Beyond Traditional Farming: An Exploration of Machine Learning Technologies in Banana Agriculture – A Literature Review

Agricultural techniques all around the world include the cultivation and evaluation of bananas. With improved accuracy, economy, and scalability, the application of Machine Learning (ML) approaches has transformed the evaluation of banana quality in recent years. Through the division of results into quality and grading, disease detection and classification, and comparative assessments of ML techniques, this paper thoroughly examines the field of ML applications in banana analysis. Distinguished results comprise the creation of Dual Channel Banana Grading Systems with Deep Learning Based Accuracy Rates Reaching Up to 99% and the application of Convolutional Neural Networks (CNNs) with 90%+ accuracy for illness classification. Examining issues like model robustness and data bias, the paper goes on to clarify the implications and constraints of machine learning in banana assessment.This paper highlights the revolutionary potential of machine learning (ML) in promoting banana cultivation through thorough analysis and comparison with previous research. In order to improve agricultural techniques and make them more accurate, efficient, and sustainable, it is important to continue researching and developing machine learning models that are specifically designed to address the difficulties involved in growing bananas.