2021/10/24 by Thiago Teixeira Santos, Santos, Thiago T., Luciano Gebler +1
Agricultural and Biological Sciences · Chemistry · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #I.4.9 #Remote Sensing in Agriculture #Smart Agriculture and AI #Spectroscopy and Chemometric Analyses
paper · pdf · doi:10.48550/arxiv.2110.12331
openalex publication_date 2021/10/24 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Computer vision methods based on convolutional neural networks (CNNs) have presented promising results on image-based fruit detection at ground-level for different crops. However, the integration of the detections found in different images, allowing accurate fruit counting and yield prediction, have received less attention. This work presents a methodology for automated fruit counting employing aerial-images. It includes algorithms based on multiple view geometry to perform fruits tracking, not just avoiding double counting but also locating the fruits in the 3-D space. Preliminary assessments show correlations above 0.8 between fruit counting and true yield for apples. The annotated dataset employed on CNN training is publicly available.