An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet - Geetha, K. (Journal of Soft Computing Paradigm, 2022)
Predictions and estimations are very important for agriculture applications. The estimation results on crop production may have a huge impact in the economy of a country by changing their export and import data. The estimation of crop production was started by collecting information manually from the fields and analyzing it using a computer. However, the accuracy was not up to the mark due to the error caused by manual collection of data. The Geographic Information System (GIS) applications are developed to store the information observed from the satellite images on change detection in town planning, disaster management, business development and vegetation management. The proposed work estimates the crop production of Indian states from a GIS dataset with a SqueezeNet algorithm. The performance of the SqueezeNet algorithm is compared with the traditional Inception and ResNet algorithms.
Keywords: GIS data retrieval; GIS information processing; crop data prediction; vegetation planning; neural network analysis on agriculture