Title: PERCEPTION OF FARMERS TOWARDS THE USE OF DRONES IN AGRICULTURE: A MACHINE LEARNING APPROACH

Authors: R. K. Garasia, R. S. Parmar and J. K. Patel

Publisher: The President, Society of Extension Education Gujarat

Keywords: drone technology, perception, farmers, Machine learning

Volume: 41

Issue: 2

Year: June 2026

DOI: https://doi.org/10.56572/gjoee.2026.41.2.0019

Abstract: Agriculture is increasingly influenced by digital innovations aimed at improving productivity and sustainability. Among these innovations, drone technology has emerged as a promising tool for precision farming. However, the successful adoption of this technology largely depends on farmers' perception and acceptance. The present study aimed to analyse and predict farmers' perception towards the use of drones in agriculture using machine learning techniques. The study was conducted in seven talukas of the Ahmedabad district, with a random sample of 120 farmers. Eleven independent variables were considered, while perception towards drone usage was treated as the dependent variable. Six classification algorithms- Multilayer Perceptron (MLP), Support Vector Machine (SMO), k-Nearest Neighbours (IBk), KStar, J48, and Random Forest (RF)-were implemented using WEKA open-source software. The models were selected based on accuracy, Kappa statistics, MAE, RMSE, precision, recall, F1-score, and MCC. Among the applied models, the J48 algorithm demonstrated the best performance, achieving 79% accuracy, the highest Kappa value (0.33), F1-score (0.76), and MCC (0.34). The findings indicate that tree-based models, particularly J48, were more effective in predicting farmers' perception towards drone adoption. The study highlights the potential of machine learning techniques in agricultural technology adoption research.