This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from $0.807$ to $0.838$ for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.

We propose a novel approach for training deep convolutional neural networks (DCNNs) that allows us to tradeoff complexity and accuracy to learn lightweight models suitable for robotic platforms such as AgBot II (which performs automated weed management). Our approach consists of three stages, the first is to adapt a pre-trained model to the task at hand. This provides state-of-the-art performance but at the cost of high computational complexity resulting in a low frame rate of just 0.12 frames per second (fps). Second, we use the adapted model and employ model compression techniques to learn a lightweight DCNN that is less accurate but has two orders of magnitude fewer parameters. Third, K lightweight models are combined as a mixture model to further enhance the performance of the lightweight models. Applied to the challenging task of weed segmentation, we improve the accuracy from 85.9%, using a traditional approach, to 93.9% by adapting a complicated pre-trained DCNN with 25M parameters (Inception-v3). The downside to this adapted model, Adapted-IV3, is that it can only process 0.12 fps. To make this approach fast while still retaining accuracy, we learn lightweight DCNNs which when combined can achieve accuracy greater than 90% while using considerably fewer parameters capable of processing between 1.07 and 1.83 fps, up to an order of magnitude faster and up to an order of magnitude fewer parameters.

This letter presents a three-dimensional (3-D) visual detection method for the challenging task of detecting peduncles of sweet peppers (Capsicum annuum) in the field. Cutting the peduncle cleanly is one of the most difficult stages of the harvesting process, where the peduncle is the part of the crop that attaches it to the main stem of the plant. Accurate peduncle detection in 3-D space is, therefore, a vital step in reliable autonomous harvesting of sweet peppers, as this can lead to precise cutting while avoiding damage to the surrounding plant. This letter makes use of both color and geometry information acquired from an RGB-D sensor and utilizes a supervised-learning approach for the peduncle detection task. The performance of the proposed method is demonstrated and evaluated by using qualitative and quantitative results [the area-under-the-curve (AUC) of the detection precision-recall curve]. We are able to achieve an AUC of 0.71 for peduncle detection on field-grown sweet peppers. We release a set of manually annotated 3-D sweet pepper and peduncle images to assist the research community in performing further research on this topic.

Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (Conv Net) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that in ideal imaging conditions, combining traditional and Conv Net features yields state-of-the art performance with an average accuracy of 97.3%±0:6% compared to traditional features which obtain an average accuracy of 91.2%±1:6%. Further experiments show that this combined classification approach consistently outperforms the best set of traditional features by an average of 5.7% for all of the evaluated condition variations.