Enhancing Citrus Plant Health through the Application of Image Processing Techniques for Disease Detection

Abstract

The foremost task in agriculture is the decisive identification of citrus plants and the timely identification of diseases in the plants with the aim of improving the quality of crops and the yield. In this work, a machine learning algorithm focuses on image processing of citrus to solve issues that are significant and cause concern in agriculture. This work focus on the machine learning models like VGG 19 and VGG 16. In addition, dataset curation, data augmentation and various other methods were employed. The dataset used in this research is a composed one which is recorded in a comprehensive manner including the data of both the affected and healthy pieces of citrus fruits. The ensemble model utilised here to ensure the improvement of trained datasets. Reviewing the research on machine learning models indicates a possibility for accurate classification of the fruits and disease detection models of the fruit. The three contenders performed admirably, with VGG 19 dominating with 95.5% accuracy. In second place was CNN with 93.4% and VGG 16 trailing at 91.2%. Such models are recognisable, because they perform well in agricultural environments, thanks to their precision, recall, and F1 scores, which are all balanced properly. The models’ capacity to lessen the number of false alarms and misses is further assessed with the use of confusion matrices, which are of utmost importance in disease control. New developments in early disease diagnosis and detection of citrus fruits in agriculture may greatly enhance the health and productivity of crops. This research can be critical in increasing agricultural productivity while ensuring the environmental sustainability and health of growers and citrus crops in the long run.

Plant phenotyping relevance

柑橘の画像から健全・罹病状態を分類する機械学習・画像処理手法が研究の中心であり、植物病害状態のフェノタイピングに該当する。

Code and data availability

The paper describes a composed citrus image dataset (750 Kaggle images plus 482 manually collected) and VGG16/VGG19/CNN models, but provides no public deposit, availability statement, or authors' URL for the dataset, code, or trained models. The Kaggle source is cited prior material, not a paper-specific released asset

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