaves may display different symptoms from one plant to another, or even within the same plant. For this reason, many researchers studied the effects of those diseases on, at most, two plants....
Citations
... The development of computer vision and machine learning technology provides a new solution for real-time automatic detection of crop diseases (Fuentes et al., 2018(Fuentes et al., , 2019. Traditional machine learning methods in crop diseases identification and positioning have made some valuable experience, such as image segmentation [such as K-means clustering (Trivedi et al., 2022) and threshold method (Singh and Misra, 2017)], feature detection [such as SURF (Hameed and Üstündag, 2020), KAZE (Rathor, 2021), and MSER blob (Lee et al., 2023)], and pattern recognition [such as KNN (Balakrishna and Rao, 2019), SVM, and bp neural network (Hatuwal et al., 2021;Kaur and Singh, 2021)]. Due to the complexity of image preprocessing and feature extraction, these methods are still ineffective in detection. ...
Introduction
Grapes are prone to various diseases throughout their growth cycle, and the failure to promptly control these diseases can result in reduced production and even complete crop failure. Therefore, effective disease control is essential for maximizing grape yield. Accurate disease identification plays a crucial role in this process. In this paper, we proposed a real-time and lightweight detection model called Fusion Transformer YOLO for 4 grape diseases detection. The primary source of the dataset comprises RGB images acquired from plantations situated in North China.
Methods
Firstly, we introduce a lightweight high-performance VoVNet, which utilizes ghost convolutions and learnable downsampling layer. This backbone is further improved by integrating effective squeeze and excitation blocks and residual connections to the OSA module. These enhancements contribute to improved detection accuracy while maintaining a lightweight network. Secondly, an improved dual-flow PAN+FPN structure with Real-time Transformer is adopted in the neck component, by incorporating 2D position embedding and a single-scale Transformer Encoder into the last feature map. This modification enables real-time performance and improved accuracy in detecting small targets. Finally, we adopt the Decoupled Head based on the improved Task Aligned Predictor in the head component, which balances accuracy and speed.
Results
Experimental results demonstrate that FTR-YOLO achieves the high performance across various evaluation metrics, with a mean Average Precision (mAP) of 90.67%, a Frames Per Second (FPS) of 44, and a parameter size of 24.5M.
Conclusion
The FTR-YOLO presented in this paper provides a real-time and lightweight solution for the detection of grape diseases. This model effectively assists farmers in detecting grape diseases.
... It is difficult to differentiate between disease types due to their similar shape, texture, and color. Recently, many studies have been published showing that both traditional and deep learning methods successfully classify different leaf disease types, [24], [25], [26]. This section presents the results of deep-learning models for the classification of guava leaf diseases. ...
A higher percentage of crops are affected by diseases, posing a challenge to agricultural production. It is possible to increase productivity by detecting and forecasting diseases early. Guava is a fruit grown in tropical and subtropical countries such as Chad, Pakistan, India, and South American nations. Guava trees can suffer from a variety of ailments, including Canker, Dot, Mummification, and Rust. A diagnosis based only on visual observation is unreliable and time-consuming. To help farmers identify plant diseases in their early stages, an automated diagnosis and prediction system is necessary. Therefore, we developed a deep learning method for classifying and forecasting guava leaf diseases. We investigated a dataset composed of 1834 leaf examples, separated into five categories. We trained the dataset using four different and generally preferred pre-trained CNN architectures. The EfficinetNet-B3 architecture outperformed the other three architectures, achieving 94.93% accuracy on the test data. The results ensure that deep learning methods are more successful and reliable than traditional methods.
... K-means is an effective clustering tool still popular [62]. It uses the centroid-based algorithm to find the representative clusters for each group. ...
Despite the importance of product repairability, current methods for assessing and grading repairability are limited, which hampers the efforts of designers, remanufacturers, original equipment manufacturers (OEMs), and repair shops. To improve the efficiency of assessing product repairability, this study introduces two artificial intelligence (AI) based approaches. The first approach is a supervised learning framework that utilizes object detection on product teardown images to measure repairability. Transfer learning is employed with machine learning architectures such as ConvNeXt, GoogLeNet, ResNet50, and VGG16 to evaluate repairability scores. The second approach is an unsupervised learning framework that combines feature extraction and cluster learning to identify product design features and group devices with similar designs. It utilizes an Oriented FAST and Rotated BRIEF feature extractor (ORB) along with k-means clustering to extract features from teardown images and categorize products with similar designs. To demonstrate the application of these assessment approaches, smartphones are used as a case study. The results highlight the potential of artificial intelligence in developing an automated system for assessing and rating product repairability.
