In planetary science, it is an important basic work to recognize and classify the features of topography and geomorphology from the massive data of planetary remote sensing. Therefore, this article proposes a lightweight model based on VGG-16, which can selectively extract some features of remote sensing images, remove redundant information, and recognize and classify remote sensing images. This model not only ensures the accuracy, but also reduces the parameters of the model. According to our experimental results, our model has a great improvement in remote sensing image classification, from the original accuracy of 85%-98% now. At the same time, the model has a great improvement in convergence speed and classification performance. By inputting the remote sensing image data of ultra-low pixels (64 * 64) into our model, we prove that our model still has a high accuracy rate of 95% for the remote sensing image with ultra-low pixels and less feature points. Therefore, the model has a good application prospect in remote sensing image fine classification, very low pixel, and less image classification.

The accurate and rapid acquisition of crop and weed information is an important prerequisite for automated weeding operations. This paper proposes the application of a network model based on Faster R-CNN for weed identification in images of cropping areas. The feature pyramid network (FPN) algorithm is integrated into the Faster R-CNN network to improve recognition accuracy. The Faster R-CNN deep learning network model is used to share convolution features, and the ResNeXt network is fused with FPN for feature extractions. Tests using >3000 images for training and >1000 images for testing demonstrate a recognition accuracy of >95%. The proposed method can effectively detect weeds in images with complex backgrounds taken in the field, thereby facilitating accurate automated weed control systems.

One of the most important food crops is rice. For this reason, the accurate identification of rice pests is a critical foundation for rice pest control. In this study, we propose an algorithm for automatic rice pest identification and classification based on fully convolutional networks (FCNs) and select 10 rice pests for experiments. First, we introduce a new encoder–decoder in the FCN and a series of sub-networks connected by jump paths that combine long jumps and shortcut connections for accurate and fine-grained insect boundary detection. Secondly, the network also integrates a conditional random field (CRF) module for insect contour refinement and boundary localization, and finally, a novel DenseNet framework that introduces an attention mechanism (ECA) is proposed to focus on extracting insect edge features for effective rice pest classification. The proposed model was tested on the data set collected in this paper, and the final recognition accuracy was 98.28%. Compared with the other four models in the paper, the proposed model in this paper is more accurate, faster, and has good robustness; meanwhile, it can be demonstrated from our results that effective segmentation of insect images before classification can improve the detection performance of deep-learning-based classification systems.

In actual farms, individual livestock identification technology relies on large models with slow recognition speeds, which seriously restricts its practical application. In this study, we use deep learning to recognize the features of individual cows. Alexnet is used as a skeleton network for a lightweight convolutional neural network that can recognise individual cows in images with complex backgrounds. The model is improved for multiple multiscale convolutions of Alexnet using the short-circuit connected BasicBlock to fit the desired values and avoid gradient disappearance or explosion. An improved inception module and attention mechanism are added to extract features at multiple scales to enhance the detection of feature points. In experiments, side-view images of 13 cows were collected. The proposed method achieved 97.95% accuracy in cow identification with a single training time of only 6 s, which is one-sixth that of the original Alexnet. To verify the validity of the model, the dataset and experimental parameters were kept constant and compared with the results of Vgg16, Resnet50, Mobilnet V2 and GoogLenet. The proposed model ensured high accuracy while having the smallest parameter size of 6.51 MB, which is 1.3 times less than that of the Mobilnet V2 network, which is famous for its light weight. This method overcomes the defects of traditional methods, which require artificial extraction of features, are often not robust enough, have slow recognition speeds, and require large numbers of parameters in the recognition model. The proposed method works with images with complex backgrounds, making it suitable for actual farming environments. It also provides a reference for the identification of individual cows in images with complex backgrounds.

In modern animal husbandry, intelligent digital farming has become the key to improve production efficiency. This paper introduces a model based on improved YOLOv8, Cattle Behavior Recognition-YOLO (CBR-YOLO), which aims to accurately identify the behavior of cattle. We not only generate a variety of weather conditions, but also introduce multi-target detection technology to achieve comprehensive monitoring of cattle and their status. We introduce Inner-MPDIoU Loss and we have innovatively designed the Multi-Convolutional Focused Pyramid module to explore and learn in depth the detailed features of cattle in different states. Meanwhile, the Lightweight Multi-Scale Feature Fusion Detection Head module is proposed to take advantage of deep convolution, achieving a lightweight network architecture and effectively reducing redundant information. Experimental results prove that our method achieves an average accuracy of 90.2% with a reduction of 3.9 G floating-point numbers, an increase of 7.4%, significantly better than 12 kinds of SOTA object detection models. By deploying our approach on monitoring computers on farms, we expect to advance the development of automated cattle monitoring systems to improve animal welfare and farm management.

Crop diseases and pests are important factors restricting agricultural production. Traditional pest detection methods are mainly targeted at a single pest species, which is difficult to meet the needs of multi-target identification and rapid response in real scenes. Therefore, this paper improves the YOLOv8n model for efficient multi-target pest detection. Two feature enhancement modules, EMSFEM and AFEM_SIE, are proposed in this paper. The EMSFEM module enriches the model’s receptive field through the combination of multi-scale asymmetric convolution kernel and different expansion rates and can better extract the width, height, texture, and edge information of the target. The AFEM_SIE module captures the similarities and differences between upper and lower features through spatial information exchange and enhances feature representation through inter-feature information exchange. In addition, an improved feature fusion operation, Concat_Weighting, is proposed on the basis of Concat. The module uses the learned weights to carry out channel weighting and feature graph weighting for input features, which realizes more flexible and effective feature fusion. The results of experiments conducted on the publicly available large-scale crop pest and disease dataset IP102 show that the performance of the AEC-YOLOv8n model is significantly improved compared with the original YOLOv8n model, with mAP50 increased by 8.9%, accuracy increased by 6.8%, and recall rate increased by 6.3%. The AEC-YOLOv8n model proposed in this study can effectively identify and deal with a variety of crop pests and has achieved the best detection accuracy on the IP102 dataset, which has high application value.