A Review of Crop Attribute Monitoring Technologies for General Agricultural Scenarios

Abstract

As global agriculture shifts to intelligence and precision, crop attribute detection has become foundational for intelligent systems (harvesters, UAVs, sorters). It enables real-time monitoring of key indicators (maturity, moisture, disease) to optimize operations—reducing crop losses by 10–15% via precise cutting height adjustment—and boosts resource-use efficiency. This review targets harvesting-stage and in-field monitoring for grains, fruits, and vegetables, highlighting practical technologies: near-infrared/Raman spectroscopy (non-destructive internal attribute detection), 3D vision/LiDAR (high-precision plant height/density/fruit location measurement), and deep learning (YOLO for counting, U-Net for disease segmentation). It addresses universal field challenges (lighting variation, target occlusion, real-time demands) and actionable fixes (illumination compensation, sensor fusion, lightweight AI) to enhance stability across scenarios. Future trends prioritize real-world deployment: multi-sensor fusion (e.g., RGB + thermal imaging) for comprehensive perception, edge computing (inference delay

Plant phenotyping relevance

作物属性の検出・監視技術を主題とするレビューで、分光、3Dビジョン、LiDAR、深層学習による植物形質・病害状態の取得方法を中心に整理している。

Code and data availability

This is a review article of crop attribute monitoring technologies. All figures and datasets described (e.g., Figures 4, 5, 7) are explicitly cited from other references ('Cited from reference [25]', 'Cited from reference [38]', 'Cited from reference [45]'), and no author-collected phenotype datasets, images, analysis,

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