Profitability of Sustainable Agricultural Innovations

The purpose of the research is to find the suitable area where new agricultural innovations are profitable for farmers while concurrently identifying and assessing the multivariate factors exerting influence on farmers' profitability on a global scale. Thus, we are conducting a survey where we plan to collect global data on a range of agricultural innovations, which we will subsequently use for academic research purposes. For this research, we need to understand the locations where these innovations are already being used and a couple of answers about them, especially whether they are profitable or not. The provided information will be used to train state-of-the-art machine learning algorithms to create global maps that show the profitability of the different innovations.

 Methodology: We have developed a short online survey, which we sent to experts from academia, companies, farming, and various other organizations and backgrounds. We will use these data points to train machine learning models to predict globally where the different technologies (precision agricultural maps, smart irrigation, weeding robots, etc.) are likely profitable at the moment and where they are not. This will help make agriculture more productive and sustainable.