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The demand for intelligent agriculture is increasing due to the continuous impact of world food and environmental crises. Focusing on fruit detection, with the rapid development of object detection technology, it is now possible to achieve high efficiency and high accuracy in fruit detection systems. However, detecting fruit with high precision in unstructured orchard environments remains particularly challenging. Such environments, which are composed of varying illumination conditions and degrees of occlusion, can be mitigated by certain strategies. To our knowledge, this is the first time that optimization strategies used in fruit detection have been reviewed. This review aims to explore methods for improving fruit detection in complex environments. First, we describe the common types of complex backgrounds found in outdoor orchard environments. Subsequently, we divide the improvement measures into two categories: optimization before and after image sampling. Next, we compare the test results obtained before and after the application of these improved methods. Finally, we describe the future development trends of fruit detection optimization technology in complex backgrounds. We hope that this review will inspire researchers to design their optimization strategies and help explore lower-cost and more robust fruit detection systems. [ABSTRACT FROM AUTHOR]

The dynamic increase factor (DIF) of the concrete material strength, obtained using a split Hopkinson pressure bar (SHPB), includes structural effects that do not precisely reflect the real strain-rate effect of concrete. To further clarify the real strain-rate effects of rubberised concrete (RC), an experimental investigation regarding the dynamic compressive response of ordinary concrete (NC) and RC with three rubber contents (10%, 20%, and 30%) was performed in this study. Additionally, based on a dynamic constitutive model, i.e., the Karagozian and Case (K&C) concrete model, numerical SHPB tests were conducted using the LS-DYNA software. According to the experimental results, all parameters of the K&C model were discussed, and the damage factors were modified to satisfy the mechanical properties of RC. After validating the numerical model, it was observed that the experimental DIF included the inertial enhancement and the real DIF. Moreover, because rubber particles effectively reduce the density and improve the deformation capacity of concrete, the real strain-rate effect of RC was found to be more rate-sensitive than that of NC by analysing the radial stress distribution. In addition to lateral inertia, another external source, namely, the interface friction between the specimen and bars, which can produce lateral confinement, was further studied. It was found that interface friction significantly contributes to lateral confinement; however, as the strain rate increased, the impact generally decreased. Finally, the mechanism of the strain-rate effect of RC was clarified. [ABSTRACT FROM AUTHOR]

This paper proposes a novel technique for fruit detection in natural environments which is applicable in automatic harvesting robots, yield estimation systems and quality monitoring systems. As most color-based techniques are highly sensitive to illumination changes and low contrasts between fruits and leaves, the proposed technique, conversely, is based on contour information. Firstly, a discriminative shape descriptor is derived to represent geometrical properties of arbitrary fragment, and applied to a bidirectional partial shape matching to detect sub-fragments of interest that match parts of a reference contour. Then, a novel probabilistic Hough transform is developed to aggregate these sub-fragments for obtaining fruit candidates. Finally, all fruit candidates are verified by a support vector machine classifier trained on color and texture features. Citrus, tomato, pumpkin, bitter gourd, towel gourd and mango datasets were provided. Experiments on these datasets demonstrated that the proposed approach was competitive for detecting most type of fruits, such as green, orange, circular and non-circular, in natural environments. [ABSTRACT FROM AUTHOR]

A novel detection algorithm based on color, depth, and shape information is proposed for detecting spherical or cylindrical fruits on plants in natural environments and thus guiding harvesting robots to pick them automatically. A probabilistic image segmentation method is first presented to segment a red–green–blue image as a binary mask. Multiplied by this mask, a filtered depth image is obtained. Region growing, a region-based image segmentation method, is then applied to group the depth image into multiple clusters. Each cluster represents a fruit, leaf, or branch that is later transformed into a point cloud. Next, a 3D shape detection method based on M-estimator sample consensus, a model parameter estimator, is employed to detect potential fruits from each point cloud. Finally, an angle/color/shape-based global point cloud descriptor (GPCD) is developed to extract a feature vector for an entire point cloud, and a support vector machine classifier trained on the GPCD features is used to exclude false positives. Pepper, eggplant, and guava datasets were captured in the field. For the pepper, eggplant, and guava datasets, the detection precision was 0.864, 0.886, and 0.888, and the recall was 0.889, 0.762, and 0.812, respectively. Experiments revealed that the proposed algorithm was universal and robust and hence applicable to an agricultural harvesting robot. [ABSTRACT FROM AUTHOR]