Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards
Suchet Bargoti,James Underwood +1 more
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In this paper, a general-purpose image segmentation approach is used, including two feature learning algorithms; multiscale multilayered perceptrons (MLP) and convolutional neural networks (CNN).Abstract:
Ground vehicles equipped with monocular vision systems are a valuable source of high-resolution image data for precision agriculture applications in orchards This paper presents an image processing framework for fruit detection and counting using orchard image data A general-purpose image segmentation approach is used, including two feature learning algorithms; multiscale multilayered perceptrons (MLP) and convolutional neural networks (CNN) These networks were extended by including contextual information about how the image data was captured (metadata), which correlates with some of the appearance variations and/or class distributions observed in the data The pixel-wise fruit segmentation output is processed using the watershed segmentation (WS) and circular Hough transform (CHT) algorithms to detect and count individual fruits Experiments were conducted in a commercial apple orchard near Melbourne, Australia The results show an improvement in fruit segmentation performance with the inclusion of metadata on the previously benchmarked MLP network We extend this work with CNNs, bringing agrovision closer to the state-of-the-art in computer vision, where although metadata had negligible influence, the best pixel-wise F1-score of 0791 was achieved The WS algorithm produced the best apple detection and counting results, with a detection F1-score of 0861 As a final step, image fruit counts were accumulated over multiple rows at the orchard and compared against the post-harvest fruit counts that were obtained from a grading and counting machine The count estimates using CNN and WS resulted in the best performance for this data set, with a squared correlation coefficient of r2=0826read more
Citations
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Crop yield prediction using machine learning: A systematic literature review
TL;DR: This study performed a Systematic Literature Review to extract and synthesize the algorithms and features that have been used in crop yield prediction studies, and found Convolutional Neural Networks is the most widely used deep learning algorithm in these studies.
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Deep learning – Method overview and review of use for fruit detection and yield estimation
TL;DR: A review of developments in the rapidly developing field of deep learning is presented, with emphasis on practical aspects for application of deeplearning models for the task of fruit detection and localisation, in support of tree crop load estimation.
Proceedings ArticleDOI
Deep fruit detection in orchards
Suchet Bargoti,James Underwood +1 more
TL;DR: In this article, 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, was presented.
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Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘MangoYOLO’
TL;DR: The performance of six existing deep learning architectures were compared for the task of detection of mango fruit in images of tree canopies and a new architecture was developed, termed ‘MangoYOLO’, which outperformed other models in processing of full images, requiring just 70 ms per image.
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Faster R-CNN for multi-class fruit detection using a robotic vision system
Shaohua Wan,Sotirios K. Goudos +1 more
TL;DR: This work is a pioneer to create a multi-labeled and knowledge-based outdoor orchard image library using 4000 images in the real world and improvement of the convolutional and pooling layers is achieved to have a more accurate and faster detection.
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