Advancements in Nematode Management: Exploring Machine Learning in Precision Agriculture

Abstract

Agriculture serves as the backbone of India's economy. The ongoing rise in global population, coupled with climate change and rapid urbanization, has significantly impacted agriculture. Maximizing crop yields is imperative to ensure global food security. Precision agriculture, or the integration of computation into agricultural practices is not a recent concept. Machine learning (ML) algorithms have long been employed in tasks such as vegetation analysis, crop modelling, and yield management. However, the application of ML in agricultural pest management is currently gaining attention, as effective pest control necessitates accurate pest identification - a challenging task. Automating this process holds promise for more efficient disease management. Various ML algorithms have been utilized in precision agriculture, and now, some are being explored for the identification and management of plant parasitic nematodes, which pose a significant threat to agricultural crops worldwide. This article provides an overview of the algorithms utilized and the databases developed to facilitate the efficient identification of plant parasitic nematodes.