Remote Sensing Application and Machine Learning Approach to Estimate the Availability of Local Food: A Case Study of Urban Area in Indonesia

Remote sensing technology has many applications in agriculture, including monitoring and analyzing local food systems in urban areas. In urban environments, space is often limited, and food is often produced on small plots of land; this makes it challenging to monitor and assess the condition of the crops using traditional methods. Traditional methods of predicting local food availability may not provide as much detail nor consider factors such as the specific weather conditions in a particular location or the health and growth of individual crops. Remote sensing technology can provide more detailed and specific information about these factors, allowing for more accurate predictions about local food availability. One of the critical applications of remote sensing for local food systems in urban areas is detecting and monitoring farmland and crops. These methods are an essential part of land use planning and policy recommendations. Crop yield prediction can be measured by applying satellite remote sensing to determine physiological conditions during the growing season. Machine learning has shown to be effective in a variety of data-driven applications. This study used Sentinel-2 images from March to October 2018 to map the normalized difference vegetation index (NDVI) and leaf area index (LAI) via satellite. Different growth stages were observed. The generated models were validated by linear regression and random forest algorithms. The results showed that the random forest method had good accuracy. NDVI had the highest accuracy (R2 = 0.90) in September 2018 and October 2018; however, the accuracy of LAI was very high (R2 = 0.91) as of July 2018. These results presented the late growth stage had good accuracy for estimating local crop production as crops reached the peak vegetative periods. Thus, machine learning provides reliable tracking of local food production at local and regional levels. The results show that vegetation indices can be used to calculate site-specific local crop management and predict yield. These integrated models can be used for logistics and decision-making related to local agricultural production.