Current developments in machine learning for agriculture: Semantic division approaches and critical evaluation of farm image analysis

Abstract

As agriculture continues to evolve, there is an increasing demand for efficient as well as sustainable practices to ensure food security and optimize resource utilization. Agricultural image processing is revolutionized by the integration of machine learning techniques with conventional approaches and semantic segmentation has become a critical area of study attention. Performance in tasks like crop cover analysis, type identification and pest/disease detection has been improved by this synthesis. Using segmentation principles as a guide, this paper thoroughly examines current developments in conventional and machine learning-based semantic segmentation techniques for agricultural imagery. The conversation focuses on machine learning's effective features that can be used in concert with the original image data. There are acknowledged challenges with agricultural image segmentation, such as the requirement for improved generalization and resilience. The review tackles the lack of labelled samples by showcasing novel approaches such as dataset augmentation and multimodal information integration that improve machine learning techniques. This culminates in a valuable guide for using image semantically segmented in agricultural informatization.