Hybrid feature optimized CNN for rice crop disease prediction

Abstract

Abstract The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy crop production. Advances in deep learning have greatly enhanced disease diagnostic techniques in agriculture. Accurate identification of rice plant diseases is crucial to preventing the severe consequences these diseases can have on crop yield. Current methods often struggle with reliably diagnosing conditions and detecting issues in leaf images. Previously, leaf segmentation posed challenges, and while analyzing complex disease stages can be effective, it is computationally intensive. Therefore, segmentation methods need to be more accurate, cost-effective, and reliable. To address these challenges, we propose a hybrid bio-inspired algorithm, named the Hybrid WOA_APSO algorithm, which merges Adaptive Particle Swarm Optimization (APSO) with the Whale Optimization Algorithm (WOA). For disease classification in rice crops, we utilize a Convolutional Neural Network (CNN). Multiple experiments are conducted to evaluate the performance of the proposed model using benchmark datasets (Plantvillage), with a focus on feature extraction, segmentation, and preprocessing. Optimizing feature selection is a critical factor in enhancing the classification algorithm’s accuracy. We compare the accuracy, sensitivity, and specificity of our model against industry-standard techniques such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and conventional CNN models. The experimental results indicate that the proposed hybrid approach achieves an impressive accuracy of 97.5% (Refer Table 8), which could inspire further research in this field.

Plant phenotyping relevance

イネ葉画像から病害状態を推定するCNN・特徴選択・セグメンテーション手法を提案し、ベンチマークデータセットと既存手法で性能比較しており、表現型取得・推定法が研究の中心である。

Code and data availability

The paper uses a 2096-image rice crop dataset from Kaggle and PlantVillage benchmark images, but provides no author-deposited dataset URL, no code availability statement, and no trained model release. The Kaggle/PlantVillage sources are third-party public repositories, not paper-specific assets, and no authors' public,

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