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Suchet Bargoti

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2 papers

1 author row

ICRA Conference 2017 Conference Paper

Deep fruit detection in orchards

Suchet Bargoti
James Patrick Underwood

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.

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ICRA Conference 2016 Conference Paper

Image classification with orchard metadata

Suchet Bargoti
James Patrick Underwood

Low cost and easy to use monocular vision systems are able to capture large scale, dense data in orchards, to facilitate precision agriculture applications. Accurate image parsing is required for this purpose, however, operating in natural outdoor conditions makes this a complex task due to the undesirable intra-class variations caused by changes in illumination, pose and tree types, etc. Typically these variations are difficult to explicitly model and discriminative classifiers strive to be invariant to them. However, given the presence of structure, in both the orchard and how the data was obtained, a subset of these factors of variations can correlate with readily available metadata, including extrinsic experimental information such as the sun incidence angle, position within farm, etc. This paper presents a method to incorporate such metadata to aid scene parsing based on a multi-scale Multi-Layered Perceptron (MLP) architecture. Experimental results are shown for pixel segmentation over data collected at an apple orchard, leading to fruit detection and yield estimation. The results show a consistent improvement in segmentation accuracy with the inclusion of metadata under different network complexities, training configurations and evaluation metrics.

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