Machine learning framework and tools in precision farming

Abstract

Farming using machine learning (ML) techniques has a role to play in the current globalization scenario due to the advantages it offers for costeffective harvesting of the crop. The areas such as crop disease detection, soil nutrient detection, fertilizer analysis and optimization, weather and irrigation schedule prediction, are investigated utilizing a range of deep learning and ML techniques, such as K-nearest neighbors (KNNs), convolutional neural networks (CNNs), and support vector machines (SVMs). The article concentrates on preparing the recommendation system for the farmer to take a quick and timely decision for crop disease, use of optimal fertilizer for crop growth, and water requirement prediction to overcome water wastage. A massive amount of data, including image data from publicly accessible sources, such as PlantVillage, Kaggle is used to train the model. Sensor data is fed into the ML model for the nutrients analysis and water requirement analysis. An Android application is developed, which can be used from any handheld device by the farmers to take advantage of the proposed recommendation system. The result shows the promising future with better accuracy than previously available models in the same area. Parameters including recall, accuracy, precision, and F1-score are considered to gauge performance.