Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

Abstract

Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

Circle Fitting Based Image Segmentation and Multi-Scale Block Local Binary Pattern Based Distinction of Ring Rot and Anthracnose on Apple Fruits

“…Deep learning has been applied to plant disease image recognition ( Tan et al, 2015 ; DeChant et al, 2017 ; Lu et al, 2017 ; Liu et al, 2018 ; Bansal et al, 2021 ; Caldeira et al, 2021 ; Chen et al, 2021 ; Trivedi et al, 2021 ; Narmadha et al, 2022 ). It can reduce image preprocessing operations and achieve satisfactory disease recognition results.…”

“…Image processing technology has been widely applied in the diagnosis, identification, and monitoring of plant diseases ( Sankaran et al, 2010 ; Barbedo, 2016 ; Vishnoi et al, 2021 ), such as wheat diseases ( Li et al, 2012 ; Johannes et al, 2017 ; Deng et al, 2021 ), maize diseases ( DeChant et al, 2017 ; Chen et al, 2021 ), rice diseases ( Phadikar et al, 2013 ; Lu et al, 2017 ; Narmadha et al, 2022 ), cotton diseases ( Camargo and Smith, 2009 ; Caldeira et al, 2021 ), soybean diseases ( Pires et al, 2016 ; Shrivastava et al, 2017 ; Araujo and Peixoto, 2019 ), cucumber diseases ( Vakilian and Massah, 2013 ; Zhang S. W. et al, 2017 ; Kainat et al, 2021 ), tomato diseases ( Yamamoto et al, 2017 ; Trivedi et al, 2021 ), grape diseases ( Tian et al, 2007 ; Oberti et al, 2014 ; Zhu et al, 2020 ), and citrus diseases ( Pydipati et al, 2006 ; Sankaran et al, 2013 ). Moreover, image processing technology has been used to make disease severity assessments ( Li et al, 2011 ; Barbedo, 2014 ; Vieira et al, 2014 ; Shrivastava et al, 2015 ; Ganthaler et al, 2018 ), conduct pathogen identification ( Chesmore et al, 2003 ; Deng et al, 2012 ; Wang et al, 2021 ), and perform automatic counting of pathogen spores ( Li X. L. et al, 2013 ; Li et al, 2017 ).…”

Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images

“…Tomato is an important horticultural crop and is popular in smart farms in many countries. Many types of research related to tomatoes are made to increase the tomato quality and production, such as leaf disease detection [ 2 , 3 , 4 , 5 ]. These researchers used different deep convolution neural networks to classify the tomato leaf images to different types of diseases.…”

Artificial Neural Network for Discrimination and Classification of Tropical Soybean Genotypes of Different Relative Maturity Groups

“…Moreover, different from parametric statistical approaches, they capture the complex characteristics of a dataset in addition to being slightly susceptible to noise and outliers and being suitable for nonlinearly separable problems common to agricultural experimentation ( Kavzoglu and Mather, 2003 ; Sudheer et al, 2003 ; Haykin, 2008 ). Currently, at the experimental level, ANN models have been used in the prediction of genetic values ( Soares et al, 2015 ), adaptability and stability ( do Carmo Oda et al, 2019 ), phenotyping ( Sá, 2018 ), yield estimates ( Lu et al, 2022 ), genetic diversity ( Rahimi et al, 2019 ; Taratuhin et al, 2020 ), disease detection, and classification ( Hang et al, 2019 ; Trivedi et al, 2021 ). Moreover, they have demonstrated that the efficiency in the breeding stages can be increased, which can reduce the time and cost of obtaining high-performance cultivars.…”