<p>The world population continues to increase with a large proportion of this increase happening in Africa just as predicated by Food and Agriculture Organization of the United Nation. To meet the demand of feeding this increasing population, innovative farming practices are being developed to increase the yield of the major stable food supply. This research developed a computer vision and control algorithm on a quadcopter for farm surveillance, crop protection from the invasion of pest, early disease detection as well as crop yield estimation. A process of developing a model capable of detecting and classifying these pests in the environment was presented in this study. The process involves selecting, preprocess and transforming the sensed data from a vision sensor to train classification algorithm and to detect and track object of interest in the environment. This study addresses a notable research gap in computer vision, control systems, big data analytics, and robotics, specifically within the context of smart farming. It primarily utilizes conventional learning algorithms, including Artificial Neural Networks and Support Vector Machines, typically found in widely-used commercial software.</p>