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.

This paper uses an enhanced sparrow search algorithm (SSA) to optimise the support vector machine (SVM) by considering the emission of air pollution sources as the independent variable. Consequently, it establishes a PM2.5 concentration prediction model to improve the prediction accuracy of fine particulate matter PM2.5 concentration. First, the principal component analysis is applied to extract key variables affecting air quality from high-dimensional air data to train the model while removing unnecessary redundant variables. Adaptive dynamic weight factors are introduced to balance the global and local search capabilities and accelerate the convergence of the SSA. Second, the SSA–SVM prediction model is defined using the optimised SSA to continuously update the network parameters and achieve the rapid prediction of atmospheric PM2.5 concentration. The findings demonstrate that the optimised SSA–SVM prediction method can quickly predict atmospheric PM2.5 concentration, using the cyclic search method for the best solution to update the model, proving the method’s effectiveness. Compared with other methods, this approach has a small prediction error, a high prediction accuracy and better practical value.