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

Abstract

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.

Code and data availability

This is a review article on AI for sugarcane nematode detection. It presents no original phenotyping measurements, datasets, images, code, or models of its own; all cited studies are prior work, and no public repository or data availability statement appears in the supplied blocks.

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