SVM-RBN Model with Attentive Feature Culling Method for Early Detection of Fruit Plant Diseases

Abstract

Accurate disease identification and early disease management strategies are required in India to achieve high production standards and good quality in fruits and vegetables. Image-based evaluations approaches have evolved nowadays as a result of technical developments. However, producing the wrong decision may have a negative impact on productivity. Thus, this study offered hybrid SVM-RBN model with attentive feature culling method for automatically recognizing diseases in apple fruits with high accuracy. As a result, the model generated more effective outcomes with 96% accuracy, 99% precision, 94% recall, and 93% F1 Score. Thus, by employing this technology, one may detect fruit plant illnesses at an early stage, thereby increasing fruit yield.

Plant phenotyping relevance

リンゴ果実の画像から病害を自動認識するモデルを開発・評価しており、植物の病害状態を抽出する画像ベースのフェノタイピング手法が中心です。

Code and data availability

The paper uses apple fruit/leaf disease images from Kaggle but provides no specific dataset name, URL, or identifier, and no code, model, or supplement availability is stated anywhere in the supplied blocks.

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