In order to enable the picking robot to detect and locate apples quickly and accurately in the orchard natural environment, we propose an apple object detection method based on Shufflenetv2-YOLOX. This method takes YOLOX-Tiny as the baseline and uses the lightweight network Shufflenetv2 added with the convolutional block attention module (CBAM) as the backbone. An adaptive spatial feature fusion (ASFF) module is added to the PANet network to improve the detection accuracy, and only two extraction layers are used to simplify the network structure. The average precision (AP), precision, recall, and F1 of the trained network under the verification set are 96.76%, 95.62%, 93.75%, and 0.95, respectively, and the detection speed reaches 65 frames per second (FPS). The test results show that the AP value of Shufflenetv2-YOLOX is increased by 6.24% compared with YOLOX-Tiny, and the detection speed is increased by 18%. At the same time, it has a better detection effect and speed than the advanced lightweight networks YOLOv5-s, Efficientdet-d0, YOLOv4-Tiny, and Mobilenet-YOLOv4-Lite. Meanwhile, the half-precision floating-point (FP16) accuracy model on the embedded device Jetson Nano with TensorRT acceleration can reach 26.3 FPS. This method can provide an effective solution for the vision system of the apple picking robot.

/pdf/a-real-time-apple-targets-detection-method-for-picking-robot-ualqaxnc.pdf

A Real-Time Apple Targets Detection Method for Picking Robot Based on ShufflenetV2-YOLOX

Fast and accurate green pepper detection in complex backgrounds via an improved Yolov4-tiny model

Real-time recognition system of soybean seed full-surface defects based on deep learning

Apple grading is an essential part of the apple marketing process to achieve high profits. In this paper, an improved YOLOv5 apple grading method is proposed to address the problems of low grading accuracy and slow grading speed in the apple grading process and is experimentally verified by the designed automatic apple grading machine. Firstly, the Mish activation function is used instead of the original YOLOv5 activation function, which allows the apple feature information to flow in the deep network and improves the generalization ability of the model. Secondly, the distance intersection overUnion loss function (DIoU_Loss) is used to speed up the border regression rate and improve the model convergence speed. In order to refine the model to focus on apple feature information, a channel attention module (Squeeze Excitation) was added to the YOLOv5 backbone network to enhance information propagation between features and improve the model’s ability to extract fruit features. The experimental results show that the improved YOLOv5 algorithm achieves an average accuracy of 90.6% for apple grading under the test set, which is 14.8%, 11.1%, and 3.7% better than the SSD, YOLOv4, and YOLOv5s models, respectively, with a real-time grading frame rate of 59.63 FPS. Finally, the improved YOLOv5 apple grading algorithm is experimentally validated on the developed apple auto-grader. The improved YOLOv5 apple grading algorithm was experimentally validated on the developed apple auto grader. The experimental results showed that the grading accuracy of the automatic apple grader reached 93%, and the grading speed was four apples/sec, indicating that this method has a high grading speed and accuracy for apples, which is of practical significance for advancing the development of automatic apple grading.

/pdf/apple-grading-method-design-and-implementation-for-automatic-1qailh5e.pdf

Apple Grading Method Design and Implementation for Automatic Grader Based on Improved YOLOv5

Design of a lightweight robotic arm for kiwifruit pollination

Target recognition method of green pepper harvesting robot based on manifold ranking

Apple target recognition method in complex environment based on improved YOLOv4

A novel and ultrasensitive fluorescent probe derived from labeled carbon dots for recognitions of copper ions and glyphosate.

One step synthesis of ultra-high quantum yield fluorescent carbon dots for “on-off-on” detection of Hg2+ and biothiols

Dual-emission carbon dots with strong yellow fluorescence (Y-CDs) were prepared by one-step hydrothermal method from p-aminophenol and ethylenediamine. Two emission centres appeared at 380 nm and 520 nm under excitation of 270 nm. After adding Al3+, the fluorescence at 520 nm decreased due to static quenching, while the fluorescence at 380 nm gradually increased. Therefore, it could be used for ratiometric detection of Al3+. In addition, the synthesized Y-CDs were concentration dependent, and the occurrence of dual emission peaks was considerably related to the concentration. When the concentration of Y-CDs decreased, the intensity of its emission peak at 380 nm gradually increased. There was an excellent linear relationship between the concentration of synthesized Y-CDs and the ratio of two emission peaks after adding Al3+, which had been used for the detection of Al3+ in actual water samples. Moreover, the synthesized Y-CDs were successfully applied to A549 cell imaging. 

Hydrothermal synthesis of dual emission carbon dots with yellow fluorescence for ratio detection of Al3+

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