Maize Tassel Detection From UAV Imagery Using Deep Learning - Alzadjali, Aziza; Alali, Mohammed H.; Veeranampalayam Sivakumar, Arun Narenthiran; Deogun, Jitender S.; Scott, Stephen; Schnable, James C.; Shi, Yeyin (Frontiers in Robotics and AI, 2021)
The timing of flowering plays a critical role in determining the productivity of agricultural crops. Flower too early and the crop would mature before the end of the growing season, losing the opportunity to capture and use large amounts of light energy. Flower too late and the crop may be killed by the change of seasons before it is ready to harvest. Maize flowering is one of the most important periods where even small amounts of stress can significantly alter yield. In this work, we developed and compared two methods for automatic tassel detection based on imagery collected from an unmanned aerial vehicle (UAV) using deep learning models. The first approach was a customized framework for Tassel Detection based on Convolutional Neural Network (TD-CNN). The other method was a state-of-the-art object detection technique of the Faster Region based CNN (Faster R-CNN) serving as a baseline detection accuracy. The evaluation criteria for tassel detection was customized to correctly reflect the needs of tassel detection in an agricultural setting. Although detecting thin tassels in the aerial imagery is challenging, our results showed promising accuracy: the TD-CNN had an F1 score of 95.9%, and the Faster R-CNN had 97.9% F1 score. More CNN-based model structures can be investigated in the future for improved accuracy, speed, and generalizability on aerial-based tassel detection.