our idea is to develop a machine learning model which is capable of predicting the quality of soil. Our idea focuses on Agriculture domain. Agriculture is the key to the economy and infrastructure of India. It plays a significant and most strategic role in the progress and financial growth of the nation. As the technology is rapidly advancing, extending it to agricultural domain yields most needed and promising results in achieving precision agriculture. The model we have designed is a works towards achieving it. The model that analyzes the quality of soil thereby predicting the yield of the crop by considering various parameters. Crop yield prediction provides information for decision makers to maximize the crop productivity.Manually testing the quality of soil regularly is a complex task, so there is a need for automating the process that we are currently following, through an ML (Machine Learning) Model. Machine learning approach offers new contingency in the field of agriculture which is very much useful in soil dataset analysis and visualization of various parameters related to soil which would also help in decision making. It is crucial to design and implement a well-planned management system for monitoring various nutrients level by means of soil analysis procedure. In our model, various soil data sample from various regions are classified based on primary and secondary properties.

Machine Learning, Image Dataset, Soil Parameters, Image Processing, Supervised Learning, SVM Image Classifier

[1] P. Vinciya, Dr. A. Valarmathi, “Agriculture Analysis for Next Generation High Tech Farming in Data Mining”, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Volume 6, Issue 5, 2016.
[2] Shivnath Ghosh, Santanu Koley, “Machine Learning for Soil Fertility and Plant Nutrient Management using Back Propagation Neural Networks”, International Journal on Recent and Innovation Trends in Computing and Communication(IJRITCC), Volume 2 Issue 2, 2014.
[3] Priya R L, “Prediction of crop yield using machine learning”, International Research Journal of Engineering and Technology (IRJET), Volume 5, Issue 2, 2018.
[4] Karisiddappa, Ramegowda, “Soil characterisation based on digital image analysis”, In the proceedings of 2010 Indian Geotechnical Conference, India.
[5] A. Morellos, “Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy”, Biosystems Engineering, Volume 152, 2016.
[6] Vrushal Milan Dolas, “A Novel Approach for Classification of Soil and Crop Prediction”, International Journal of Computer Science and Mobile Computing, Volume 7, Issue 3, 2018.
[7] Supriya D M, “Analysis of Soil Behavior and Prediction of Crop Yield using Data Mining Approach”, International Journal of Innovative Research in Computer and Communication Engineering, Volume 5, Issue 5, 2017.
[8] Rushika Ghadge, “Prediction of Crop Yield using Machine Learning”, International Research Journal of Engineering and Technology, Volume 5, Issue 2, 2018.
[9] Vaneesbeer Singh,Abid Sarwar, “Analysis of soil and prediction of crop yield (Rice) using Machine Learning approach”, IJARCSE, Volume 5, Issue 8,2017.
[10] P. Vinciya, Dr. A. Valarmathi, “Agriculture Analysis for Next Generation High Tech Farming in Data Mining”, IJARCSSE,vol. 6, Issue 5, 2016.