Abstract
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. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for specific fruit shapes, color and/or other attributes. 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. To each grid cell an image detection and localization algorithm is applied. 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. 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. More than 100 images of apple and pear trees were taken. Each tree image with approximately 50 fruits, that at the end resulted on more than 5000 images of apple and pear fruits each. Labeling images for training consisted on manually specifying the bounding boxes for fruits, where (x, y) are the center coordinates of the box and (w, h) are width and height. This architecture showed an accuracy of more than 90% fruit detection. Based on correlation between number of visible fruits, detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Processing speed is higher than 20 FPS which is fast enough for any grasping/harvesting robotic arm or other real-time applications.HIGHLIGHTSUsing new convolutional deep learning techniques based on single-shot detectors to detect and count fruits (apple and pear) within the tree canopy.
Highlights
Agriculture is a sector with very specific working conditions and constraints
The metrics evaluating the accuracy will be according to the well-known criteria based on Pascal Visual Object Classes (VOC) that much of the research on this field uses
Precision evaluates the fraction of true positives (TP) detected bounding boxes in the pool of all true positives predictions True Positive (TP) and false positive predictions (FP) while recall evaluates the fraction of TP detected bounding boxes in the pool of all TP and false negatives predictions (FN): TP
Summary
Agriculture is a sector with very specific working conditions and constraints This is due to the dependency on the weather conditions, but as well on the labor market. Rural areas are already facing difficulties in creating attractive jobs in general, pushing toward an ongoing migration toward urban centers Those structural changes in agriculture are expected to continue with higher investments in technology. Most fruits when ripe have a distinctive color: red (apples, strawberries, and peaches, etc...), orange (oranges, etc...), or yellow (pears, lemons, peaches, and bananas). This makes them stand out from the green foliage when they are ready to pick (Edan et al, 2009; Barnea et al, 2016). Some fruits even after ripening are still green (apple cv Granny Smith even after ripening does not change color) making them indistinguishable from the foliage on the basis of color alone (Edan et al, 2009)
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