Deep Convolutional Neural Networks for image based tomato leaf disease detection

Anandhakrishnan, T.; Jaisakthi, S.M.

doi:10.1016/j.scp.2022.100793

Cited by 34 publications

(11 citation statements)

References 13 publications

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“…Deep learning, especially CNN-based image processing, was combined with gene technology, remote sensing, cloud computing, and IoT to improve the efficiency and effectiveness of the food supply chain in the whole process [ 139 , 202 ]. Several examples indicated applications of CNN models to monitor and predict leaf diseases [ 146 , 148 , 157 , 176 ].…”

Section: Discussionmentioning

confidence: 99%

“…Geng et al (2017) introduced a predictive model based on AHP integrated extreme learning machine (ELM), rather than a traditional artificial neural network (ANN), to monitor the food safety system in China [ 193 ]. Pham et al (2020) and Anandhakrishnan and Jaisakthi (2022) separately detected early diseases on plant leaves with small disease spots through ANN and deep convolutional neural network (DCNN) [ 176 , 180 ]. Zhao et al (2022) proposed a hybrid convolutional network combined with hyperspectral imaging for wheat seed classification [ 178 ].…”

Section: Abstract and Hot Spotsmentioning

confidence: 99%

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Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Liu

Wang

Zhang

et al. 2023

Foods

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Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

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Section: Discussionmentioning

confidence: 99%

Section: Abstract and Hot Spotsmentioning

confidence: 99%

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Liu

Wang

Zhang

et al. 2023

Foods

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show abstract

“…The proposed system's training accuracy for three different types of leaf picture types varies from 96% to 98%, indicating the neural network approach's feasibility. [9]This research develops an autonomous approach for recognizing leaf illness in tomato leaves using Deep CNN. In this paper, they used a plant village data set.…”

Section: Introductionmentioning

confidence: 99%

An Improved CNN model for Identifying Tomato Leaf Diseases

BHARALI

2024

tjes

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A major loss in gross domestic product, quantity and quality of products produced, as well as tomato production, is caused by diseases in tomato leaves due to which farmers have a difficult time in controlling and monitoring the health of tomato leaves, one of which is leaf disease. In our project, we developed an Enhanced CNN by using data augmentation techniques to identify the seven classes(blight ,leaf curl, leaf miner ,Alteneria, leaf spot, cutwork infected) of tomato leaf diseases. Using 27807 trainable parameters, the enhanced CNN obtains the maximum training accuracy of ninety nine point nine eight percent(99.98%) and validating accuracy of ninety eight point four percent(98.4%) .With fewer parameters, The Enhanced CNN can more accurately determine the type of illness of tomato leaf. Our Enhanced CNN model also determine the type of illness of tomato leaf when tested with the images of diseased tomato leaf collected from the internet sources.Using 152850 trainable parameters the enhanced CNN obtain the maximum training accuracy of 99.68% and validation accuracy of 89%.

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“…In the existing grading of agricultural products, the main criterion of grading is still appearance ( 15 , 16 ). However, the biochemical properties of the produce also have an impact on the grade of the produce ( 17 ).…”

Section: Introductionmentioning

confidence: 99%

Intelligent grading method for walnut kernels based on deep learning and physiological indicators

Chen¹,

Dai²,

Jian³

et al. 2023

Front. Nutr.

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Walnut grading is an important step before the product enters the market. However, traditional walnut grading primarily relies on manual assessment of physiological features, which is difficult to implement efficiently. Furthermore, walnut kernel grading is, at present, relatively unsophisticated. Therefore, this study proposes a novel deep-learning model based on a spatial attention mechanism and SE-network structure to grade walnut kernels using machine vision to ensure accuracy and improve assessment efficiency. In this experiment, we found through the literature that both the lightness (L* value) and malondialdehyde (MDA) contens of walnut kernels were correlated with the oxidation phenomenon in walnuts. Subsequently, we clustered four partitionings using the L* values. We then used the MDA values to verify the rationality of these partitionings. Finally, four network models were used for comparison and training: VGG19, EfficientNetB7, ResNet152V2, and spatial attention and spatial enhancement network combined with ResNet152V2 (ResNet152V2-SA-SE). We found that the ResNet152V2-SA-SE model exhibited the best performance, with a maximum test set accuracy of 92.2%. The test set accuracy was improved by 6.2, 63.2, and 74.1% compared with that of ResNet152V2, EfficientNetB7, and VGG19, respectively. Our testing demonstrated that combining spatial attention and spatial enhancement methods improved the recognition of target locations and intrinsic information, while decreasing the attention given to non-target regions. Experiments have demonstrated that combining spatial attention mechanisms with SE networks increases focus on recognizing target locations and intrinsic information, while decreasing focus on non-target regions. Finally, by comparing different learning rates, regularization methods, and batch sizes of the model, we found that the training performance of the model was optimal with a learning rate of 0.001, a batch size of 128, and no regularization methods. In conclusion, this study demonstrated that the ResNet152V2-SA-SE network model was effective in the detection and evaluation of the walnut kernels.

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Deep Convolutional Neural Networks for image based tomato leaf disease detection

Cited by 34 publications

References 13 publications

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

An Improved CNN model for Identifying Tomato Leaf Diseases

Intelligent grading method for walnut kernels based on deep learning and physiological indicators

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