Application of machine learning in crop productivity enhancement through crop recommendation in Nepal | |
| Author | Baral, Smrity |
| Call Number | AIT Thesis no.CS-23-03 |
| Subject(s) | Artificial intelligence--Agricultural applications--Nepal Machine learning |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Computer Science |
| Publisher | Asian Institute of Technology |
| Abstract | Agriculture is undoubtedly the largest provider of livelihoods in most of the developing country. Also, for most of these countries, the agriculture sector is a significant contributor factor to the country’s Gross Domestic Product. In the context of Nepal, agriculture is the main occupation of Nepalese people. The population is increasing, and so is the need for food. However, the productivity in Nepal is not enough for the Nepalese people. Farmers in developing countries like Nepal face various environmental and economic constraints, such as limited arable land, global warming, infertile soil, and lack of proper resources. The overall motive of the research is to apply the best Machine Learning model to suggest suitable crops to farmers based on their location's environmental and economic conditions, which results in improved crop productivity. The study uses regression models to predict crop price and crop yield while employing classification models to select appropriate crops based on soil and weather conditions. The research objectives include designing and developing machine learning models for crop selection, comparing different models to find the most accurate crop selector model, developing yield and crop price prediction models, integrating crop selection with production and crop price predictions, and testing the system for functionality. The models utilize various features, including soil and weather parameters, to predict crop prices and yields, and suggest suitable crops. The study employs multiple regressor and classifier models, including Linear Regression, Support Vector Regressor, DecisionTree Regressor, RandomForest Regressor, and XGB Regressor for prediction while it uses KNN, DecisionTree Classifier, RandomForest Classifier, and XGB Classifier for crop classification, with their default values initially. For Crop price prediction and Crop yield prediction, SVR and XGBooster performed well with a test accuracy of 91.6% and 96.4% respectively. For crop selection, Randomforest Classifier performed better with a test accuracy of 94.13%. The models are implemented in web applications created in the Django framework. The results of this study can assist farmers in Western Nepal to make informed decisions about crop selection and production, based on both environmental and economic factors. Keywords: Crop Recommendation, Crop Price Prediction, Crop Yield Prediction, Machine Learning, KNN, Linear Regression, Decision Tree, SVR, XGBooster, Random Forest. |
| Year | 2023 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Chutiporn Anutariya |
| Examination Committee(s) | Chaklam Silpasuwanchai;Himanshu, Sushil Kumar |
| Scholarship Donor(s) | AIT Scholarships |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2023 |