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.

Fast, accurate, and non-destructive large-scale detection of sweet cherry ripeness is the key to determining the optimal harvesting period and accurate grading by ripeness. Due to the complexity and variability of the orchard environment and the multi-scale, obscured, and even overlapping fruit, there are still problems of low detection accuracy even using the mainstream algorithm YOLOX in the absence of a large amount of tagging data. In this paper, we proposed an improved YOLOX target detection algorithm to quickly and accurately detect sweet cherry ripeness categories in complex environments. Firstly, we took a total of 2400 high-resolution images of immature, semi-ripe, and ripe sweet cherries in an orchard in Hanyuan County, Sichuan Province, including complex environments such as sunny days, cloudy days, branch and leaf shading, fruit overlapping, distant views, and similar colors of green fruits and leaves, and formed a dataset dedicated to sweet cherry ripeness detection by manually labeling 36068 samples, named SweetCherry. On this basis, an improved YOLOX target detection algorithm YOLOX-EIoU-CBAM was proposed, which embedded the Convolutional Block Attention Module (CBAM) between the backbone and neck of the YOLOX model to improve the model’s attention to different channels, spaces capability, and replaced the original bounding box loss function of the YOLOX model with Efficient IoU (EIoU) loss to make the regression of the prediction box more accurate. Finally, we validated the feasibility and reliability of the YOLOX-EIoU-CBAM network on the SweetCherry dataset. The experimental results showed that the method in this paper significantly outperforms the traditional Faster R-CNN and SSD300 algorithms in terms of mean Average Precision (mAP), recall, model size, and single-image inference time. Compared with the YOLOX model, the mAP of this method is improved by 4.12%, recall is improved by 4.6%, F-score is improved by 2.34%, while model size and single-image inference time remain basically comparable. The method in this paper can cope well with complex backgrounds such as fruit overlap, branch and leaf occlusion, and can provide a data base and technical reference for other similar target detection problems.

Citrus pests pose a major threat to both citrus yield and fruit quality. The early prevention of pests is essential for sustainable citrus cultivation, cost savings, and the reduction of environmental pollution. Despite the increasing application of deep learning techniques in agriculture, the performance of existing models for small target detection of citrus pests is limited, mainly in terms of information bottlenecks that occur during the transfer of information. This hinders its effectiveness in fully automating the detection of citrus pests. In this study, a new approach was introduced to overcome these limitations. Firstly, a comprehensive large-scale dataset named IP-CitrusPests13 was developed, encompassing 13 distinct citrus pest categories. This dataset was amalgamated from IP102 and web crawlers, serving as a fundamental resource for precision-oriented pest detection tasks in citrus farming. Web crawlers can supplement information on various forms of pests and changes in pest size. Using this comprehensive dataset, we employed the SPD Module in the backbone network to preserve fine-grained information and prevent the model from losing important information as the depth increased. In addition, we introduced the AFFD Head detection module into the YOLOv8 architecture, which has two important functions that effectively integrate shallow and deep information to improve the learning ability of the model. Optimizing the bounding box loss function to WIoU v3 (Wise-IoU v3), which focuses on medium-quality anchor frames, sped up the convergence of the network. Experimental evaluation on a test set showed that the proposed SAW-YOLO (SPD Module, AFFD, WIoU v3) model achieved an average accuracy of 90.3%, which is 3.3% higher than the benchmark YOLOv8n model. Without any significant enlargement in the model size, state-of-the-art (SOTA) performance can be achieved in small target detection. To validate the robustness of the model against pests of various sizes, the SAW-YOLO model showed improved detection performance on all three scales of pests, significantly reducing the rate of missed detections. Our experimental results show that the SAW-YOLO model performs well in the detection of multiple pest classes in citrus orchards, helping to advance smart planting practices in the citrus industry.

Disease diagnosis and control play important roles in agriculture and crop protection. Traditional methods of identifying plant disease rely primarily on human vision and manual inspection, which are subjective, have low accuracy, and make it difficult to estimate the situation in real time. At present, an intelligent detection technology based on computer vision is becoming an increasingly important tool used to monitor and control crop disease. However, the use of this technology often requires the collection of a substantial amount of specialized data in advance. Due to the seasonality and uncertainty of many crop pathogeneses, as well as some rare diseases or rare species, such data requirements are difficult to meet, leading to difficulties in achieving high levels of detection accuracy. Here, we use kiwifruit trunk bacterial canker (Pseudomonas syringae pv. actinidiae) as an example and propose a high-precision detection method to address the issue mentioned above. We introduce a lightweight and efficient image generative model capable of generating realistic and diverse images of kiwifruit trunk disease and expanding the original dataset. We also utilize the YOLOv8 model to perform disease detection; this model demonstrates real-time detection capability, taking only 0.01 s per image. The specific contributions of this study are as follows: (1) a depth-wise separable convolution is utilized to replace part of ordinary convolutions and introduce noise to improve the diversity of the generated images; (2) we propose the GASLE module by embedding a GAM, adjust the importance of different channels, and reduce the loss of spatial information; (3) we use an AdaMod optimizer to increase the convergence of the network; and (4) we select a real-time YOLOv8 model to perform effect verification. The results of this experiment show that the Fréchet Inception Distance (FID) of the proposed generative model reaches 84.18, having a decrease of 41.23 compared to FastGAN and a decrease of 2.1 compared to ProjectedGAN. The mean Average Precision (mAP@0.5) on the YOLOv8 network reaches 87.17%, which is nearly 17% higher than that of the original algorithm. These results substantiate the effectiveness of our generative model, providing a robust strategy for image generation and disease detection in plant kingdoms.