Satellite image processing for precision agriculture and agroindustry using convolutional neural network and genetic algorithm

3rd International Symposium on LAPAN-IPB Satellite For Food Security and Environmental Monitoring 2016

地球科学;轻工业;生态环境科学

Firdaus^1 ; Arkeman, Y.^1,2 ; Buono, A.^1 ; Hermadi, I.^1

Departement of Computer Science, Bogor Agricultural University, Indonesia^1

Departement of Agroindustrial Technology, Bogor Agricultural University, Indonesia^2

关键词: Artificial intelligence methods;    CO2 emissions;    Convolutional neural network;    Economic factors;    Land degradation;    Pareto optima;    Satellite image processing;    Sustainable land use;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/54/1/012102/pdf
DOI  :  10.1088/1755-1315/54/1/012102

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Translating satellite imagery to a useful data for decision making during this time are usually done manually by human. In this research, we are going to translate satellite imagery by using artificial intelligence method specifically using convolutional neural network and genetic algorithm to become a useful data for decision making, especially for precision agriculture and agroindustry. In this research, we are focused on how to made a sustainable land use planning with 3 objectives. The first is maximizing economic factor. Second is minimizing CO2emission and the last is minimizing land degradation. Results show that by using artificial intelligence method, can produced a good pareto optimum solutions in a short time.