Convolutional neural network soil organic matter analysis model construction system and method

• Main Authors: CHENG FEIYAN, MA MINGXING, AN XIAOYU, SONG ZHENQIANG, PAN TUO, ZHANG ZHEN, GAO XIAODONG, WEI MAOSHENG
• Format: Patent
• Language: Chinese; English
• Published: 17.09.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS

The invention relates to the technical field of remote sensing technology and variable rate fertilization, in particular to a convolutional neural network soil organic matter analysis model construction system and method. The system comprises an original data arrangement module, a wave band transformation module, a sensitivity analysis module, a transformation wave band analysis module, a parameter input module, a convolutional neural network construction module, a model training module, a precision evaluation module, a coarse error elimination module, a model verification module, a model storage module, a model retraining module and a result map output module which are operated in a flow mode. The sensitivity analysis module is used for analyzing the sensitivity degree of an original wave band and a transformation wave band of a soil sample image to soil organic matters and calibrating input wave band parameters, and accurate and comprehensive fertilization can be guided through a soil organic matter analysi