Author(s): Mamadou MBAYE
This article presents an innovative approach to integrating hyperspectral imaging into agricultural investment and development strategies, with a focus on developing countries. The use of hyperspectral data offers a unique perspective for assessing crop health, soil moisture content, and other critical parameters, thereby enabling relevant decision-making in the agricultural field. Beyond satellite information, this advanced technology utilizes sensors and algorithms to collect microscopic-level data on soils. The proposed study combines hyperspectral information with climatic factors, agricultural investments, and socio-economic variables to predict the yield of enterprises in the agriculture sector. This model provides a solid foundation for understanding the adjustment of investments based on agricultural conditions detected by hyperspectral imaging, thereby contributing to maximizing production in often complex and unpredictable environments. These estimates optimize investment decisions, allowing various agricultural sector players to better anticipate potential yields and refine their financial strategies. This integrative approach is based on a robust analytical framework to assist entrepreneurs, researchers, and practitioners in the sector in leveraging hyperspectral imaging for more effective farm management in emerging economy contexts. By uniting elements of econometric modeling and advanced technology, our article paves the way for innovative solutions to address the challenges of agricultural growth in developing countries.