Machine Learning-based Citrus Plant Disease Detection and Management System using Computer Vision

Abstract

This article presents a novel method for identifying and managing diseases that affect citrus fruits by utilizing cutting-edge computer vision and machine learning techniques. The method is presented in this article. The proposed system utilizes a Convolutional Neural Network (CNN) model to extract features from images and classify them as healthy or diseased. Following this, a Random Forest algorithm is used to make the final prediction based on the extracted features. The system enables farmers to easily upload images of their citrus fruits from mobile or web-based platforms, enabling instant diagnosis of diseases and providing effective management plans to address the issue. The design of the proposed system is centered on enhancing the performance of the model in recognizing patterns that are associated with healthy and diseased fruits. In addition to this, it features an automatic management plan generation and alert system, both of which are designed to prompt farmers to take prompt actions for the prevention and control of diseases. The purpose of the proposed system is to deliver an instrument that is both effective and simple to use for the diagnosis and treatment of diseases, which will ultimately result in increased crop yields and increased profitability for citrus growers. The agricultural sector as a whole intends to gain significantly from the successful implementation of this system, which has the potential to bring about significant improvements.

Plant phenotyping relevance

柑橘果実画像から健全・罹病状態を推定するコンピュータビジョン手法が研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。

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