Abstract: Soil Behavior helps the farmer predict performance for growing crops, nutrient movement, and determine soil limitations. The traditional methods for soil classification in the laboratory require time and human resources and are expensive. This analysis examines the possibility of image recognition by artificial intelligence, with a machine learning technique called deep learning, to develop the cases that use artificial intelligence. This study performed deep learning with a model using a neural network. Neural Networks has used to evaluate relationships between the parameters of the three-dimensional coordinates resulting in soil classification and parameters. So Artificial Neural Networks (ANN) can be an effective tool for soil classification. This paper focused on AI techniques used to predict the soil type, advice the crop to yield, and discuss the transformed learning and benefits.

N Lakshmi Kalyani and Kolla Bhanu Prakash, “Soil Color as a Measurement for Estimation of Fertility using Deep Learning Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130536

@article{Kalyani2022,
title = {Soil Color as a Measurement for Estimation of Fertility using Deep Learning Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130536},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130536},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {N Lakshmi Kalyani and Kolla Bhanu Prakash}
}