Review on Plant Parasitic Nematode (PPN) Infections in Sugarcane Cultivation Using AI Algorithms

Arjunan, Viswanathan and Deenan, Surya Prabha (2025) Review on Plant Parasitic Nematode (PPN) Infections in Sugarcane Cultivation Using AI Algorithms. International Journal of Science and Research Archive, 14 (2). pp. 430-441. ISSN 2582-8185

Sugarcane farming plays a vital role in India's economy, society, and culture, as the country is among the top producers and users of sugarcane globally. Plant parasitic nematodes (PPNs) is a major global threat to sugarcane crops, resulting in yield reductions and financial hardship for farmers. In order to minimize crop damage and implement efficient management strategies, the early detection of nematode infestations is imperative. Artificial Intelligence (AI) presents a viable approach for the early identification, tracking, and prevention of damage caused by nematodes through the implementation of cutting-edge machine learning algorithms, remote sensing technologies, and data analytics. This review focuses on the use of AI in sugarcane crop nematode infection detection and management. By integrating AI technologies in a complementary way with conventional agricultural practices, it is feasible to enhance the productivity and resistance of sugarcane crops to nematode infections.