In the context of precision agriculture, recent years have witnessed a remarkable increase in the abundance of remote sensing image resources, accompanied by a significant boost in computing power. This progress has led to more refined land cover classification maps through semantic segmentation of remote sensing images, which is of great significance for farmland ecosystem analysis. In the agricultural domain, analyzing the distribution characteristics of farmland and roads,key components of the rural infrastructure network, enables accurate determination of straw yield and the distribution data of farmland roads. This information is crucial for formulating scientific and reasonable collection, storage, and transportation route plans, which is essential for efficient agricultural resource management.In existing research, within the scope of agricultural informatics, the application of deep-learning models to extract farmland road information encounters challenges such as high complexity. In this study, leveraging semantic segmentation technology, a core technique in remote sensing data processing for agriculture, research on the extraction of farmland road information from remote sensing images is carried out. Based on the land cover classification extracted from the images, research on optimizing the location selection of straw collection and storage stations is conducted, which is an important part of agricultural waste management.Through ablation experiments, it is demonstrated that both the asymmetric fusion non - local block (AFNB) and the Structure of the Dual Attention Module contribute positively to the segmentation effect. Compared with the original network model, the integrated results after combining these two components lead to increases in IoU, Acc-road, and mIoU by 5.2, 7.78, and 2.73 percentage points, respectively. By utilizing class activation graph analysis, this model significantly enhances the accuracy and efficiency of farmland road information extraction, a fundamental task in agricultural remote sensing. Its superiority is validated across multiple datasets, with achieving a minimum mIoU of 68.98%.To verify the generalization of the improved DlinkNet model in other rural regions within the agricultural landscape, Gaoyi County in Hebei Province is taken as an example. The farmland road extraction task is completed, and an in - depth analysis is performed based on the extraction results. Through research on optimizing the location selection of straw collection and storage stations, the straw yield and theoretical available amount of winter wheat, key parameters in agricultural production assessment, are calculated. Based on the World Cover dataset, this study optimized the site selection of straw collection and storage stations using the K-means clustering algorithm. By defining straw resource distribution density, residential areas, rivers, lakes, and other environmental factors, the optimal locations for village-level straw collection stations were determined. The results demonstrate that the selected sites meet the requirements of environmental protection, straw resource availability, and transportation accessibility, laying the foundation for subsequent research on straw transportation route optimization. This contributes to the establishment of a comprehensive optimization system for straw collection, storage, and transportation.

Maize stalk has been one of the major byproducts during agricultural production in China. This study aims to promote the energy utilization of maize stalks for soil fertility. The bioenergy production was integrated with the soil management strategies. The maize stalks were employed as the feedstock. The thermochemical conversion was utilized to produce the biochar in the potential application of biochar during soil improvement. A systematic investigation was conducted using thermogravimetric analysis (TGA) with Fourier transform infrared spectroscopy (TGA-FTIR). The weight loss of the corn stalk pyrolysis was elucidated to determine the dynamic release patterns of the volatile components. The pyrolysis experiment was initiated at ambient temperature with the gradient pyrolysis terminal temperature (300, 400, 500, 600, and 700°C) under a constant heating rate of 10°C/min. A series of biochar samples were produced after the test. A comparative analysis was performed to evaluate the contents of the organic carbon and inorganic carbon in the biochar samples, as well as the stability of the biochar. The laboratory-scale experiments of the biochar soil amendment were also carried out to assess the impact of the different pyrolysis temperatures on soil improvement. The experimental results showed that the increased heating rate led to the temperature difference between the surface and the interior of the corn straw samples, thus resulting in heat transfer resistance to inhibit pyrolysis. Subsequently, the maximum peak of the weight loss was caused by the shift into higher temperatures. Furthermore, the carbon-containing gases were carbon dioxide (CO₂), methane (CH₄), and carbon monoxide (CO) during pyrolysis. The yield of CO₂was much higher than that of the rest carbon-containing gases. The release of CO₂ was the primary cause of the mass loss in the feedstock. The carbon content in the biochar samples that were produced at different final pyrolysis temperatures significantly increased by 35.32% to 59.69% (P<0.05), compared with the maize stalk. Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), TGA, and the K₂Cr₂O₇ oxidation revealed that the biochar pyrolyzed at above 500 ℃ exhibited better thermal and chemical stability. The carbon sequestration potential of the biochar that produced at different pyrolysis terminal temperatures was ranged from 26.21% to 28.54%. The carbon sequestration potential was ranked in the descending order of: the biochar pyrolyzed at 700, 300, 600, 500, and 400 ℃. Compared with the no biochar addition, the biochar pyrolyzed at 300, 500, and 700 ℃ achieved the best improvement on the soil total nitrogen, total phosphorus, and total potassium, increasing by 39.21%, 39.52%, and 60.32%, respectively. Biochar pyrolyzed at 600 and 700 ℃ shared the best improvement on the soil organic and inorganic carbon, respectively, with an increase of 58.38% and 30.02%, respectively. Three-dimensional fluorescence spectroscopy indicated that the addition of biochar altered the composition of the soil organic matter, thereby affecting the content of humic acid. According to the energy consumption for biochar production, the carbon sequestration, and the soil fertility, the biochar pyrolyzed at 600 ℃ can be expected to serve as the ideal soil amendment.

This study aims to promote the full-value utilization of corn stover. Data sources were selected as the China Knowledge Infrastructure database and the Web of Science. Visualization software was used to draw knowledge graphs in the field of corn stover use. The current status of research and trends were summarized to analyze the key generic technologies of corn stover use. Two stages were divided in the utilization of corn stoves: 1990-2007 and 2008-2022. In the latter stage, the research system of corn stover use was gradually formed using fertilizer and feed that was supplemented by fuel. A literature review found that the research on corn stover use roughly experienced three phases of hot spot migration, each of which lasted about 10 years. The research was focused mainly on stover returning during the period 1990-2000; The focus of the research was straw returning to the field, straw energy, and feed use during the period 2001-2010; The utilization of corn stover was diversified to gradually form during the period 2011-2022. The utilization of corn stover was achieved mainly in the fields of stover returning and biomass energy production. According to the knowledge graph, the hot directions of corn stover use included biomass energy production, land improvement and protection, animal husbandry development, and feed production. Finally, the knowledge engineering and induction show that there were still some bottlenecks in the technology of corn stover use, such as delayed collection and transportation, low quality of returning, immature technology of stover feed, high cost, and low added value of energy technology. Driven by the important national demand for the utilization of agricultural waste resources, 13 key generic technologies were summarized for the full-value utilization of corn stover, including intelligent control and management of agricultural machinery and equipment, digital agriculture, high-quality field-returning, field-returning under the local conditions, sealed storage, high-yield cellulose strain of screening, selection, and breeding of efficient microbial strains for lignin degradation, strain matching and enzyme application, high-efficiency biomass energy conversion, stover cellulose to ethanol, lignocellulosic sugar, stover cultivation substrate preparation, and bio-based material manufacturing.