Model for Fruit Tree Classification Through Aerial Images

Manual measurements and visual inspection of trees are common practices among farmers, which incur labor costs and time-consuming operations to obtain information about the state of their crops at a specific moment. Considering that an approximately 1-hectare (ha) plot of land can have up to 1100 planted trees [1], this becomes a challenging task, and human error in such cases tends to be high. To address these issues, the emphasis is placed on the use of Convolutional Neural Networks (CNNs); however, CNNs alone are not robust enough to detect complex features in any given problem. Therefore, this article proposes a model that supports agricultural activities in organizing their tasks. The main procedure of the model is the classification of fruit trees (mango, citrus, and banana) using aerial images captured by a drone (UAV) in the Colombian context. The technique employed in this procedure is known as Mask R-CNN, which enables automatic segmentation of fruit trees.