Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Research Progress on the Application of Crop Yield Calculation based on Image Analysis Technology

Version 1 : Received: 5 February 2024 / Approved: 6 February 2024 / Online: 6 February 2024 (03:52:45 CET)

A peer-reviewed article of this Preprint also exists.

Yu, F.; Wang, M.; Xiao, J.; Zhang, Q.; Zhang, J.; Liu, X.; Ping, Y.; Luan, R. Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation. Remote Sens. 2024, 16, 1003. Yu, F.; Wang, M.; Xiao, J.; Zhang, Q.; Zhang, J.; Liu, X.; Ping, Y.; Luan, R. Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation. Remote Sens. 2024, 16, 1003.

Abstract

Yield calculation is an important link in modern precision agriculture, which is an effective means to improve breeding efficiency, and 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 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.

Keywords

remote sensing images; visible light images; machine learning; deep learning; biomass; yield calculation

Subject

Computer Science and Mathematics, Computer Science

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