Tassel is an important part of the maize plant. The automatic detection and counting of tassels using unmanned aerial vehicle (UAV) imagery can promote the development of intelligent maize planting. However, the actual maize field situation is complex, and the speed and accuracy of the existing algorithms are difficult to meet the needs of real-time detection. To solve this problem, this study constructed a large high-quality maize tassel dataset, which contains information from more than 40,000 tassel images at the tasseling stage. Using YOLOv7 as the original model, a Tassel-YOLO model for the task of maize tassel detection is proposed. Our model adds a global attention mechanism, adopts GSConv convolution and a VoVGSCSP module in the neck part, and improves the loss function to a SIoU loss function. For the tassel detection task, the mAP@0.5 of Tassel-YOLO reaches 96.14%, with an average prediction time of 13.5 ms. Compared with YOLOv7, the model parameters and computation cost are reduced by 4.11 M and 11.4 G, respectively. The counting accuracy has been improved to 97.55%. Experimental results show that the overall performance of Tassel-YOLO is better than other mainstream object detection algorithms. Therefore, Tassel-YOLO represents an effective exploration of the YOLO network architecture, as it satisfactorily meets the requirements of real-time detection and presents a novel solution for maize tassel detection based on UAV aerial images.

To protect birds, it is crucial to identify their species and determine their population across different regions. However, currently, bird monitoring methods mainly rely on manual techniques, such as point counts conducted by researchers and ornithologists in the field. This method can sometimes be inefficient, prone to errors, and have limitations, which may not always be conducive to bird conservation efforts. In this paper, we propose an efficient method for wetland bird monitoring based on object detection and multi-object tracking networks. First, we construct a manually annotated dataset for bird species detection, annotating the entire body and head of each bird separately, comprising 3737 bird images. We also built a new dataset containing 11,139 complete, individual bird images for the multi-object tracking task. Second, we perform comparative experiments using a state-of-the-art batch of object detection networks, and the results demonstrated that the YOLOv7 network, trained with a dataset labeling the entire body of the bird, was the most effective method. To enhance YOLOv7 performance, we added three GAM modules on the head side of the YOLOv7 to minimize information diffusion and amplify global interaction representations and utilized Alpha-IoU loss to achieve more accurate bounding box regression. The experimental results revealed that the improved method offers greater accuracy, with mAP@0.5 improving to 0.951 and mAP@0.5:0.95 improving to 0.815. Then, we send the detection information to DeepSORT for bird tracking and classification counting. Finally, we use the area counting method to count according to the species of birds to obtain information about flock distribution. The method described in this paper effectively addresses the monitoring challenges in bird conservation.

This paper provides a systematic analysis of existing resource platforms, evaluating their advantages and drawbacks with respect to data privacy protection. To address the privacy and security risks associated with resource platform data, we propose a novel privacy protection algorithm based on chunking disorder. Our algorithm exchanges data within a specific range of chunk size for the position and combines the chunked data with the MD5 value in a differential way, thus ensuring data privacy. To ensure the security of the algorithm, we also discuss the importance of preventing client and server decompilation during its implementation. The findings of our experiments are as follows. Our proposed privacy-preserving algorithm is extremely secure and easy to implement. Our algorithm has a significant avalanche effect, maintaining values of 0.61–0.85, with information entropy being maintained at 4.5–4.9. This indicates that our algorithm is highly efficient without compromising data security. Furthermore, our algorithm has strong encryption and decryption time stability. The key length can be up to 594 bits, rendering it challenging to decrypt. Compared with the traditional DES algorithm, our algorithm has better security under the same conditions and approaches the levels of security offered by the AES and RC4 algorithms.