Deep fruit detection in orchards

An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical deployment of the detection network, including how much training data is required to capture variability in the dataset. Data augmentation techniques are shown to yield significant performance gains, resulting in a greater than two-fold reduction in the number of training images required. In contrast, transferring knowledge between orchards contributed to negligible performance gain over initialising the Deep Convolutional Neural Network directly from ImageNet features. Finally, to operate over orchard data containing between 100-1000 fruit per image, a tiling approach is introduced for the Faster R-CNN framework. The study has resulted in the best yet detection performance for these orchards relative to previous works, with an F1-score of > 0. 9 achieved for apples and mangoes.

Authors

• Suchet Bargoti
• James Patrick Underwood

Keywords

• Object detection
• Image color analysis
• Training
• Land vehicles
• Robot sensing systems
• Computer vision
• Fruit Detection
• Training Data
• Convolutional Neural Network
• Deep Neural Network
• Detection Performance
• Data Augmentation
• Number Of Images
• Training Images
• Variance In The Dataset
• Detection Framework
• Augmentation Techniques
• Output Map
• Data Augmentation Techniques
• Number Of Training Images
• Object Detection Framework
• Imaging Data
• Low Resolution
• Large Image
• Yield Estimation
• Transfer Learning
• VGG-16 Network
• Bounding Box
• Hand-held Camera
• Ground Vehicles
• Convolutional Layers
• Detection Results
• Non-maximum Suppression