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Leaf image samples of the raw dataset, consisting of 10 leaf diseases from eight crop species. (0) Apple healthy, (1) apple_scab, (2) corn gray leaf spot, (3) corn northern leaf blight, (4) cherry healthy, (5) cherry powdery mildew, (6) grape healthy, (7) grape black rot, (8) Esca, (9) grape leaf blight, (10) peach healthy, (11) bell pepper bacterial spot, (12) strawberry leaf scorch, (13) strawberry healthy, (14) blueberry healthy, (15) corn common rust, (16) raspberry healthy, and (17) corn healthy.
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... raw leaf dataset used in this work comes from PlantVillage 17 dataset collected under controlled conditions, which contains 54,306 images of diseased and healthy plant leaves. In this work, 18,347 leaf images selected from the PlantVillage are used as experimental data, including 10 leaf diseases from 8 crop species (Fig. ...
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... improved ResNet50 model was trained using the Adam optimization algorithm and compared with the original ResNet50 model. For the original ResNet50, the test results of each category and the confusion matrix are illustrated in Table 4 and Fig. 10, respectively. For the improved ResNet50, the test results of each category and the confusion matrix are illustrated in Table 4 and Fig. 11, respectively. From Figs. 10 and 11, it can be seen that incorrect predictions always occurred in neighboring classes, for example, in Fig. 10 (colored in orange), 17 images belonging to class 13 were predicted ...
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... using the Adam optimization algorithm and compared with the original ResNet50 model. For the original ResNet50, the test results of each category and the confusion matrix are illustrated in Table 4 and Fig. 10, respectively. For the improved ResNet50, the test results of each category and the confusion matrix are illustrated in Table 4 and Fig. 11, respectively. From Figs. 10 and 11, it can be seen that incorrect predictions always occurred in neighboring classes, for example, in Fig. 10 (colored in orange), 17 images belonging to class 13 were predicted to be labeled as belonging to class 14, and 11 of the ones belonging to class 14 were wrongly labeled as belonging to class 13. This is mainly because the degrees ...
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... category and the confusion matrix are illustrated in Table 4 and Fig. 10, respectively. For the improved ResNet50, the test results of each category and the confusion matrix are illustrated in Table 4 and Fig. 11, respectively. From Figs. 10 and 11, it can be seen that incorrect predictions always occurred in neighboring classes, for example, in Fig. 10 (colored in orange), 17 images belonging to class 13 were predicted to be labeled as belonging to class 14, and 11 of the ones belonging to class 14 were wrongly labeled as belonging to class 13. This is mainly because the degrees for one plant disease are different but similar, for example, grape black measles (Esca) general (class 13) and Esca serious ...
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... 13) and Esca serious (class 14). Another reason is that two similar diseases occurred in some plants, for example, grape black rot general (class 11) and Esca general (class 13). Compared with the original ResNet50 model, the improved model decreased wrong predictions. For example, one image in class 6 was predicted as class 23 (colored in red in Fig. 10), but this was corrected by the improved model (red in Fig. ...
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... diseases occurred in some plants, for example, grape black rot general (class 11) and Esca general (class 13). Compared with the original ResNet50 model, the improved model decreased wrong predictions. For example, one image in class 6 was predicted as class 23 (colored in red in Fig. 10), but this was corrected by the improved model (red in Fig. ...
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... the same parameter settings as in Fig. 12, the improved ResNet50 model was superior to the original ResNet50 model, both for classification accuracy and for test loss. This showed that the improved model has good generalization ...
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... understand better the process for training our model, which involved model optimization and model adjustment, visualization of the outputs of each layer in the deep learning network was investigated. As can be seen from Fig. 13, the first convolutional layer analyzes the processed information from the original picture and mainly extracts the edges of the leaf. After the first layer, much of the information from the original picture remains, which will continue to be extracted by subsequent convolutional layers. Higher levels of the convolutional network ...
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... the edges of the leaf. After the first layer, much of the information from the original picture remains, which will continue to be extracted by subsequent convolutional layers. Higher levels of the convolutional network extract more abstract information about the leaf, which is more difficult to understand using human intuition. The pictures in Fig. 13 showed that, the higher a level of the network is, the more the rules of extraction represented by the pictures are, result in more abstract images. The abstract features may represent the common characteristics of the pictures and thus allow the network to accumulate more of the information common to all the pictures. Crop leaf ...
