Towards a Machine Vision-Based Yield Monitor for the Counting and Quality Mapping of Shallots.

ABSTRACT: In comparison to field crops such as cereals, cotton, hay and grain, specialty crops often require more resources, are usually more sensitive to sudden changes in growth conditions and are known to produce higher value products. Providing quality and quantity assessment of specialty crops during harvesting is crucial for securing higher returns and improving management practices. Technical advancements in computer and machine vision have improved the detection, quality assessment and yield estimation processes for various fruit crops, but similar methods capable of exporting a detailed yield map for vegetable crops have yet to be fully developed. A machine vision-based yield monitor was designed to perform size categorization and continuous counting of shallots in-situ during the ha

SUBMITTER: Boatswain Jacques AA 

PROVIDER: S-EPMC8146908 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature