Harnessing artificial intelligence for sustainable rice leaf disease classification.

Abstract

Introduction Agriculture underpins global food security by providing food, raw materials, and livelihoods, contributing 4% to global GDP and up to 25% in rural areas. Rice, a staple for more than half of the world's population, is nutritionally vital but highly vulnerable to diseases such as Hispa, leaf blast, and brown spots, which significantly reduce yield and quality. Achieving Sustainable Development Goal (SDG) 2 requires innovative approaches to mitigate these threats. Artificial intelligence (AI), particularly computer vision and machine learning, offers promising tools for early disease detection. Methods This study developed a convolutional neural network (CNN)-based model for rice leaf disease detection and classification. A publicly available dataset containing 3,355 labeled images across four categories-Brown Spot, Leaf Blast, Hispa, and Healthy leaves-was used to train and evaluate the model. To improve classification accuracy, the CNN was enhanced with spatial and channel attention mechanisms, enabling it to focus on the most discriminative image regions. The system was designed for modular deployment, allowing lightweight, real-time implementation on edge devices. Results The enhanced CNN achieved high accuracy and robust performance metrics across all disease categories. Attention mechanisms significantly improved precision in identifying subtle disease patterns. The lightweight design ensured efficient operation on edge devices, demonstrating feasibility for real-world agricultural applications. Discussion and conclusion The proposed AI-driven system provides reliable and scalable rice leaf disease detection, supporting timely intervention to reduce yield loss. By strengthening rice production and promoting sustainable practices, the model contributes to SDG 2 by advancing global food security. This research highlights AI's transformative role in agriculture, fostering mechanization, ecological stability, and resilience in food systems.

Plant phenotyping relevance

イネ葉の病徴を画像から分類するCNN手法を開発・評価しており、植物の病害状態を直接推定するフェノタイピング手法が研究の中心である。

Code and data availability

The paper's data availability statement points to a public Kaggle dataset of 3,355 labeled rice leaf disease images (Brown Spot, Leaf Blast, Hispa, Healthy) used to train and evaluate the CNN model. No author code or trained model deposit is mentioned.