AI-Driven Plant Disease Detection: Leveraging Deep Learning for Accurate Plant Disease Detection from Leaf Images

Abstract

Plant diseases are a critical threat to global food security and agricultural sustainability because of the crop losses they cause. 195 million tons of crops are lost to fungal diseases each year and alone in India, more than 5 million metric tonnes go waste annually from it[1]. Also, from FAO "Globally up to 16% of harvests worth about US$220 billion are lost due to plant pests every year" [2] The urgency of the situation is clear, as wrapped up in these figures are reasons why long-term growth requires early bite detection services to prevent plant disease. In this paper, deep learning algorithms were used to detect diseases in plants by taking image of the leaves as input. Our research is limited to the detection of these 6 plant diseases, Aphid infestation, Bacterial leaf spot disease Black apple scab Early blight Septoria leaf spot on tomato Grape powdery mildew. In order to do this, we construct and train a machine learning model using two different raw image datasets. These were meticulously curated and enriched datasets, geared towards improving the model's generalisability across extensive variety of conditions. Our approach not only facilitates the early detection of these diseases but also demonstrates the potential for scalable, real-time applications in agricultural settings. The results highlight the effectiveness of deep learning in identifying and classifying plant diseases, offering a promising solution for reducing crop losses and improving agricultural productivity.

Plant phenotyping relevance

葉画像から植物病害を検出・分類する深層学習手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として含める。

Code and data availability

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