Precision Farming in Nepal: A Machine Learning Perspective
Year: 2024
Type: article
Abstract: This paper encompasses three different machine learning models that we built to help Nepali farmers in selecting ideal crops for their land, using the right fertilizers, and predicting plant diseases. We tried about five models each for crop recommendation and fertilizer recommendation and a single model for plant disease prediction. We chose “Decision Trees” for both our Crop Recommendation and Fertilizer Recommendation and “Convolutional Neural Networks (CNN)” for Plant Disease Prediction. All models achieved over 95% accuracy. Our GitHub repository houses all the code, making it accessible for future researchers and ML developers working on related tasks.
Authors: Anam Giri, Rabin Sapkota, Raunak Shrestha, Prathama Shrestha, Darshan Paudel, Ashwina Pokharel
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W6911739649
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