AUTHOR=Rana Muhammad Ehsan , Hameed Vazeerudeen Abdul , Eng Ian Kiew Yi , Tripathy Hrudaya Kumar , Mallik Saurav 

TITLE=Harnessing artificial intelligence for sustainable rice leaf disease classification

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1594329

DOI=10.3389/fpls.2025.1594329

ISSN=1664-462X

ABSTRACT=IntroductionAgriculture 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.MethodsThis 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.ResultsThe 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 conclusionThe 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.