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... Likewise, a remarkable system for non-destructive and visual disease detection was developed for 13 plant species and was named as "PlantDoc" . Equally, the use of ResNet-50 with an accuracy of 95.61% in plant disease and pest detection has also been reported (Fang et al., 2020). Numerous techniques exist based on AI techniques for plant disease and pest identification (Pandey et al., 2022;Zheng et al., 2022;Singh et al., 2023) (Nagasubramanian et al., 2019;Thenmozhi and Reddy, 2019;Xu et al., 2022). ...
Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030—Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.
... As described in the publication [7] the following is an explanation of each of the technique four fundamental steps, which are used to identify diseases: After determining a color transformation structure for the RGB image that is being input, the green pixels in the image are masked and deleted by utilizing a certain threshold value. This is done after the initial step of determining the color transformation structure. ...
... With powdery mildew and stripe rust of wheat as the research object, Wenxia Bao, Jian Zhao, Gensheng Hu, [50] et al. proposed an algorithm to identify wheat leaf diseases and their severity through elliptic maximum edge criterion (E-MMC) metric learning using a combination of elliptic metric and maximum spacing criterion to indicate the nonlinear transformation of a spatial structure or semantic information of wheat leaf disease images. Tao Fang, Peng Chen, Jun Zhang, et al. [51] determined the ratio of the number of pixels in the diseased area to the number of pixels in the diseased leaf area. They incorporated the ratio as the classification threshold of disease classes into the convolutional neural network Resnet-50 to classify ten diseases of eight plants. ...
Extensive research suggested that the core of how to use pesticides scientifically is the careful and accurate determination of the severity of crop diseases. The existing grading standards of plant leaf diseases have been excessively singular. Thus, the diseases roughly fall into general and severe grades. To address the above problems, this study considered the effect of the distribution of disease spots, and two evaluation indicators (termed the imbalance degree and main vein distance) were newly added to optimize the grading criteria of apple leaf diseases. Combined with other factors, the grade evaluation indicator was determined through PCA principal component analysis. A gradual multivariate logistic regression algorithm was proposed to evaluate apple leaf disease grade and an optimized apple leaf disease grade evaluation model was built through PCA-logistic regression analysis. In addition, three common apple leaf diseases with a total of 4500 pictures (i.e., black rot, scab, and rust) were selected from several open-source datasets as the subjects of this paper. The object detection algorithm was then used to verify the effectiveness of the new model. As indicated by the results, it can be seen from the loss curve that the loss rate reaches a stable range of around 70 at the epoch. Compared with Faster R-CNN, the average accuracy of Mask R-CNN for the type and grade recognition of apple leaf disease was optimized by 4.91%, and the average recall rate was increased by 5.19%. The average accuracy of the optimized apple leaf disease grade evaluation model was 90.12%, marking an overall increase of 20.48%. Thus, the effectiveness of the new model was confirmed.
... The ResNet-18 model won first place in the classification category in the ImageNet competition due to its simplicity and practicality. 36,37 It creates many methods based on ResNet-50 and ResNet-101. 38 The ResNet-18 model consists of 17 convolutional layers and one fully connected layer. ...
Medical Imaging with Deep Learning has recently become the most prominent topic in the scientific world. Significant results have been obtained in the classification of medical images using deep learning methods, and there has been an increase in studies on malignant types. The main reason for choosing breast cancer is that breast cancer is one of the critical malignant types that increase the death rate in women. In this study, 1236 ultrasound images were collected from Elazig Fethi Sekin City Hospital, and three different ResNet CNN architectures were used for feature extraction. Data were trained with an SVM classifier. In addition, the three ResNet architectures were combined, and novel fused ResNet architecture was used in this study. In addition, these features were used with three different feature selection techniques, MR‐MR, NCA, and Relieff. These results are 89.3% obtained from ALL‐ResNet architecture and the feature selected with NCA in normal and lesion classification. Normal, malignant, and benign classification best accuracy is 84.9% with ALL‐ResNet NCA. Experimental studies show that MR‐MR, NCA, and Relieff feature selection algorithms reduce features and give more results that are successful. This indicates that the proposed method is more successful than classical deep learning methods.
