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Maize Tassel Detection From UAV Imagery Using Deep Learning

Aziza Alzadjali, Mohammed H. Alali, Arun Narenthiran Veeranampalayam Sivakumar, Jitender S. Deogun, Stephen Scott, James C. Schnable,
Published: 9 June 2021
Frontiers in Robotics and AI , Volume 8; doi:10.3389/frobt.2021.600410

Abstract: The timing of flowering plays a critical role in determining the productivity of agricultural crops. If the crops flower too early, the crop would mature before the end of the growing season, losing the opportunity to capture and use large amounts of light energy. If the crops flower too late, 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 the imagery collected from an unmanned aerial vehicle, 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 baseline detection accuracy. The evaluation criteria for tassel detection were 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.
Keywords: phenotyping / object detection / flowering / Faster R-CNN / CNN

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