Deep Learning-Enhanced Unmanned Aerial Vehicles: Detection and Classification in Crop Production

Over the past few years, unmanned aerial vehicles (UAVs) have been revolutionizing various sectors. In the context of agriculture, UAVs have been increasingly employed by researchers for diverse applications, especially as remote sensing platforms. These advanced platforms enable efficient scientific data collection, offering significant advantages over traditional methods and satellite imagery. UAVs capture spectral imagery at exceptionally high resolutions on demand, and bypass cloud cover, providing near-real-time data acquisition. As the volume of high-quality data continues to grow, the demand for effective analysis, classification, and meaningful interpretation of agricultural parameters is becoming increasingly critical. Deep learning (DL), an advanced form of artificial intelligence (AI), plays a vital role in this endeavor through its neural networks, which enable the automatic feature extraction and the efficient handling of large datasets. This chapter delves into the DL methodologies, focusing on convolutional neural networks (CNNs), object detection, and image classification techniques applied to UAV imagery in agricultural landscapes.