Artificial Intelligence in Precision Agriculture: Effects on Crop Productivity and Resource Efficiency

Abstract

This was a quantitative study that aimed to investigate the impacts of precision agriculture using artificial intelligence (AI) on crop productivity and resource use efficiency. The farmers surveyed were those using AI in agriculture, such as through the use of remote sensing, satellite data, automated irrigation, crop-monitoring apps, and AI-driven decision-making tools. The structured questionnaire was given to about 200 farmers who were selected using purposive sampling. Measures of level of AI adoption, precision agriculture practice, crop productivity, water-use efficiency, fertilizer-use efficiency, and overall resource efficiency measured on 5-point Likert scale items. The relationships between the adoption of AI and agricultural outcomes were analyzed using descriptive statistics, correlation analysis, and multiple regression analysis in SPSS. Cronbach's alpha was used to test the reliability while factor analysis was used to test the validity, and the multicollinearity was checked by the variance inflation factor (VIF). The results showed that the use of AI and precision agriculture techniques was significantly and positively correlated with crop productivity and overall resource efficiency in both regression models, with precision agriculture showing to be the stronger predictor for both models. The results indicate that the use of AI in precision farming had a positive impact on the productivity and resource efficiency of the farmers surveyed. Implications for agricultural extension services, for technology adoption policy and for future research are discussed.