Evaluation of Agricultural Modernization Development Level based on Exploratory Spatial Data Analysis and Back Propagation Neural Network

Recent days, agricultural modernization represents a transformative shift from traditional farming practices to a modern, science-driven agricultural system. The existing approaches for assessing agricultural modernization development level have encountered significant challenges which includes, model interoperability, identification of patterns and relationships, variability in temporal and spatial variables. Therefore, this paper proposes Exploratory Spatial Data Analysis-Backpropagation Neural Network (ESDABPNN) for Evaluation of Agricultural Modernization Development Level which is employed on statistical yearbook of Chinese government. Then, the data is preprocessed by using data normalization which effectively improved the model interoperability. After that, the ESDA-BPNN is used for analyzing the agricultural development which effectively identified the patterns and relationships between neighboring regions, highlighted the areas of high agricultural modernization. Finally, obstacle degree model is used for identifying key constraints and quantifies the obstacle impact. The proposed ESDA has increased in the year 2024 C 1 has increased 22%, C2 increased 24%, C3 increased 25%, C4 increases 22% and C5 increases 24%, where, C1, C2, C3, C4 and C 5 are per unit arable machinery, irrigation rate, per capita electricity consumption, agriculture results of expenditure, area of arable land per household when compared with existing ESDA.