Evaluation of the Level of Farmland Infrastructure Based on High-Resolution Images of UAV

Jingrui Pan, Chunyan Chang (), Zhuoran Wang, Gengxing Zhao (), Yinshuai Li, Shuwei Zhang and Yue Chen
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Jingrui Pan: National Engineering Research Center for Efficient Use of Soil Fertilizer, College of Resources and Environment, Shandong Agricultural University, Tai’an 271000, China
Chunyan Chang: National Engineering Research Center for Efficient Use of Soil Fertilizer, College of Resources and Environment, Shandong Agricultural University, Tai’an 271000, China
Zhuoran Wang: National Engineering Research Center for Efficient Use of Soil Fertilizer, College of Resources and Environment, Shandong Agricultural University, Tai’an 271000, China
Gengxing Zhao: National Engineering Research Center for Efficient Use of Soil Fertilizer, College of Resources and Environment, Shandong Agricultural University, Tai’an 271000, China
Yinshuai Li: National Engineering Research Center for Efficient Use of Soil Fertilizer, College of Resources and Environment, Shandong Agricultural University, Tai’an 271000, China
Shuwei Zhang: National Engineering Research Center for Efficient Use of Soil Fertilizer, College of Resources and Environment, Shandong Agricultural University, Tai’an 271000, China
Yue Chen: National Engineering Research Center for Efficient Use of Soil Fertilizer, College of Resources and Environment, Shandong Agricultural University, Tai’an 271000, China

Sustainability, 2023, vol. 15, issue 17, 1-21

Abstract: The evaluation of the level of farmland infrastructure is a necessary objective condition for the use of arable land and agricultural development. In order to investigate the evaluation index system and method of farmland infrastructure level, this article uses the Kenli District of the Yellow River Delta as the research region. In the study region, six typical observation sample areas are chosen. Each area receives high-resolution UAV photos, which are then used to extract information about the farmland infrastructure of the field. A farming infrastructure evaluation index system was built, consisting of 10 indexes for four aspects, including farmland roads, field plots, ditches, and forest belts, using the 100 m by 100 m grid method to divide the evaluation units. The comprehensive index technique was used to calculate the farmland infrastructure score of each unit and identify the degree of excellent, good, and poor farmland infrastructure. The weight of each indication was decided by the hierarchical analysis method. There were 20 excellent grades, 77 good grades, and 29 poor grades among the 126 evaluation units in the study area, with excellent and good grades accounting for 79.13% and area proportions of 14.29%, 64.84%, and 20.87%, respectively. Among the six sample areas, sample areas E and F had the highest percentages of excellent grades, sample area A had 82.62% of the good grades, and all sample areas except A and C had a percentage of poor grades that was higher than 20%. Regularity of the fields, average size of the fields, and the agricultural plot’s slope are the dominant indexes of farmland infrastructure in each observation sample area, and the indexes of the ratio of the perimeter of roads to the perimeter of fields, density of ditches, and the ratio of area of agricultural forest networks to area of fields need to be optimized and improved. The spatial distribution of each grade differs significantly. The evaluation results are consistent with the real situation in the study region and have positive reference meaning for the development and management of farming infrastructure, according to the study’s proposed evaluation system and methodology.

Keywords: UAV images; extraction of features; farmland infrastructure; evaluation system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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