In recent years, the international status of agriculture in the BRICS countries—Brazil, Russia, India, China and South Africa—has been continuously improved. In 2018, the gross agricultural production of the BRICS countries accounted for more than 50% of the world’s total. Further strengthening the developing cooperation of the BRICS countries is of great significance for ensuring global food security. Based on the data from FAOSTAT and UN Comtrade Database, this study builds a food self-sufficiency rate and food security cooperation potential index to quantitatively analyze the food security status, cooperation effectiveness, and future trends of the BRICS countries. The study finds that the overall food security of the BRICS countries is generally showing a trend of volatility and growth, with high rates for cereals and relatively low rates for fresh products. In the future, BRICS food security cooperation should be based on their own resource endowment and socioeconomic characteristics. The BRICS countries need to constantly improve the awareness of joint cooperation and action in the future, focusing on scientific and technological cooperation, information sharing, complementary advantages in trade, and improving the global competitiveness of products. With the help of agricultural science and technology, Brazil is growing as a strong export country of food products. Russia needs to increase the introduction of agricultural science and technology and foreign capital to give full play to its resource advantages. India can improve its food self-sufficiency faster by the construction of a BRICS Agricultural Research Platform. China makes full use of BRICS resources, actively promotes agricultural enterprises to go global, and constantly optimizes the food supply structure. South Africa maintains the advantages of fruit and vegetable industry and increases the introduction and promotion of agricultural science and technology to improve the domestic food production capacity.

Deep convolution neural network (DCNN) technology has achieved great success in extracting buildings from aerial images. However, the current mainstream algorithms are not satisfactory in feature extraction and classification of homesteads, especially in complex rural scenarios. This study proposes a deep convolutional neural network for rural homestead extraction consisting of a detail branch, a semantic branch, and a boundary branch, namely Multi-Branch Network (MBNet). Meanwhile, a multi-task joint loss function is designed to constrain the consistency of bounds and masks with their respective labels. Specifically, MBNet guarantees the details of prediction through serial 4× down-sampled high-resolution feature maps and adds a mixed-scale spatial attention module at the tail of the semantic branch to obtain multi-scale affinity features. At the same time, the low-resolution semantic feature maps and interaction between high-resolution detail feature maps are maintained. Finally, the result of semantic segmentation is refined by the point-to-point module (PTPM) through the generated boundary. Experiments on UAV high-resolution imagery in rural areas show that our method achieves better performance than other state-of-the-art models, which helps to refine the extraction of rural homesteads. This study demonstrates that MBNet is a potential candidate for building an automatic rural homestead management system.

The accurate and timely monitoring of land cover types is of great significance for the scientific planning, rational utilization, effective protection and management of land resources. In recent years, land cover classification based on hyperspectral images and the collaborative representation (CR) model has become a hot topic in the field of remote sensing. However, most of the existing CR models do not consider the problem of sample imbalance, which affects the classification performance of CR models. In addition, the Tikhonov regularization term can improve the classification performance of CR models, but greatly increases the computational complexity of CR models. To address the above problems, a local nearest neighbor (LNN) method is proposed in this paper to select the same number of nearest neighbor samples from each nearest class of the test sample to construct a dictionary. This is then introduced into the original collaborative representation classification (CRC) method and CRC with Tikhonov regularization (CRT) for land cover classification, denoted as LNNCRC and LNNCRT, respectively. To verify the effectiveness of the proposed LNNCRC and LNNCRT methods, the classification performance and running time of the proposed methods are compared with those of six popular CR models on a hyperspectral scene with nine land cover types. The experimental results show that the proposed LNNCRT method achieves the best land cover classification performance, and the proposed LNNCRC and LNNCRT methods not only further exclude the interference of irrelevant training samples and classes, but also effectively eliminate the influence of imbalanced training samples, so as to improve the classification performance of CR models and effectively reduce the computational complexity of CR models.

Raw milk powder is one of most important raw materials for dairy processing and manufacturing. In the past decade, the rapid growth of imported raw milk powder (IMP) consumption has brought about significant impacts on China’s social economy. For more in-depth analysis of such impacts, this study develops a comprehensive IMP consumption model for China covering 2007–2015 based on the input-output analysis method from the perspective of material metabolism. In detail, material flows and consumption analysis are employed to reveal the direct and indirect IMP consumption and supply of various sectors. Using an innovative hierarchy analysis and network utility analysis, the internal structure of the IMP consumption system and consumption relationships between different sectors are identified. Due to the increasing intensive production activities between the manufacture and processing of the dairy products sector and others, its downstream sectors have grown rapidly and the consumption relationships with others are mostly mutualism. The surge in IMP has caused dramatic changes in consumption relationships, which has the greatest impact on the service sectors, followed by manufacturing sectors. The rapid increase in IMP consumption has put great pressure on the supply and consumption of intermediate products of many sectors.

It is very significant for rural planning to accurately count the number and area of rural homesteads by means of automation. The development of deep learning makes it possible to achieve this goal. At present, many effective works have been conducted to extract building objects from VHR images using semantic segmentation technology, but they do not extract instance objects and do not work for densely distributed and overlapping rural homesteads. Most of the existing mainstream instance segmentation frameworks are based on the top-down structure. The model is complex and requires a large number of manually set thresholds. In order to solve the above difficult problems, we designed a simple query-based instance segmentation framework, QueryFormer, which includes an encoder and a decoder. A multi-scale deformable attention mechanism is incorporated into the encoder, resulting in significant computational savings, while also achieving effective results. In the decoder, we designed multiple groups, and used a Many-to-One label assignment method to make the image feature region be queried faster. Experiments show that our method achieves better performance (52.8AP) than the other most advanced models (+0.8AP) in the task of extracting rural homesteads in dense regions. This study shows that query-based instance segmentation framework has strong application potential in remote sensing images.

The continuous changes in Land Use and Land Cover (LULC) produce a significant impact on environmental factors. Highly accurate monitoring and updating of land cover information is essential for environmental protection, sustainable development, and land resource planning and management. Recently, Collaborative Representation (CR)-based methods have been widely used in land cover classification from Hyperspectral Images (HSIs). However, most CR methods consider the spatial information of HSI by taking the average or weighted average of spatial neighboring pixels of each pixel to improve the land cover classification performance, but do not take the spatial structure information for pixels into account. To address this problem, a novel Weighted Spatial–Spectral Joint CR Classification (WSSJCRC) method is proposed in this paper. WSSJCRC only performs spatial filtering on HSI through a weighted spatial filtering operator to alleviate the spectral shift caused by adjacency effect, but also utilizes the labeled training pixels to simultaneously represent each test pixel and its spatial neighborhood pixels to consider the spatial structure information of each test pixel to assist the classification of the test pixel. On this basis, the kernel version of WSSJCRC (i.e., WSSJKCRC) is also proposed, which projects the hyperspectral data into the kernel-induced high-dimensional feature space to enhance the separability of nonlinear samples. The experimental results on three real hyperspectral scenes show that the proposed WSSJKCRC method achieves the best land cover classification performance among all the compared methods. Specifically, the Overall Accuracy (OA), Average Accuracy (AA), and Kappa statistic (Kappa) of WSSJKCRC reach 96.21%, 96.20%, and 0.9555 for the Indian Pines scene, 97.02%, 96.64%, and 0.9605 for the Pavia University scene, and 95.55%, 97.97%, and 0.9504 for the Salinas scene, respectively. Moreover, the proposed WSSJKCRC method obtains the promising accuracy with OA over 95% on the three hyperspectral scenes under the situation of small-scale labeled samples, thus effectively reducing the labeling cost for HSI.