Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation

Abstract

Yield calculation is an important link in modern precision agriculture that is an effective means to improve breeding efficiency and to adjust planting and marketing plans. With the continuous progress of artificial intelligence and sensing technology, yield-calculation schemes based on image-processing technology have many advantages such as high accuracy, low cost, and non-destructive calculation, and they have been favored by a large number of researchers. This article reviews the research progress of crop-yield calculation based on remote sensing images and visible light images, describes the technical characteristics and applicable objects of different schemes, and focuses on detailed explanations of data acquisition, independent variable screening, algorithm selection, and optimization. Common issues are also discussed and summarized. Finally, solutions are proposed for the main problems that have arisen so far, and future research directions are predicted, with the aim of achieving more progress and wider popularization of yield-calculation solutions based on image technology.

Plant phenotyping relevance

画像解析による作物収量推定という植物形質取得手法を中心に、データ取得、変数選択、アルゴリズム、最適化をレビューしているため。

Code and data availability

This is a review article on image-analysis-based crop-yield estimation. It presents no original phenotyping measurements, datasets, images, or analysis code. The Data Availability Statement says data are available only on request from the corresponding author, and all cited works are prior publications, not paper-quali

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