Using deep-learning techniques eliminates the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. Image/video processing for fruit detection in the tree using hard-coded feature extraction algorithms has shown high accuracy on fruit detection during recent years. Dipartimento di Scienze Agrarie, University of Bologna, Bologna, Italy.
Kushtrim Bresilla *, Giulio Demetrio Perulli, Alexandra Boini, Brunella Morandi, Luca Corelli Grappadelli * and Luigi Manfrini *
0 kommentar(er)
