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      Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease

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          Abstract

          Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data. We utilized an architecture for deep learning based on a recently developed convolutional neural network that is trained over 18,161 segmented and non-segmented tomato leaf images—using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work. For the detection and segmentation of disease-affected regions, two state-of-the-art semantic segmentation models, i.e., U-Net and Modified U-Net, are utilized in this work. The plant leaf pixels are binary and classified by the model as Region of Interest (ROI) and background. There is also an examination of the presentation of binary arrangement (healthy and diseased leaves), six-level classification (healthy and other ailing leaf groups), and ten-level classification (healthy and other types of ailing leaves) models. The Modified U-net segmentation model outperforms the simple U-net segmentation model by 98.66 percent, 98.5 IoU score, and 98.73 percent on the dice. InceptionNet1 achieves 99.95% accuracy for binary classification problems and 99.12% for classifying six segmented class images; InceptionNet outperformed the Modified U-net model to achieve higher accuracy. The experimental results of our proposed method for classifying plant diseases demonstrate that it outperforms the methods currently available in the literature.

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          Rethinking the Inception Architecture for Computer Vision

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            MobileNetV2: Inverted Residuals and Linear Bottlenecks

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              Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                07 October 2022
                2022
                : 13
                : 1031748
                Affiliations
                [1] 1 Department of Computer Science, CECOS University of Information Technology (IT) and Emerging Sciences , Peshawar, Pakistan
                [2] 2 High Performance Computing and Networking Institute, National Research Council (ICAR-CNR) , Naples, Italy
                [3] 3 College of Technological Innovation, Zayed University , Dubai, United Arab Emirates
                [4] 4 Department of Robotics and Mechatronics Engineering, Daegu Gyeonbuk Institute of Science and Engineering (DGIST) , Daegu, South Korea
                [5] 5 Institute of Computer Science & Information Technology, The University of Agriculture Peshawar , Peshawar, Pakistan
                [6] 6 Department of Software, Sejong University , Seoul, South Korea
                Author notes

                Edited by: Marcin Wozniak, Silesian University of Technology, Poland

                Reviewed by: Moazam Ali, Bahria University, Pakistan; Parminder Singh, Mohammed VI Polytechnic University, Morocco

                *Correspondence: Sang Hyun Park, shpark13135@ 123456dgist.ac.kr ; Farman Ali, Farmankanju@ 123456sejong.ac.kr

                †These authors have contributed equally to this work and share first authorship

                This article was submitted to Plant Bioinformatics, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2022.1031748
                9585275
                36275583
                664dca01-bbbe-4968-b960-e3235d3b66b5
                Copyright © 2022 Shoaib, Hussain, Shah, Ullah, Shah, Ali and Park

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 August 2022
                : 15 September 2022
                Page count
                Figures: 10, Tables: 8, Equations: 9, References: 54, Pages: 18, Words: 7774
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                plant disease detection,deep learning,u-net cnn,inception-net,object detection and recognition

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