Deep Machine Learning on Segmenting and Classifying Crop Images Taken by Unmanned Aerial Vehicle

In the realm of precision agriculture, a crucial element involves the precise quantification or estimation of seedlings, fruits, and other agricultural produce on expansive multi-acre farms at various stages of cultivation. With the advent of unmanned aerial vehicles (UAVs), capturing images of watermelon fields has become a straightforward task. These images can be subsequently processed, segmented, and categorized to determine the total count of watermelons. Currently, conventional methods are employed to address this challenge, but they have their limitations. The field has benefited from the evolution of machine learning, which has the potential to streamline the process. Nevertheless, the training phase is intricate, and achieving a valuable model can be demanding. This research delves into an examination and presentation of the existing pre-trained models for image processing in this context.