Against the backdrop of global food-security concerns, climate change, farmland constraints, and accelerating urbanization, modern agriculture is shifting from a land-dependent model toward a new paradigm shaped by spatial reconfiguration, energy integration, advanced equipment, and digital intelligence. Food systems account for a large share of anthropogenic greenhouse-gas emissions, making low-carbon transformation a central issue. Projected global food demand and hunger risk highlight the need for both productivity and resilience. Emissions from long-distance transport also suggest that localized production near consumption centers deserves greater attention. Taking the TsingSky Guangzhou... More >

The intelligent transformation of agriculture places plant growth prediction as a critical component for ensuring food security, optimizing resource allocation, and enhancing sustainable productivity. Traditional methods reliant on empirical or simplified mechanistic models struggle with the nonlinearity, high dimensionality, and spatiotemporal heterogeneity inherent in agro-ecological systems. This study investigates the paradigm shift enabled by agricultural big data integrating multi-source, real-time streams from IoT sensors, satellites, UAVs, and farm management systems. We propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically coupl... More >

Accurate recognition of greenhouse tomato leaf diseases is crucial for crop monitoring, timely intervention, and yield protection. In greenhouse environments, disease symptoms are often affected by complex illumination, background clutter, overlapping leaves, mixed patterns, and subtle inter-class differences, making reliable image-based diagnosis challenging. To evaluate compact convolutional neural networks for this task, this study presents a controlled comparison of five CNN models—MobileNetV3-Large, ShuffleNetV2\_x1\_0, MobileNetV2, EfficientNet-B0, and ResNet18—using the public Tomato Leaf Image Dataset (TLID). A curated split of 15,254 images covering seven conditions was used, wi... More >

Bamboo forests constitute a strategic resource within China's forest ecosystem, encompassing approximately 6.416 million hectares, which represents nearly 2% of the total forested area. This paper presents a comprehensive review of bamboo forest management policies in China, tracing the evolution of macro-level policy frameworks, organizational structures, and incentive mechanisms from the 1950s to the present. The Chinese bamboo sector has transformed from a marginal forestry subsector into a nationally prioritized industry, with the total output value projected to exceed one trillion yuan by 2035. Key policy milestones, including the 2008 reform of collective forest rights, national bamboo... More >

The integration of information technology into agriculture is fundamental to modern agricultural development. However, traditional agricultural information models, which rely on analytical AI for prediction and monitoring, face significant limitations in handling unstructured data, generating actionable knowledge, and supporting complex decision-making in dynamic farm environments. This study's core innovation lies in constructing a “Generative AI-Driven Agricultural Informatization Framework” (GAAIF), which quantifies the synergistic mechanisms between generative models and specific agricultural scenarios. By introducing a multi-modal data fusion engine and a task-specific fine-tuning p... More >

The growing volume of agricultural residues poses significant environmental and economic challenges, while existing waste management practices remain inefficient and unsustainable. This paper presents SAWAOS (Smart Agri-Waste Analysis and Optimization System), an applied AI-based decision-support framework for intelligent agricultural waste utilization. SAWAOS integrates waste characteristics, location information, and domain knowledge to generate context-aware recommendations for composting, bioenergy conversion, and industrial reuse. The system employs an explainable, rule-enhanced AI decision logic suitable for low-data rural environments and incorporates a digital marketplace that direct... More >

Under the background of AI promoting precision livestock farming, this study compared the effects of fermentation bed and net bed systems on gosling rearing in cold northern regions using AI-based intelligent temperature monitoring. A total of 10,000 one-day-old "Dasanhua" goslings were divided into two groups (n=5,000/group) and reared for 28 days. An AI-driven wireless temperature sensing system enabled real-time, high-precision monitoring of environmental temperatures. Growth performance (ADG, ADFI, F/G, survival rate) and economic benefits were systematically evaluated. The AI system revealed significant temperature differences: the fermentation bed maintained a stable average temperatur... More >

Hilly and mountainous areas account for 69% of China’s land area and undertake critical agricultural production tasks, but the poor adaptability of mainstream intelligent agricultural machinery and inefficient promotion models have become key bottlenecks restricting agricultural modernization. This study’s core innovation lies in constructing a ``four-dimensional integrated solution'' (equipment lightweight improvement - dynamic control optimization - hybrid sharing promotion - farmland mechanization-friendly transformation) and quantifying the coupling mechanism between topographic constraints and agricultural machinery performance. By introducing plot shape coefficient and slope volati... More >