... The cluster segmentation is one of the specific theoretical approaches to image, typically, the K-means clustering algorithm 13 and the fuzzy C-mean clustering algorithm 14 . Trivedi 15 used a K-means clustering segmentation algorithm to segment plant leaves into homogeneous segments which significantly improved the accuracy of plant leaf pest detection. Wu 16 used the Canny algorithm to process text image edge detection and then used the k-means algorithm for clustering pixel recognition, which effectively improved the accuracy of text image recognition. ...
... As shown in Table 1, the OSNC method has fewer iterations and shorter searching time than the other methods (K-means 15 , FCM 21 , PSO-FCM 20 ). Experiments show that this method reduces the search time of the optimal segmentation number to a certain extent. ...
For solving the problem of quality detection in the production and processing of stuffed food, this paper suggests a small neighborhood clustering algorithm to segment the frozen dumpling image on the conveyor belt, which can effectively improve the qualified rate of food quality. This method builds feature vectors by obtaining the image's attribute parameters. The image is segmented by a distance function between categories using a small neighborhood clustering algorithm based on sample feature vectors to calculate the cluster centers. Moreover, this paper gives the selection of optimal segmentation points and sampling rate, calculates the optimal sampling rate, suggests a search method for optimal sampling rate, as well as a validity judgment function for segmentation. Optimized small neighborhood clustering (OSNC) algorithm uses the fast frozen dumpling image as a sample for continuous image target segmentation experiments. The experimental results show the accuracy of defect detection of OSNC algorithm is 95.9%. Compared with other existing segmentation algorithms, OSNC algorithm has stronger anti-interference ability, faster segmentation speed as well as more efficiently saves key information ability. It can effectively improve some disadvantages of other segmentation algorithms.
... In K-mean segmentation, multiple segmentations on a given image can be performed and bring it for a classifier and try to find the boundaries that describe the location of an object [16,17]. The K-means approach is composed of two distinct steps. ...
Agriculture is one of the primary pillars powering India's economy. It is alarming to note that India's agriculture rate is declining steeply. Climate change, environmental pollution, and soil erosion are well-known factors affecting crop productivity. The increasing prevalence of plant diseases is also a significant factor affecting agriculture. Early disease detection and mitigation actions based on identified conditions in the plants are critical in increasing crop productivity. This study considers a machine learning model for detecting disease in cashew leaves. This work concentrates on Anthracnose disease, which leads to severe yield loss when it affects the cashew plant. In this regard, cashew leaves are collected and used to train various machine learning classifiers to identify and classify the disease. This work focuses on the segmentation and classification of leaves using multiple Machine Learning models. Basic segmentation approaches like Global Threshold, Adaptive Gaussian, Adaptive Mean, Otsu, Canny, Sobel, and K-Means, and Machine Learning models like Random Forest, Decision Tree, KNN, Logistic Regression, Gaussian Naive Bayes Classifiers are employed. The final classification employs a Hard and Soft voting classifier and the Decision Tree, KNN, Logistic Regression, and Gaussian Naive Bayes classifiers. Finally, we observe that K-Means segmentation with Random Forest outperforms other classifiers. The accuracy obtained from the Random Forest classifier is 96.7% for the CCDDB dataset, and the accuracy obtained from the Random Forest classifier is 99.7% for the PDDB dataset.
... We quantified the randomness associated with segmented cortical by computing the relative entropy variations in the associated pixels. In this scheme (Fig. 7), an input image is decomposed into K segments using the K-mean clustering algorithm [33,50], and the enclosed entropy level is evaluated for each segment. To select the optimal count of the cluster, the ELBOW method [33] is applied, and based on the minimum within-cluster sum of square (WCSS) score, K = 4 is selected as the optimal value. ...
