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Yield sensing technologies for perennial and annual horticultural crops: a review
Yield sensing technologies for perennial and annual horticultural crops: a review
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Yield sensing technologies for perennial and annual horticultural crops: a review
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Yield sensing technologies for perennial and annual horticultural crops: a review
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Yield sensing technologies for perennial and annual horticultural crops: a review
Yield sensing technologies for perennial and annual horticultural crops: a review
Journal Article

Yield sensing technologies for perennial and annual horticultural crops: a review

2022
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Overview
Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems.