AI-driven technologies for pest monitoring, unsound kernel detection, and intelligent aeration in grain storage

Abstract

Grain storage plays a crucial role in safeguarding food security and maintaining market stability, and it has therefore attracted growing attention from both academia and industry. The primary objective of storage technologies is to minimize post-harvest losses caused by pests, mold, and mechanical damage. However, conventional storage management methods, which rely heavily on manual labor, are often inefficient and costly. With the rapid advancement of artificial intelligence (AI), various approaches, such as convolutional neural network (CNN)-based models, Transformer-based frameworks, and emerging Mamba architectures, have been introduced into the field of grain storage. This paper presents a comprehensive review of artificial intelligence methodologies applied across multiple stages of the grain storage process. From four complementary perspectives, including application significance, existing AI techniques, comparative analysis, and future development trends, the review systematically summarizes current progress in pest monitoring, unsound kernel detection, and intelligent aeration. It critically examines their respective advantages and limitations, while outlining key challenges and future research directions. The review aims to offer a global perspective on the integration of AI technologies in grain storage and to foster interdisciplinary collaboration toward the development of intelligent, efficient, and sustainable storage systems.