Wilson’s Disease (WD) is a rare, autosomal recessive disorder caused by excessive accumulation of Copper (Cu) in various human organs such as the liver, brain, and eyes. Accurate WD diagnosis is challenging because of: (1) subtle intensity variations in infected tissues, and (2) Biased training results in case of a small and imbalanced dataset. This study provides a novel WD classification model for a small MRI dataset (3072 scans). The proposed study explores multi-dimensional Gabor kernels in five scales and eight orientations to produce pixel-specific features and process them in the 4th-order tensor format. The tucker decomposition technique is applied to obtain approximate factors from the Gabor tensors set. Five-fold cross-validation results show that the proposed classification model achieves 99.91% classification accuracy which is better than four well-known feature extraction techniques: (1) 2D-Discrete Wavelet Transform, (2) Intensity histograms, (3) Histogram of oriented gradients, and (4) Grey level co-occurrence matrix. Also, our method improves the classification accuracy by an average of 33% and Area Under the Curve (AUC) by 25% over the above-mentioned feature extraction techniques. In the latter category, the performance of the proposed method is compared with three deep learning models: (1) Customized Convolution Neural Network (CCNN), (2) AlexNet, and (3) VGGNet. In addition, it enhances classification accuracy by 10%, 3.5%, and 3%, compared to CCNN, AlexNet, and VGGNet, respectively. Also, our proposed approach is computationally fast compared to discussed feature extraction techniques.
... In K-Mean segmentation, multiple segmentations on a given image can be performed and bring it for a classi er and try to nd the boundaries that describe the location of an object [21,23]. ...
Agriculture is one of the primary pillars powering India's economy. It is alarming to note that India's agriculture rate is declining steeply. Climate change, environmental pollution, and soil erosion are well-known factors affecting crop productivity. The increasing prevalence of plant diseases is also a significant contributing factor affecting agriculture. Early disease detection and mitigation actions based on identified diseases in the plants are critical in increasing crop productivity. This study considers a machine-learning model for detecting disease in cashew leaves. This work concentrates on Anthracnose disease, which leads to severe yield loss when it affects the cashew plant. In this regard, cashew leaves are collected and used to train various machine learning classifiers to identify and classify the disease. This work focuses on the segmentation and classification of leaves using various Machine Learning models. For this, Basic segmentation approaches like Global threshold, Adaptive Gaussian, Adaptive Mean, Otsu, Canny, Sobel, and K-Means, and Machine Learning models like Random Forest, Decision Tree, KNN, Logistic Regression, Gaussian Naive Bayes Classifiers are employed. The final classification employs a Hard and Soft voting classifier in addition to the Decision Tree, KNN, Logistic Regression, and Gaussian Naive Bayes classifiers. Finally, we observe that K-Means segmentation with Random Forest outperforms other classifiers. The accuracy obtained from the Random Forest classifier is 96.7% for the CCDDB dataset, and the accuracy obtained from the Random Forest classifier is 99.7% for the PDDB dataset.
... The watershed algorithm is also used for segmentation with K-means, as in [18]. Many reviews and adaptive techniques are also studied in [19] [20] [21] [22]. K means with genetic algorithms are used together in some researches in the field of predictive mapping of mineral prospectively [23] This research will shed light on the details of optimization Genetic algorithm and how to use it to optimize the selection of a specific segmentation algorithm parameter. ...
... Moreover, the algorithm was found to be robust to venations, leaf size, and so forth. Trivedi et al. (2022) used a min-max hue histogram and k-mean clustering-based technique to segment plant leaf disease automatically. First, a rank order fuzzy (ROF) filter is used to remove background noise from the images. ...
This study uses digital image processing and machine learning to quantify the infection patterns on tomato leaves due to blight diseases. Quantification, also known as severity measurement, is a technique to determine how much a leaf is diseased by calculating a numeric value. This value could be a fraction representing how much the diseased region is present on the leaf compared to the entire leaf region, or it can be a percentage value too. There are two main approaches to measuring disease severity; the first technique involves visual estimation using references like standard area diagrams. The second approach involves taking a digital image of the leaf, separating the diseased regions from the healthy regions, and then calculating the area of those two regions. The approach we took is similar. We first took the digital image and segmented the diseased and healthy regions. For quantification, we calculated the ratio of total pixels representing the diseased region to the total number of pixels representing the leaf. While finding ways to improve the accuracy of the segmentation algorithm, we also discovered our segmentation technique which automatically segments the diseased regions of the leaves from the healthy areas using k‐means clustering. The clustering‐segmentation algorithm did give good results for the sample images to which it was applied. The main thing about the clustering‐segmentation algorithm is that it tends to be automatic compared to some of the semi‐automatic segmentation approaches that have been discovered till now. We could reproduce the validated quantification results as other authors achieved in the recent work, which also validated our methodology.
... It can be observed from the outcome of three contrast enhancement procedures viz. contrast stretching min-max [33], CLAHE (Contrast Limiting histogram Equalization) [34], and Histogram Equalization applied [35] to both control (see. Figure 2(a)) and drought (see. ...
