Weakly Supervised Fruit Counting for Yield Estimation Using Spatial Consistency. Bellocchio, E., Ciarfuglia, T. A., Costante, G., & Valigi, P. IEEE ROBOTICS AND AUTOMATION LETTERS, 4(3):2348–2355, 2019.
Weakly Supervised Fruit Counting for Yield Estimation Using Spatial Consistency [link]Paper  doi  abstract   bibtex   
Fruit counting is a fundamental component for yield estimation applications. Most of the existing approaches address this problem by relying on fruit models (i.e., by using object detectors) or by explicitly learning to count. Despite the impressive results achieved by these approaches, all of them need strong supervision information during the training phase. In agricultural applications, manual labeling may require a huge effort or, in some cases, it could be impossible to acquire fine-grained ground truth labels. In this letter, we tackle this problem by proposing a weakly supervised framework that learns to count fruits without the need for task-specific supervision labels. In particular, we devise a novel convolutional neural network architecture that requires only a simple image level binary classifier to detect whether the image contains instances of the fruits or not and combines this information with image spatial consistency constraints. The result is an architecture that learns to count without task-specific labels (e.g., object bounding boxes or the multiplicity of fruit instances in the image). The experiments on three different varieties of fruits (i.e., olives, almonds, and apples) show that our approach reaches performances that are comparable with SotA approaches based on the supervised paradigm.
@article{
	11391_1448956,
	author = {Bellocchio, Enrico and Ciarfuglia, Thomas A. and Costante, Gabriele and Valigi, Paolo},
	title = {Weakly Supervised Fruit Counting for Yield Estimation Using Spatial Consistency},
	year = {2019},
	journal = {IEEE ROBOTICS AND AUTOMATION LETTERS},
	volume = {4},
	abstract = {Fruit counting is a fundamental component for yield estimation applications. Most of the existing approaches address this problem by relying on fruit models (i.e., by using object detectors) or by explicitly learning to count. Despite the impressive results achieved by these approaches, all of them need strong supervision information during the training phase. In agricultural applications, manual labeling may require a huge effort or, in some cases, it could be impossible to acquire fine-grained ground truth labels. In this letter, we tackle this problem by proposing a weakly supervised framework that learns to count fruits without the need for task-specific supervision labels. In particular, we devise a novel convolutional neural network architecture that requires only a simple image level binary classifier to detect whether the image contains instances of the fruits or not and combines this information with image spatial consistency constraints. The result is an architecture that learns to count without task-specific labels (e.g., object bounding boxes or the multiplicity of fruit instances in the image). The experiments on three different varieties of fruits (i.e., olives, almonds, and apples) show that our approach reaches performances that are comparable with SotA approaches based on the supervised paradigm.},
	keywords = {Agricultural automation; computer vision for other robotic applications; deep learning in robotics and automation; robotics in agriculture and forestry; visual learning; Control and Systems Engineering; Human-Computer Interaction; Biomedical Engineering; Mechanical Engineering; Control and Optimization; Artificial Intelligence; Computer Science Applications1707 Computer Vision and Pattern Recognition; 1707},
	url = {http://ieeexplore.ieee.org/servlet/opac?punumber=7083369},
	doi = {10.1109/LRA.2019.2903260},
	pages = {2348--2355},
	number = {3}
}
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