Remote sensing evaluation method for farmland machinery adaptability at the parcel level supported by deep learning

The adaptability level of farmland to agricultural machinery (i.e., the degree to which farmland facilitates agricultural machinery) can determine the efficiency and cost of mechanized operations in modern agriculture. However, existing research has focused mostly on the machinery adaptability or cost-benefit analysis after farmland consolidation. It is lacking in a systematic and refined evaluation of national food security. It is often required to implement the high adaptability of the large-scale farmland machinery. This study aims to automatically extract the spatial distribution of farmland parcels and roads using multi-source medium- and high-resolution remote sensing data and the deep learning semantic segmentation network HRNet (High-Resolution Network). A multi-task learning was employed to simultaneously predict the farmland, parcel boundaries, and roads. Parcel boundaries were also refined using conditional probability post-processing. Thereby, fine-scale data were provided to assess the adaptability level of farmland machinery. A 30 m digital elevation model (DEM) was combined with the paddy/dryland distribution data from a random forest classifier. An evaluation system was established from four dimensions: parcel shape, parcel size, surface evenness, and road accessibility. Seven indicators were obtained to determine the weight of each indicator. Criteria Importance Through Intercriteria Correlation (CRITIC) and the parcel-level Farmland Machinery Adaptability Index (FMAI) were subsequently calculated to validate in Dangyang City, Hubei Province. The results show that the HRNet semantic segmentation model performed well in both parcel and road extraction, with an area F1 score of 0.92 for farmland and 0.79 for roads, as well as an edge F1-score (F1-edge) of 0.85 for parcel boundaries and 0.86 for roads. A total of 468 000 farmland parcels and 9747.23 km of roads were identified in the study area. The spatial distribution of farmland machinery adaptability exhibited significant heterogeneity: High-adaptability areas (FMAI > 77.3) were concentrated in the southeastern Juzhang River alluvial plain in the central plains and low hilly areas, while the western hilly and mountainous regions generally shared the low adaptability levels. The grading results indicated that the area proportion of grades 1 and 2 (good adaptability) accounted for 67.3% of the total farmland area, but their parcel number proportion was only 38.6%, indicating the fragmented parcels generally had the lower adaptability grades. Township-scale attribution analysis revealed that the irregular parcel shape and small parcel size were common constraints to machinery adaptability over all townships, except for Caobuhu Town. The additional constraints were attributed to the insufficient road accessibility (road accessibility score 62-70) and low surface evenness (evenness score 78-85) in hilly and mountainous townships (e.g., Wangdian and Miaoqian). The evaluation can effectively and finely characterize the parcel-level farmland machinery adaptability and its limiting factors. The finding can provide data support and decision-making for dynamic monitoring of large-scale machinery adaptability, differentiated consolidation, and high-standard construction in farmland.