In recent decades, agricultural goods demand has grown exponentially due to the growth of the human population. Agricultural production demands new and simple techniques. Moreover, safe, efficient, and cost-effective methods are required for monitoring agriculture crops. This research aims to provide a simple image processing technique that detects and distinguishes the object (agricultural goods) in drone-based agrarian imagery. We used cotton crop images as experimental data in this research due to its different spectral characteristics according to drone camera sensors, minimum weather constraints, and flight schedule timing-related constraints. The proposed method used fuzzy reasoning-based tactics combined with RGB and HSV color spaces by manipulating image color pixel values and setting the upper/lower limits values of the colors for detection and distinguishing the agricultural objects such as white cotton bolls from the rest of the image.