期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 4, 期 4, 页码 3216-3223出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2926957
关键词
Unmanned aerial vehicles; agro-food robotics; machine vision; plants breeding; field assessment; crop emergence
类别
资金
- European cooperation program INTERREG Deutschland-Nederland [143081]
Crop breeding consists of the process of editing crop genetic profile for increasing many crop qualities. In order to achieve optimal results, crop breeders have to plant thousands of plants and keep a track of their growth almost daily. This process requires increased man-hour inspection over large fields, which results in poor accuracy due to human fatigue and a time-inefficient strategy. In this letter, two machine vision approaches were compared for classifying three crop germination classes (good, average, and bad). A naive approach using a classical segmentation and an unsupervised learning approach using k-means segmentation were compared within a high-resolution unmanned aerial vehicles imagery dataset. Experimental results demonstrate the classification of germinated patches up to 0.05 m(2) /patch of resolution with a minimum F1-score of 76% and 80%, and AUC of 95% and 91% for high and low spatial image resolutions, respectively.
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