Maize leaf disease detection is an essential project in the maize planting stage. This paper proposes the convolutional neural network optimized by a Multi-Activation Function (MAF) module to detect maize leaf disease, aiming to increase the accuracy of traditional artificial intelligence methods. Since the disease dataset was insufficient, this paper adopts image pre-processing methods to extend and augment the disease samples. This paper uses transfer learning and warm-up method to accelerate the training. As a result, three kinds of maize diseases, including maculopathy, rust, and blight, could be detected efficiently and accurately. The accuracy of the proposed method in the validation set reached 97.41%. This paper carried out a baseline test to verify the effectiveness of the proposed method. First, three groups of CNNs with the best performance were selected. Then, ablation experiments were conducted on five CNNs. The results indicated that the performances of CNNs have been improved by adding the MAF module. In addition, the combination of Sigmoid, ReLU, and Mish showed the best performance on ResNet50. The accuracy can be improved by 2.33%, proving that the model proposed in this paper can be well applied to agricultural production.

There has been substantial progress in small object detection in aerial images in recent years, due to the extensive applications and improved performances of convolutional neural networks (CNNs). Typically, traditional machine learning algorithms tend to prioritize inference speed over accuracy. Insufficient samples can cause problems for convolutional neural networks, such as instability, non-convergence, and overfitting. Additionally, detecting aerial images has inherent challenges, such as varying altitudes and illuminance situations, and blurred and dense objects, resulting in low detection accuracy. As a result, this paper adds a transformer backbone attention mechanism as a branch network, using the region-wide feature information. This paper also employs a generative model to expand the input aerial images ahead of the backbone. The respective advantages of the generative model and transformer network are incorporated. On the dataset presented in this study, the model achieves 96.77% precision, 98.83% recall, and 97.91% $mAP$ by adding the Multi-GANs module to the one-stage detection network. These three indices are enhanced by 13.9%, 20.54%, and 10.27%, respectively, when compared to the other detection networks. Furthermore, this study provides an auto-pruning technique that may achieve 32.2 FPS inference speed with a minor performance loss while responding to the real-time detection task’s usage environment. This research also develops a macOS application for the proposed algorithm using Swift development technology.

Protecting wheat yield is a top priority in agricultural production, and one of the important measures to preserve yield is the control of wheat diseases. With the maturity of computer vision technology, more possibilities have been provided to achieve plant disease detection. In this study, we propose the position attention block, which can effectively extract the position information from the feature map and construct the attention map to improve the feature extraction ability of the model for the region of interest. For training, we use transfer learning to improve the training speed of the model. In the experiment, ResNet built on positional attention blocks achieves 96.4% accuracy, which is much higher compared to other comparable models. Afterward, we optimized the undesirable detection class and validated its generalization performance on an open-source dataset.

Farming areas are made up of diverse land use types, such as arable lands, grasslands, woodlands, water bodies, and other surrounding agricultural architectures. They possess imperative economic value, and are considerably valued in terms of farmers’ livelihoods and society’s flourishment. Meanwhile, detecting crops in farming areas, such as wheat and corn, allows for more direct monitoring of farming area production and is significant for practical production and management. However, existing image segmentation methods are relatively homogeneous, with insufficient ability to segment multiple objects around the agricultural environment and small-scale objects such as corn and wheat. Motivated by these issues, this paper proposed a global-transformer segmentation network based on the morphological correction method. In addition, we applied the dilated convolution technique to the backbone of the model and the transformer technique to the branches. This innovation of integrating the above-mentioned techniques has an active impact on the segmentation of small-scale objects. Subsequently, the backbone improved by this method was applied to an object detection network based on a corn and wheat ears dataset. Experimental results reveal that our model can effectively detect wheat ears in a complicated environment. For two particular segmentation objects in farming areas, namely water bodies and roads, we notably proposed a morphological correction method, which effectively reduces the number of connected domains in the segmentation results with different parameters of dilation and erosion operations. The segmentation results of water bodies and roads were thereby improved. The proposed method achieved 0.903 and 13 for $mIoU$ and continuity. This result reveals a remarkable improvement compared with the comparison model, and the continuity has risen by 408%. These comparative results demonstrate that the proposed method is eminent and robust enough to provide preliminary preparations and viable strategies for managing farming area resources and detecting crops.

Grasslands, as an important part of terrestrial ecosystems, are facing serious threats of land degradation. Therefore, the remote monitoring of grasslands is an important tool to control degradation and protect grasslands. However, the existing methods are often disturbed by clouds and fog, which makes it difficult to achieve all-weather and all-time grassland remote sensing monitoring. Synthetic aperture radar (SAR) data can penetrate clouds, which is helpful for solving this problem. In this study, we verified the advantages of the fusion of multi-spectral (MS) and SAR data for improving classification accuracy, especially for cloud-covered areas. We also proposed an adaptive feature fusion method (the SK-like method) based on an attention mechanism, and tested two types of patch construction strategies, single-size and multi-size patches. Experiments have shown that the proposed SK-like method with single-size patches obtains the best results, with 93.12% accuracy and a 0.91 average f1-score, which is a 1.02% accuracy improvement and a 0.01 average f1-score improvement compared with the commonly used feature concatenation method. Our results show that the all-weather, all-time remote sensing monitoring of grassland is possible through the fusion of MS and SAR data with suitable feature fusion methods, which will effectively enhance the regulatory capability of grassland resources.

The rapid advancement of intelligent livestock farming and precision breeding has underscored the importance of non-contact body measurement and weight estimation. Based on 3D reconstruction, these techniques represent a critical pathway for the digital transformation of animal husbandry. However, there are no publicly available 3D point cloud datasets specific to Jining Qing Goats, particularly systematic data covering key developmental stages. To bridge this gap, we present a comprehensive dataset comprising multi-view 3D point clouds and standardized morphometric records of Jining Qing Goats. The dataset spans multiple age groups and emphasizes critical phases such as juvenile, growing, mature, and reproductive stages, thereby capturing a holistic representation of the breed’s life cycle. During data acquisition, two Microsoft Kinect DK depth cameras were positioned bilaterally to capture RGB and depth images simultaneously under relatively static conditions. Multi-view point clouds were registered using the Iterative Closest Point (ICP) algorithm, with the floor plane serving as a unified reference to align all scans within a global coordinate system. In parallel, manual measurements of six key morphometric traits, including body length, withers height, shoulder width, abdominal width, heart girth and hip width, were collected as validation references. The dataset consists of raw RGB images, depth maps, point cloud files, camera calibration parameters, and manually annotated measurement records, all of which are openly accessible. This resource supports a wide range of computer vision tasks such as livestock 3D reconstruction, automated morphometric measurement, and weight estimation, thereby facilitating digital transformation, intelligent management, and sustainable development in modern livestock farming.