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

A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20%of the total consumption.An increase of 3.3%in consumption is predicted from 2024 to 2032.Tomatoes 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 levels.Thus,tomatoes are globally important due to their widespread usage and nutritional value.To face the high demand for tomatoes,it is mandatory to investigate the causes of crop loss and minimize them.Diseases are one of the major causes that adversely affect crop yield and degrade the quality of the tomato fruit.This leads to financial losses and affects the livelihood of farmers.Therefore,automatic disease detection at any stage of the tomato plant is a critical issue.Deep 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 parameters.Also,most of the models proposed so far work efficiently for images with plain backgrounds where a clear demarcation exists between the background and leaf region.Moreover,the existing techniques lack in recognizing multiple diseases on the same leaf.To address these concerns,we introduce a customized deep learning-based convolution vision transformer model.Themodel achieves an accuracy of 93.51%for classifying tomato leaf images with plain as well as complex backgrounds into 13 categories.It 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,respectively.Its training time of 44 min is 51.12%,74.12%,and 57.7%lower than the above-mentioned models.Thus,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 crops.The periodicmonitoring promotes timely action to prevent the spread of diseases and reduce crop loss.

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

The increasing gap between the demand and productivity of maize crop is a point of concern for the food industry,and farmers.Its'susceptibility to diseases such as Turcicum Leaf Blight,and Rust is a major cause for reducing its production.Manual detection,and classification of these diseases,calculation of disease severity,and crop loss estimation is a time-consuming task.Also,it requires expertise in disease detection.Thus,there is a need to find an alternative for automatic disease detection,severity prediction,and crop loss estimation.The 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 crop.The research works available in literature,have proven their potential in automatic disease detection using machine learning,and deep learning models.But,there is a lack none of these works a reliable and real-life labelled dataset for training these models.Also,none of the existing works focus on severity prediction,and crop loss estimation.The authors in this manuscript collect the real-life dataset labelled by plant pathologists.They propose a deep learning-based framework for pre-processing of dataset,automatic disease detection,severity prediction,and crop loss estimation.It uses the K-Means clustering algorithm for extracting the region of interest.Next,they employ the customized deep learning model‘MaizeNet’for disease detection,severity prediction,and crop loss estimation.The model reports the highest accuracy of 98.50%.Also,the authors perform the feature visualization using the Grad-CAM.Now,the proposed model is integrated with a web application to provide a userfriendly interface.The 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.The copyright for the associated web application‘Maize-Disease-Detector’is filed with diary number:17006/2021-CO/SW.