Deep learning-based classification of mature and immature lavender plants using UAV orthophotos and a hybrid CNN approach

The classification of vegetation types worldwide plays a significant role in studies involving remote sensing. This method, used notably in agriculture, aids producers in devising more efficient agricultural management models. It relies on satellite and aircraft technologies to analyze agricultural lands. Nevertheless, the recent emergence of unmanned aerial vehicles (UAVs) has introduced faster and more cost-effective alternatives to traditional satellite and aircraft systems. These UAVs provide higher resolution images, leading to a shift in remote sensing practices. For deep learning in UAV-based image classification, convolutional neural network (CNN) techniques are commonly employed due to their advantageous features and exceptional extraction capabilities. This study proposes a hybrid approach based on CNN, combining 2D depthwise separable convolution (DSC) with a conventional 2D CNN and a Squeeze-and-Excitation network (SENet). The inclusion of SENet aims to boost classification performance without significantly increasing the overall parameter count. By integrating 2D DSC, computational costs and the number of trainable parameters are notably reduced. The multipath network structure's core purpose is to amplify the extracted features from UAV-derived images. The effectiveness of this multipath hybrid approach was evaluated using an orthophoto from Harran University's campus captured by a UAV. The primary goal was to distinguish between mature and immature lavender plants. The results indicate a high accuracy, with immature lavender plants classified at 99.77% accuracy and mature lavender plants at 95.15% accuracy. These findings from experimental studies demonstrate the high effectiveness of our hybrid method in identifying immature lavender plants.