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A Smart Agronomy: Deep Learning Process for Recognition and Classification Plant Leaf Diseases

A Smart Agronomy: Deep Learning Process for Recognition and Classification Plant Leaf Diseases
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Author(s): Chandra Prabha Ramakrishnappa (BMS Institute of Technology and Management, India)and Seema Singh (BMS Institute of Technology and Management, India)
Copyright: 2024
Pages: 18
Source title: Advanced Computational Methods for Agri-Business Sustainability
Source Author(s)/Editor(s): Suchismita Satapathy (KIIT University (Deemed), India)and Kamalakanta Muduli (Papua New Guinea University of Technology, Papua New Guinea)
DOI: 10.4018/979-8-3693-3583-3.ch002

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Abstract

The main part of the agriculture process is the timely detection of leaf diseases to have a healthy growth. In routine implementation, the identification of diseases is realized either by manual or laboratory testing. Physical testing involves few expertise and results could vary from individuals which can result in false interpretation while the latter requires extra time and might not be able to deliver the production, due to which the spread of disease gradually increases. Hence an automated system is required for the identification and classification of the disease. This chapter intends leaf sickness detection and recognition by applying deep learning for two data split ratios. The classification task is performed using Alex-net, a pre-trained architecture. The data set has three categories of leaf disease, namely, bacterial leaf blight, brown spot, and leaf blast, each consisting of 40 infected images. The proposed architecture classifies the diseases into three categories. The comparison study for various performance metrics—such as recall, precision, and specificity—is measured.

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