Early Detection of Sterility Mosaic Disease (SMD) in Pigeon Pea (Arhar/Tur) Using Machine Learning and Regional Data Sources: A Review

Sterility Mosaic Disease (SMD), commonly known as the "Green Plague" of pigeon pea, is one of the major threats to pulse production in South Asia, particularly India. The disease is spread by the eriophyid mite Aceria cajani and is brought on by the Pigeon pea Sterility Mosaic Virus (PPSMV).and causes partial or total sterility of plants, resulting in production losses that range from 30% to 100%. Although they produce accurate results, traditional diagnostic methods like field scouting, serological testing, and molecular procedures like PCR are labor-intensive, time-consuming, and not appropriate for widespread use.

Deep learning (DL) and machine learning (ML) models have become more well-known in recent years as quick, scalable, and affordable approaches to early disease identification. These models, which include lightweight architectures, transfer learning, and Convolutional neural networks (CNNs) are increasingly being used in pigeon peas because they have demonstrated exceptional accuracy in recognizing plant diseases in a range of crops.

 Existing information on SMD epidemiology, conventional and contemporary detection methods, databases, and difficulties is compiled in this review. It presents cutting-edge machine learning techniques and talks about how they may be incorporated into farmer-centric solutions. Model performance is summarized in tables, while publishing patterns, ML pipelines, and disease symptoms are depicted in figures. The analysis concludes by outlining future directions and research gaps, including as explainable AI, multimodal data integration, and policy-level adoption for sustainable SMD management.

Plant pathology, pigeon pea, machine learning, deep learning, sterility mosaic disease, and regional agriculture

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