The density of a chicken population has a great influence on the health and growth of the chickens. For free-range chicken producers, an appropriate population density can increase their economic benefit and be utilized for estimating the economic value of the flock. However, it is very difficult to calculate the density of chickens quickly and accurately because of the complicated environmental background and the dynamic number of chickens. Therefore, we propose an automated method for quickly and accurately counting the number of chickens on a chicken farm, rather than doing so manually. The contributions of this paper are twofold: (1) we innovatively designed a full convolutional network—DenseFCN—and counted the chickens in an image using the method of point supervision, which achieved an accuracy of 93.84% and 9.27 frames per second (FPS); (2) the point supervision method was used to detect the density of chickens. Compared with the current mainstream object detection method, the higher effectiveness of this method was proven. From the performance evaluation of the algorithm, the proposed method is practical for measuring the density statistics of chickens in a farm environment and provides a new feasible tool for the density estimation of farm poultry breeding.

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.

The gender ratio of free-range chickens is considered as a major animal welfare problem in commercial broiler farming. Free-range chicken producers need to identify chicken gender to estimate the economic value of their flock. However, it is challenging for farmers to estimate the gender ratio of chickens efficiently and accurately, since the environmental background is complicated and the chicken number is dynamic. Moreover, manual estimation is likely double counts or missed count and thus is inaccurate and time consuming. Hence, automated methods that can lead to results efficiently and accurately replace the identification abilities of a chicken gender expert, working in a farm environment, are beneficial to the industry. The contributions in this paper include: (1) Building the world’s first chicken gender classification database annotated manually, which comprises 800 chicken flock images captured on a farm and 1000 single chicken images separated from the flock images by an object detection network, labelled with gender information. (2) Training a rooster and hen classifier using a deep neural network and cross entropy in information theory to achieve an average accuracy of 96.85%. The evaluation of the algorithm performance indicates that the proposed automated method is practical for the gender classification of chickens on the farm environment and provides a feasible way of thinking for the estimation of the gender ratio.

Extracting water bodies from remote sensing images is important in many fields, such as in water resources information acquisition and analysis. Conventional methods of water body extraction enhance the differences between water bodies and other interfering water bodies to improve the accuracy of water body boundary extraction. Multiple methods must be used alternately to extract water body boundaries more accurately. Water body extraction methods combined with neural networks struggle to improve the extraction accuracy of fine water bodies while ensuring an overall extraction effect. In this study, false color processing and a generative adversarial network (GAN) were added to reconstruct remote sensing images and enhance the features of tiny water bodies. In addition, a multi-scale input strategy was designed to reduce the training cost. We input the processed data into a new water body extraction method based on strip pooling for remote sensing images, which is an improvement of DeepLabv3+. Strip pooling was introduced in the DeepLabv3+ network to better extract water bodies with a discrete distribution at long distances using different strip kernels. The experiments and tests show that the proposed method can improve the accuracy of water body extraction and is effective in fine water body extraction. Compared with seven other traditional remote sensing water body extraction methods and deep learning semantic segmentation methods, the prediction accuracy of the proposed method reaches 94.72%. In summary, the proposed method performs water body extraction better than existing methods.

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.

Introduction: Zanthoxylum bungeanum Maxim is an economically significant crop in Asia, but large-scale cultivation is often threatened by frequent diseases, leading to significant yield declines. Deep learning-based methods for crop disease recognition have emerged as a vital research area in agriculture.Methods: This paper presents a novel model, LT-DeepLab, for the semantic segmentation of leaf spot (folium macula), rust, frost damage (gelu damnum), and diseased leaves and trunks in complex field environments. The proposed model enhances DeepLabV3+ with an innovative Fission Depth Separable with CRCC Atrous Spatial Pyramid Pooling module, which reduces the structural parameters of Atrous Spatial Pyramid Pooling module and improves cross-scale extraction capability. Incorporating Criss-Cross Attention with the Convolutional Block Attention Module provides a complementary boost to channel feature extraction. Additionally, deformable convolution enhances low-dimensional features, and a Fully Convolutional Network auxiliary header is integrated to optimize the network and enhance model accuracy without increasing parameter count.Results: LT-DeepLab improves the mean Intersection over Union (mIoU) by 3.59%, the mean Pixel Accuracy (mPA) by 2.16%, and the Overall Accuracy (OA) by 0.94% compared to the baseline DeepLabV3+. It also reduces computational demands by 11.11% and decreases the parameter count by 16.82%.Discussion: These results indicate that LT-DeepLab demonstrates excellent disease segmentation capabilities in complex field environments while maintaining high computational efficiency, offering a promising solution for improving crop disease management efficiency.