A Comprehensive Review Of Statistical Methods For Enhancing Performance In Precision Agriculture

Abstract

Even more precision agriculture tools rely on statistical techniques for fruit detection and recognition optimisation. A thorough review of these statistical techniques was built in this paper, and they have been applied to fruit detection systems since 2018 for efficiency, accuracy, and robustness. Thinking about how advanced statistical methods integrate with machine learning algorithms and deep neural networks, we face significant obstacles such as handling imbalanced datasets, improving detection in occluded or dense environments and dealing with variation between fruit size, shape, and species. Statistical methods also enhance data cleaning before use, feature extraction, and model evaluation, leading to faster fruit detection and more accurate results. A review paper is the result of combining ideas from many experts. It should help the development of precision agriculture focused on lower resource usage farther afield and seeing agro-environmental detection systems as equivalent. Now, we discuss solutions to those problems and future trends in statistical methodology. This work aims to set the direction for future precision agriculture research and practice by pointing out how to reduce the computational complexity of detection systems under various agro-environmental conditions.