Smart Farming (Ai-Generated) as an Approach to Better Control Pest and Disease Detection in Agriculture: POV Agricultural Institutions

Abstract

Purpose – The purpose of current study was to examine the role of smart farming through artificial intelligence (AI) (Data Integration; Machine Learning; Sensor Technologies; Image Processing and Computer Vision; Decision Support Systems and Scalability and Adaptability) in controlling pest and disease detection in agriculture.
 Methodology/ Design / Approach – Quantitative methodology was adopted and a questionnaire was self-administered online by (328) agricultural engineers working in private agricultural institutions in Jordan that are subject to the laws of the Jordanian Ministry of Agriculture. SPSS was employed in order to screen and analyze primary data.
 Findings – Study results indicated that acceptance of the main hypothesis that argued, “Smart Farming Agriculture has an effect on Control Pest and Disease Detection”. Results indicated an R-value (0.963) and an overall variance of 92.7%. In addition to that, among the chosen sub-variables of study, results revealed that scalability and adaptability scored that highest influence on disease detection and control with (r = 0.961) and an overall variance of 92.4%. Study recommended the necessity of training and qualifying agricultural staff to use modern agricultural technology and artificial intelligence.
 Originality – The originality of the current study lies in its application within the Jordanian environment. In addition, there weren’t direct studies that took into perspective the idea of smart farming through AI and its uses in pests and disease detection and control in crops. 
 Implications – The implications of current study stems from its ability to present the AI potentials to enhance food production, increase the efficiency of agricultural materials that would be a source in guaranteeing food security, and create job opportunities for individuals in this sector.