Segmentation and Classification of UAV-based Orthophoto of Watermelon Field Using Support Vector Machine Technique

Agricultural crops monitoring covering large-scale areas with high-efficiency is of great importance in sustainable agriculture management to combat the demand for agricultural crops brought by the exponential growth of population worldwide. The study was conducted to assess the promising potential of segmenting cloud free and high-resolution images for watermelon fruit monitoring using the combination of Unmanned Aerial Vehicle (UAV) orthophotographs and Support Vector Machine (SVM) supervised machine learning model. The digital images of the watermelon field were captured using UAV DJI Mavic 2 Pro through Pix4D operation and were orthorectified to generate an orthophotograph of the study site using the Agisoft Metashape software. After the segmentation using the edge segmentation and full lambda algorithm, the segmented objects were classified using the SVM technique with 150 and 100 segmented blocks as training samples representing the fruit of the watermelon and the non-fruit, respectively. The result revealed that use of high-resolution digital imagery and the application of machine learning techniques could be a tool to a wide area accounting of agricultural crops as indicated by the overall classification accuracy of 91% and a Kappa coefficient of 0.79.