İnsansiz hava aracı (İHA) görüntülerinin analizi ile karpuz meyvelerinin tespiti üzerine bir araştırma

Unmanned aerial vehicles (UAV) equipped with a digital camera are one of the technologies that the precision agriculture profits from. In this study, watermelons in the images obtained by a UAV from a watermelon field in Sarıçam, Adana, Turkey were segmented. For the study, three approaches were implemented. In the first approach, Haralick features and Bayes Linear Discriminant Analysis (LDA) methods with two categories were used. In the second approach, the first approach was combined with k-means clustering. Next, the second approach was developed by considering three categories with one versus all classifier in the final approach. The classification performance of each approach was evaluated and reviewed by utilising confusion matrices obtained from the classifier outcomes. The average categorization accuracy and the rate of detected watermelons without incorporating clustering outcome were 96.5% and 98.5% respectively. It is worth emphasizing that k-means clustering enhances the segmentation and, consequently, the accuracies. This study is the first step of yield estimation in watermelon production. It is believed that watermelon detection using image processing technology can be an asset to farmers and dealers regarding yield estimation and marketing. Key Words: UAV; image segmentation; Haralick features; linear classifier; k-means clustering; precision agriculture; watermelon detection.