Application Of Artificial Neural Networks In Plant Sciences: A Comprehensive Review

Abstract

Artificial neural networks (ANNs) have emerged as powerful computational models for handling complex, nonlinear relationships in diverse scientific fields. In plant sciences, ANNs are increasingly used for phenotypic analysis, disease diagnosis, yield prediction, and environmental stress assessment. This paper reviews the evolution of ANN models within plant research, outlines recent advances, discusses methodological approaches, and highlights future directions. The integration of ANNs with image analysis, sensor data, and genomic information presents a promising path toward precision agriculture and sustainable crop management.