Recent research studies showed that the performance of significantly improving classification of hyperspectral images can be done with characteristics related to both spectra and space. This paper documents an innovation: drone-based sensing system that improves upon the analysis of satellite imagery for detailed rice farm mapping. The system provides superior sensors to capture high-resolution imagery and multispectral data while recording vital parameters like indices of vegetation, moisture levels in soil, and temperatures. The concept about using a 3D CNN framework to enhance hyperspectral image classification is then provided. Coupled with this is the use of high-resolution aerial data by drones along with the bigger overview from the satellite could make it all possible with this system so that each of the spatial variations is well captured as well as identified, and this includes an analysis of field-wise or farm-wise status of health for crops, which would tell them if some had diseases. It proposes a new approach in the use of precision agriculture.