Fruit detection matlab source code free download

Each tree image with approximately 50 fruits, that at the end resulted on more than 5000 images of apple and pear fruits each.

More than 100 images of apple and pear trees were taken. We want this confidence score to be high if a fruit exists in a cell, otherwise to be zero, if no fruit is in the cell. Each of those cells is responsible to predict bounding boxes and confidence score for fruit (apple and pear in the case of this study) detected in that cell. To each grid cell an image detection and localization algorithm is applied.

This architecture takes the input image and divides into AxA grid, where A is a configurable hyper-parameter that defines the fineness of the grid. 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 *

Fruit detection matlab source code free download