Journal Article

IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms

Varadarajan, Vijayakumar, Bakthavatchalam, Kalaiselvi, Muthal, Sriram, Jose, Deepa, Kotecha, Ketan, Karthik, Balaguru, Thiruvengadam, Vijayan

2022

Overview

IoT architectures facilitate us to generate data for large and remote agriculture areas and the same can be utilized for Crop predictions using this machine learning algorithm. Recommendations are based on the following N, P, K, pH, Temperature, Humidity, and Rainfall these attributes decide the crop to be recommended. The data set has 2200 instances and 8 attributes. Nearly 22 different crops are recommended for a different combination of 8 attributes. Using the supervised learning method, the optimum model is attained using selected machine learning algorithms in WEKA. The Machine learning algorithm selected for classifying is multilayer perceptron rules-based classifier JRip, and decision table classifier. The main objective of this case study is to end up with a model which predicts the high yield crop and precision agriculture. The proposed system modeling incorporates the trending technology, IoT, and Agriculture needy measurements. The performance assessed by the selected classifiers is 98.2273%, the Weighted average Receiver Operator Characteristics is 1 with the maximum time taken to build the model being 8.05 s.

• Publisher: MDPI AG
• Subject: Agricultural production / Agriculture / Algorithms / Classifiers / decision table / Internet of Things / JRip / Machine learning / multilayer perceptron / Multilayer perceptrons / Neural networks / precision agriculture / Rainfall / Sensors / WEKA