基于无人机遥感与作物分布感知的撂荒地种植推荐策略OA

In recent years,due to the lag in farmland monitoring and management methods,abandoned land has become increasingly prevalent in some rural areas,resulting in decreased utilization efficiency of cultivated land and constraints on grain production capacity.To address this issue,a cropping recommendation strategy for abandoned farmland was proposed,which integrated crop monitoring with spatial distribution perception.The approach utilized UAV-acquired remote sensing imagery with complex farmland backgrounds as the primary research object.On the basis of the DeepLabv3+semantic segmentation model,a lightweight MobileNetv4 network was introduced as the backbone feature extractor to reduce parameter complexity and computational cost.Additionally,an adaptive fine-grained channel attention mechanism was incorporated in the decoder to enhance the model's sensitivity to crop boundary contours and texture details.To improve the extraction of small-scale farmland features under UAV nadir perspectives,the conventional 3×3 convolution was replaced with windmill convolution.Furthermore,a hybrid focal-dice loss function was constructed to mitigate the effects of class imbalance and the difficulty in distinguishing between visually similar crop categories.Finally,by combining the remote sensing analysis results with geolocation data and crop spatial distribution statistics,the model aggregated surrounding crop information over a broad spatial domain and recommended suitable crops for abandoned plots based on seasonal farming schedules and regional crop dominance.Experimental results demonstrated that the improved DeepLabv3+model achieved an ACC of 96.64%,mPA of 96.37%,and MIoU of 92.82%,representing increases of 1.85,3.71,and 6.10 percentage points,respectively,over the baseline model.This approach can provide a critical technical foundation for precision crop monitoring and abandoned land reutilization,promoting intelligent agricultural management and sustainable farmland development.