Intelligent agriculture imposes higher requirements on the recognition and localization of fruit and vegetable picking robots. Due to its unique visual information and relatively low hardware cost, machine vision is widely applied in the recognition and localization of fruit and vegetable picking robots. This article provides an overview of the application of machine vision in the recognition and localization of fruit and vegetable picking robots. Firstly, the advantages, disadvantages, and the roles of different visual sensors and machine vision algorithms in the recognition and localization of fruit and vegetable picking robots are introduced, including monocular cameras, stereo cameras, structured light cameras, multispectral cameras, image segmentation algorithms, object detection algorithms, and 3D reconstruction algorithms. Then, the current status and challenges faced by machine vision in the recognition and localization of fruit and vegetable picking robots are summarized. These challenges include the stability of fast recognition under complex background interference, stability of recognition under different lighting environments for the same crop, the reliance of recognition and localization on prior information in the presence of fruit overlap and occlusions caused by leaves and branches, and the uncertainty of picking caused by complex working environments. In current research on algorithms dealing with complex background interference and various occlusion disturbances, good results have been achieved. Different lighting environments have a significant impact on the recognition and positioning of fruits and vegetables, with a minimum accuracy of 59.2%. Finally, this article outlines future research directions to address these challenges.

The typical occlusion of cherry tomatoes in the natural environment is one of the most critical factors affecting the accurate picking of cherry tomato picking robots. To recognize occluded cherry tomatoes accurately and efficiently using deep convolutional neural networks, a new occluded cherry tomato recognition model DSP-YOLOv7-CA is proposed. Firstly, images of cherry tomatoes with different degrees of occlusion are acquired, four occlusion areas and four occlusion methods are defined, and a cherry tomato dataset (TOSL) is constructed. Then, based on YOLOv7, the convolution module of the original residual edges was replaced with null residual edges, depth-separable convolutional layers were added, and jump connections were added to reuse feature information. Then, a depth-separable convolutional layer is added to the SPPF module with fewer parameters to replace the original SPPCSPC module to solve the problem of loss of small target information by different pooled residual layers. Finally, a coordinate attention mechanism (CA) layer is introduced at the critical position of the enhanced feature extraction network to strengthen the attention to the occluded cherry tomato. The experimental results show that the DSP-YOLOv7-CA model outperforms other target detection models, with an average detection accuracy (mAP) of 98.86%, and the number of model parameters is reduced from 37.62MB to 33.71MB, which is better on the actual detection of cherry tomatoes with less than 95% occlusion. Relatively average results were obtained on detecting cherry tomatoes with a shade level higher than 95%, but such cherry tomatoes were not targeted for picking. The DSP-YOLOv7-CA model can accurately recognize the occluded cherry tomatoes in the natural environment, providing an effective solution for accurately picking cherry tomato picking robots.

Lane detection plays an important role in driving safety, so it is necessary to explore more practical lane detection methods. Nowadays, with the development of deep learning, mainstream methods regard the lane detection task as a pixel-level image segmentation task. Although the powerful neural network can greatly improve the detection accuracy, it also brings a large computational cost and reduces the running speed. Therefore, achieving high-precision and high-speed lane detection is still challenging. In this paper, we design an efficient and fast sparse prediction method. Specifically, our method describes the lane line by predicting the location of discrete lane points, so the calculation magnitude is the number of discrete points. This method greatly reduces the calculation cost and is not limited by the number of lane lines. The network structure uses an Encoder–Decoder architecture, and the large receptive field obtained by the structure after downsampling can maintain the spatial continuity between lane points, thus ensuring high accuracy. In addition, self-attention distillation is used to improve the learning ability of the network. Compared with other state-of-the-art methods on the TuSimple and CULane datasets, sparse prediction method achieves high accuracy with obvious speed advantage.

The advancement of precision agriculture and smart tillage relies on high-precision, real-time perception of unmanned ground vehicle (UGV) positions. In large-scale farmland operations, conventional Global Navigation Satellite System (GNSS)-based positioning may suffer from short-term signal loss, degrading accuracy. Multi-unmanned aerial vehicle (UAV)-assisted vehicle localization based on direction-of-arrival (DOA) estimation can provide critical positioning compensation for UGV, where compressed sensing (CS)-based DOA-assisted localization algorithms are commonly employed. However, existing schemes neither account for the bias induced by the local positional oscillation of UAVs, nor address the limited accuracy and real-time performance of CS-based DOA estimation, restricting their agricultural deployment. To this end, this paper first develops an assisted-localization architecture that explicitly incorporates the local positional offsets of multiple UAVs, together with a corresponding array signal reception model. To overcome the accuracy–efficiency trade-off of conventional CS-DOA methods, an adaptive local overcomplete dictionary (LOD) is then constructed to robustly refine the angular resolution around the region of interest. With the number of sources K assumed to be known and fixed, a particle swarm optimization (PSO)-based local refinement algorithm is further introduced to adaptively optimize the DOA estimates within the constructed local dictionary, thereby improving estimation robustness under low-SNR and coherent-source conditions. Consequently, the proposed method improves robustness while maintaining favorable localization accuracy and computational efficiency in the simulated scenarios. Simulation results show that it substantially reduces localization error compared with state-of-the-art algorithms, suggesting its potential as a localization-assistance approach for UGV navigation in sustainable tillage.

Feng Dai was removed from the list of authors in the original publication