The Delineation and Grading of Actual Crop Production Units in Modern Smallholder Areas Using RS Data and Mask R-CNN

作者：Yahui Lv, Chao Zhang, Wenju Yun, Lulu Gao, Huan Wang, Jiani Ma, Hongju Li, Dehai Zhu · 发表于：Remote Sensing · 年份：2020 · DOI：10.3390/rs12071074 · 被引用次数：38 · 研究领域：Land Use and Ecosystem Services、Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications

The extraction and evaluation of crop production units are important foundations for agricultural production and management in modern smallholder regions, which are very significant to the regulation and sustainable development of agriculture. Crop areas have been recognized efficiently and accurately via remote sensing (RS) and machine learning (ML), especially deep learning (DL), which are too rough for modern smallholder production. In this paper, a delimitation-grading method for actual crop production units (ACPUs) based on RS images was explored using a combination of a mask region-based convolutional neural network (Mask R-CNN), spatial analysis, comprehensive index evaluation, and cluster analysis. Da’an City, Jilin province, China, was chosen as the study region to satisfy the agro-production demands in modern smallholder areas. Firstly, the ACPUs were interpreted from perspectives such as production mode, spatial form, and actual productivity. Secondly, cultivated land plots (C-plots) were extracted by Mask R-CNN with high-resolution RS images, which were used to delineate contiguous cultivated land plots (CC-plots) on the basis of auxiliary data correction. Then, the refined delimitation-grading results of the ACPUs were obtained through comprehensive evaluation of spatial characteristics and real productivity clustering. For the conclusion, the effectiveness of the Mask R-CNN model in C-plot recognition (loss = 0.16, mean average precision (mAP) = 82.29%) and a re...