Construction of Remote Sensing Model of Fresh Corn Biomass Based on Neural Network

• Published in: Computational intelligence and neuroscience 2022-05, Vol.2022, p.2844563-8
• Main Authors: Chen, Jianjian, Zhang, Hui, Bian, Yunlong, Li, Xiangnan, Lv, Guihua
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Altitude; Analysis; Artificial intelligence; Back propagation networks; Biomass; Brain research; Cameras; Corn; Crop yields; Elevation; Information processing; Interdisciplinary subjects; Low altitude; Methods; Neural networks; Remote sensing; Sensors; Spatial distribution; Vegetation
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/2844563

Corn has a high yield and is widely used. Therefore, developing corn production and accurately estimating corn biomass yield are of great significance to improving people’s lives, developing rural economy and climate issues. In this paper, a 3-layer BP neural network model is constructed by using the LM algorithm as the training algorithm of the corn biomass BP network model. From the three aspects of elevation, slope, and aspect, combined with the BP neural network model of corn biomass, the spatial distribution of corn biomass in the study area is analyzed. The results showed that the average biomass per unit area of maize increased with the increase in altitude below 1000 m. There are relatively more human activities in low altitude areas, which are more active in forestry production. The best planting altitude of corn is 0 ∼ 1000 m. When the altitude is higher than 1000 m, the corn biomass gradually decreases. In terms of slope, if the slope is lower than 15°, the biomass of maize increases with the increase in slope. If the slope is lower than 15°, the biomass of maize decreases gradually with the increase in slope. The biomass of maize on sunny slope was higher than that on shady slope.