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Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

Maize is a crop that is widely cultivated all over the world. Thus, the classification of maize seeds quality is important, while the traditional methods based on the texture, shape, and color which require repeated work is not efficient. Recently, deep learning reached the goal in the field of image processing, and a deep convolutional neural network (DCNN) is often used to do the image classification task. Here, we explored another neural network called Vision Transformer (ViT), which originally was applied to the natural language processing. Based on the self‐attention mechanism, ViT discards the convolutional structure. But when trained from scratch on medium‐sized datasets, ViT performed poorly compared to CNN. Due to the lack of local structure within the input image, tokenization cannot be used to generate a valid training set in the original ViT model. As a result, we proposed an improved ViT model SeedViT. Compared with the original ViT which could only train large datasets, SeedViT can train small and medium datasets to achieve SOTA (State of the Art) in vision classification with only 2,500 images in our study. The feasibility of SeedViT to classify maize seeds’ quality was studied in this article, and we compared it with DCNN and traditional machine learning algorithms. The accuracy, sensitivity, specificity, and precision were 97.6%, 94.1%, 98.9%, and 97%, respectively. In addition, we employed ViT and VGG (Visual Geometry Group, a convolutional neural network) to extract image features, and SVM (support vector machine) was used as the classifier to classify them, with the result that ViT‐SVM was stable around 96.6% on the test set and VGG‐SVM was stable around 94.6%. At last, a visual attention map was generated by visualization technology. It showed that SeedViT can be a new and novel way for maize seed manufacturing. Practical applications An algorithm for classifying and sorting out high‐quality maize seeds. Use the Transformer algorithm from the field of natural language processing instead of the convolutional neural network algorithm. Use GeLU function for activation and soft split with images to improve Vision Transformer model so that it can achieve great performance with small and medium datasets.

Deep learning has been widely used in removing image noise, but the blind denoising problem in agriculture limits the accuracy of citrus recognition by picking robots, and most denoising methods do not preserve the texture details of the image well. To solve these problems, this study proposes a multi‐module image denoising method based on feature pyramid network. The algorithm is based on the pyramid network structure, adding modules such as attention mechanism, dilated convolution, and feature fusion to denoise images. The attention mechanism and dilated convolution enhance the extraction of noisy features. The feature fusion module fuses the feature maps of each layer to solve the problem of incomplete image detail texture preservation. The experimental results of this algorithm on different datasets show that the performance of our method on various datasets with different noise levels is superior, and it is solving the problem of real image denoising. It is effective to perform both on and save the image texture details, which proves the practicability of the method in this study. After using this method to remove the real noise in the image, the average score of citrus detection accuracy is 52.4% higher than that before denoising. Practical Applications Citrus picking is a labor‐intensive work. Traditionally, citrus picking depends on manpower. This harvesting method has high cost and low efficiency, which seriously hinders the development of citrus industry. In this study, the pyramid network structure is used to denoise the citrus image by adding modules such as attention mechanism, dilated convolution, and feature fusion, so as to guide the robot to recognize the citrus and improve the production efficiency. It is of great value to the recognition technology of industrial citrus picking robot.