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Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds

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Computers, Materials & Continua 2024年第4期79卷 117-142页

作者： Ankita Gangwar vijaypal singh dhaka Geeta Rani Shrey Khandelwal Ester Zumpano Eugenio Vocaturo Department of Computer and Communication Engineering Manipal University JaipurJaipurIndia Department of IoT and Intelligent Systems Manipal University JaipurJaipurIndia Department of Computer Engineering ModellingElectronics and Systems(DIMES)University of CalabriaRende(Cosenza)Italy National Research Council Institute of Nanotechnology(NANOTEC)Rende(Cosenza)Italy

A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20%of the total *** increase of 3.3%in consumption is predicted from 2024 to *** are also rich in iron,potassium,antioxidant lycopene,vitamins A,C and K which are important for preventing cancer,and maintaining blood pressure and glucose ***,tomatoes are globally important due to their widespread usage and nutritional *** face the high demand for tomatoes,it is mandatory to investigate the causes of crop loss and minimize *** are one of the major causes that adversely affect crop yield and degrade the quality of the tomato *** leads to financial losses and affects the livelihood of ***,automatic disease detection at any stage of the tomato plant is a critical *** learning models introduced in the literature show promising results,but the models are difficult to implement on handheld devices such as mobile phones due to high computational costs and a large number of ***,most of the models proposed so far work efficiently for images with plain backgrounds where a clear demarcation exists between the background and leaf ***,the existing techniques lack in recognizing multiple diseases on the same *** address these concerns,we introduce a customized deep learning-based convolution vision transformer *** achieves an accuracy of 93.51%for classifying tomato leaf images with plain as well as complex backgrounds into 13 *** requires a space storage of merely 5.8 MB which is 98.93%,98.33%,and 92.64%less than stateof-the-art visual geometry group,vision transformers,and convolution vision transformermodels,*** training time of 44 min is 51.12%,74.12%,and 57.7%lower than the above-mentioned ***,it can be deployed on(Internet of Things)IoT-enabled devices,drones,or mobile devices to assist farmers in the real-time monitoring of tomato *** p

关键词： Tomato disease transformer deep learning mobile devices

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Disease detection,severity prediction,and crop loss estimation in MaizeCrop using deep learning

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Artificial Intelligence in Agriculture 2022年第1期6卷 276-291页

作者： Nidhi Kundu Geeta Rani vijaypal singh dhaka Kalpit Gupta Siddaiah Chandra Nayaka Eugenio Vocaturo Ester Zumpano Department of Computer and Communication Engineering Manipal University JaipurJaipurIndia ICAR DOS in Biotechnology University of Mysore ManasagangotriMysore 570005India DIMES(Department of Computer Engineering ModelingElectronics and Systems)University of CalabriaItaly Nanotec Italian National Research Council87036 RendeCSItaly

The increasing gap between the demand and productivity of maize crop is a point of concern for the food industry,and ***'susceptibility to diseases such as Turcicum Leaf Blight,and Rust is a major cause for reducing its *** detection,and classification of these diseases,calculation of disease severity,and crop loss estimation is a time-consuming ***,it requires expertise in disease ***,there is a need to find an alternative for automatic disease detection,severity prediction,and crop loss *** promising results of machine learning,and deep learning algorithms in pattern recognition,object detection,and data analysis motivate researchers to employ these techniques for disease detection,classification,and crop loss estimation in maize *** research works available in literature,have proven their potential in automatic disease detection using machine learning,and deep learning ***,there is a lack none of these works a reliable and real-life labelled dataset for training these ***,none of the existing works focus on severity prediction,and crop loss *** authors in this manuscript collect the real-life dataset labelled by plant *** propose a deep learning-based framework for pre-processing of dataset,automatic disease detection,severity prediction,and crop loss *** uses the K-Means clustering algorithm for extracting the region of ***,they employ the customized deep learning model‘MaizeNet’for disease detection,severity prediction,and crop loss *** model reports the highest accuracy of 98.50%.Also,the authors perform the feature visualization using the ***,the proposed model is integrated with a web application to provide a userfriendly *** efficacy of the model in extracting the relevant features,a smaller number of parameters,low training time,high accuracy favors its importance as an assisting tool for plant pathology experts.T

关键词： Disease detection Crop loss Severity Deep learning Maize

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