Soil Analysis and Classification to Improve the Crop Yield in Agriculture Using Fast Graph - CRNN Approach

Changes in land use over the past few decades have left their mark on Earth's surface. Key ecosystem services provided by soils are currently being altered due to global environmental changes. In light of these factors, monitoring equipment is required to maintain a healthy ecological status and advance soil conservation on a local, regional, and global scale. This system demonstrates the potential utility of remote sensing in collecting soil properties. Methodologies based on various remote sensing devices and classification strategies have been developed for assessing soil characteristics. Soil analysis by remote sensing is a relatively new area of study in the agricultural sciences. The majority of people in India rely on agriculture as a means of subsistence and economic survival. The low cost of yield prediction has led to a decline in agricultural output in recent years. Predicting yields is challenging because of the many variables at play, including as soil, rainfall, and fertilizers. The quality of the soil is crucial for agricultural output. MSC and SNV are used in the suggested method for preprocessing. LDA is used for feature selection. The proposed method employs FGCRNN to train the envisioned model. The proposed model is evaluated in comparison to the CNN and GRU models. Its performance is superior to that of the other two types.