... The holistic approaches characterize the whole image, depending on whether it contains a lesion or not. Such works have mainly used convolutional neural networks (CNNs) see, e.g., [5], where leaf images were used, or where images acquired by mobile phone cameras were fed into ResNet architectures [6]. ...
... The DNN-based approaches can be further separated into a classification method, a detection method, and a segmentation method according to the types of output [13]. The classification method produces the types of plant diseases and pests [14][15][16][17]. The detection method provides the location, as well as the types of plant diseases and pests [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. ...
As a result of climate change and global warming, plant diseases and pests are drawing attention because they are dispersing more quickly than ever before. The tomato leaf miner destroys the growth structure of the tomato, resulting in 80 to 100 percent tomato loss. Despite extensive efforts to prevent its spread, the tomato leaf miner can be found on most continents. To protect tomatoes from the tomato leaf miner, inspections must be performed on a regular basis throughout the tomato life cycle. To find a better deep neural network (DNN) approach for detecting tomato leaf miner, we investigated two DNN models for classification and segmentation. The same RGB images of tomato leaves captured from real-world agricultural sites were used to train the two DNN models. Precision, recall, and F1-score were used to compare the performance of two DNN models. In terms of diagnosing the tomato leaf miner, the DNN model for segmentation outperformed the DNN model for classification, with higher precision, recall, and F1-score values. Furthermore, there were no false negative cases in the prediction of the DNN model for segmentation, indicating that it is adequate for detecting plant diseases and pests.
... Furthermore, investigated approaches like structure modification, module enhancement and data preprocessing (augmentation, segmentation or background removal) have been validated to enhance the performances [11][12][13][14] , which can be seen as the solution to dataset size problem. ...
... Matrix assessment indicators vary, and indices include precision, recall, and mean average precision (mAP). [21].The harmonic mean F1 score is based on precision and recall. Positive error (FP) indicates a scenario in which the score is actually positive despite being predicted as negative. ...
Farming productivity is essential to the economy, and research on plant diseases plays an important role in agriculture. Plant diseases are normal, and thus ensuring the quality and quantity of product remains a serious challenge faced by farmers. Hence, urgent attention should be paid to plant diseases and their direct impacts on plants. The importance of the topic is reflected by impact on food security. Modern systems and computer vision technologies have greatly facilitated image classification in farming. Convolutional neural networks (CNNs) are latest technologies for image recognition and allow quick and specific diagnosis. This paper demonstrates the importance of CNNs to plant disease classification and how they work, providing a path toward a solution for artificial diseases and reference for researchers and countries with economies based on agriculture.
... Tobacco is an important economic crop of China. e tobacco-related industry is also an important industry in China's social economy [1,2]. e tobacco industry provides an important support for social and economic development every year. ...
In order to improve the problems of poor accuracy and low efficiency in tobacco leaves disease recognition and diagnosis and avoid the misjudgment in tobacco disease recognition, a disease recognition and spot segmentation method based on the improved ORB algorithm was proposed. The improved FAST14-24 algorithm was used to preliminarily extract corners. It overcame the deficiency of the sensitivity of the traditional ORB corner detection algorithm to image edges. During the experiment, 28 parameters were obtained through the extraction of color features, morphological features, texture, and other features of tobacco disease spots. Through the experimental comparisons, it was found that the fitness of the improved ORB algorithm was 96.68 and the cross-checking rate was 93.21%. The validation and recognition rate for samples was 96%. The identification rate of tobacco brown spot disease and frog eye disease was 92%, and the identification rate of 6 categories in different periods was over 96%. The experimental results verified the effects of the disease identification fully.
... There are many basic deep learning models such as Deep Feed Forward, where data extracted from training process is propagated in only one direction in the network through several layers of neurons. There are several algorithms such as: Back-Propagation, Convolutional Neural Network, Recurrent Neural Network, including Long Short-Term Memory, Auto-Encoder, Deep Belief Network, and Deep Reinforcement Learning [8][9][10][11]. One particular very used category of feed forward network that may be very easy to be trained compared to fully connected networks is the one peculiar architecture named the convolutional neural network (CNN). ...
In this paper we present an algorithm based on deep learning. The program allows the user to select in a graphical interface the type of plant for which it is desired to insert images for disease identification. To train and test models, we used our own data set with a relatively small number of images, and all images were captured in culture, and not in laboratory conditions. The algorithm is based on deep learning approach.
