Utilization of Artificial Intelligence and Remote Sensing for Yield Prediction and Irrigation Management in Drylands

Abstract

Drylands face serious challenges in the agricultural sector, especially in terms of water availability and accurate crop yield prediction. This study aims to assess the utilization of artificial intelligence (AI) and remote sensing technology in supporting crop yield prediction and efficient irrigation management in drylands. Using a descriptive qualitative approach, data were collected through in-depth interviews, structured questionnaires, participatory observation, and documentation studies in five villages in Belu District, East Nusa Tenggara. The results showed that the integration of AI and remote sensing was able to improve water distribution efficiency by 30% and the accuracy of crop yield prediction reached more than 85%. Respondents such as extension workers and technicians showed a high level of satisfaction with the technology, although some farmers still face digital literacy barriers. Observational findings also confirmed that vegetation in technologically advanced fields had a higher plant health index (NDVI). This study suggests the importance of technology training for farmers as well as the provision of supportive digital infrastructure as strategic steps in the implementation of sustainable precision agriculture.