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. Matlab projects, Matlab code and Matlab toolbox. Change position select obj in matlab: orthogonal least squares algorithms for sparse signal reconstruction in matlab: in matlab: 2 d fir filter design in matlab: a simple particle filter simulator for robot localization in matlab. Free Source Code Download Icons Free Sound Effects. ![]() 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. Introduction According to [], sourcing skilled farm labour in the agriculture industry (especially horticulture) is one of the most cost-demanding factors in that industry. This is due to the rising values of supplies, such as power, water irrigation, agrochemicals, and so on. This is driving farm enterprises and horticultural industry to be under pressure with small profit margins. Under these challenges, food production still needs to meet the growing demands of an ever-growing world population, and this casts a critical problem to come. Robotic harvesting can provide a potential solution to this problem by reducing the costs of labour (longer endurance and high repeatability) and increasing fruit quality. For these reasons, there has been growing interest in the use of agricultural robots for harvesting fruit and vegetables over the past three decades [,]. The development of such platforms includes numerous challenging tasks, such as manipulation and picking. However, the development of an accurate fruit detection system is a crucial step toward fully-automated harvesting robots, as this is the front-end perception system before subsequent manipulation and grasping systems; if fruit is not detected or seen, it cannot be picked. This step is challenging due to various factors, among which are illumination variation, occlusions, as well as the cases when the fruit exhibits a similar visual appearance to the background, as shown in. To overcome these, a well-generalised model that is invariant and robust to brightness and viewpoint changes and highly discriminative feature representations are required. Example images of the detection for two fruits. ( a) and ( b) show a colour (RGB) and a Near-Infrared (NIR) image of sweet pepper detection denoted as red bounding boxes respectively. ( c) and ( d) are the detection of rock melon. In this work, we present a rapid training (about 2 h on a K40 GPU) and real-time fruit detection system based on Deep Convolutional Neural Networks (DCNN) that can generalise well to various tasks with pre-trained parameters. It can be also easily adapted to different types of fruits with a minimum number of training images. In addition, we introduce approaches that combine multiple modalities of information (colour and near-infrared images) with early and late fusion. For the evaluation, we demonstrate both quantitative and qualitative results compared to previous work []. The contributions of this paper are therefore: •. Returning our findings to the community through open datasets and tutorial documentation []. To the best of our knowledge, this is the first attempt to fuse RGB and NIR multi-modal images within a DCNN framework for fruit detection. We use standard evaluation metrics, precision-recall curves and the F1 score [] (i.e., the harmonic mean of precision and recall), to perform extensive evaluations using data collected from three commercial sites acquired during day and night.
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