Customized crop feature construction using genetic programming for early-and in-season crop mapping☆

作     者：Wen, Caiyun Lu, Miao Bi, Ying Xia, Lang Sun, Jing Shi, Yun Wei, Yanbing Wu, Wenbin 

作者机构：Chinese Acad Agr Sci Inst Agr Resources & Reg Planning State Key Lab Efficient Utilizat Arid & Semiarid A Beijing 100081 Peoples R China Natl Ctr Technol Innovat Comprehens Utilizat Salin Dongying 257345 Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China State Key Lab Intelligent Agr Power Equipment Luoyang 471000 Peoples R China 

出 版 物：《COMPUTERS AND ELECTRONICS IN AGRICULTURE》 (Comput. Electron. Agric.)

年 卷 期：2025年第231卷

核心收录：

学科分类：09[农学] 0901[农学-作物学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Key Research and Development Program of China [2023YFD200140101] National Natural Science Foundation of China Agricultural Science and Technology Innovation Program (ASTIP) [CAAS-ZDRW202201] 

主　　题：Remote Sensing Crop mapping Genetic Programming Feature Construction Customized feature 

摘      要：Early- and in-season crop mapping provides vital information for precision agriculture. It is still a challenge for early- and in-season crop mapping because of the limited available images and similar spectral information. This study aims to enhance early- and in-season crop mapping by developing a Genetic Programming (GP) method to construct customized crop features. GP automatically generated candidate features for the target-crop using early- or in-season images, selected programs with substantial value disparities between target and non-target crops through the fitness function, and finally outputted the customized feature after the evolutionary process. These customized features were then compared with commonly used spectral bands and vegetation indices to evaluate their effectiveness for early- and in-season crop mapping. The results proved that the customized crop features had significant advantages in both early- and in-season crop mapping. The early-season accuracy in April after crop planting was 3.97% to 9.53% higher than spectral features and vegetation indices. Based on the incremental classification for the in-season crop mapping, the customized crop features maintained the best performance. Advantages of customized crop features include the ability to automatically select effective bands of useful months without requiring expert knowledge, the ability to catch and enlarge the subtle spectral differences with the early- and in-season images, and the little information redundancy compared with spectral features and vegetation indices. It can be concluded that the customized crop features are outstanding for early- and in-season crop mapping.