Maize Tassel Detection From UAV Imagery Using Deep Learning

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.

A UAV‐based high‐throughput phenotyping approach to assess time‐series nitrogen responses and identify trait‐associated genetic components in maize

“…The high‐resolution RGB images become especially critical to manually label the objects for training machine learning models. Recently, several machine learning models have been developed to detect flowering time using manually labeled RGB images (Alzadjali et al., 2021). Our discovery that the Woebbecke index showed a moderate level of correlation with the yield‐related trait may provide a useful RGB‐based index for further plant improvement.…”

Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm

“…The bounding box provides us with the specific location of the object in the image and the object classification to be found. In this study, we use Faster R-CNN as our object detection method, which has been proven an efficient and accurate method in many fields ( Alzadjali et al, 2021 ).…”

“…Unmanned aerial vehicles (UAVs) are particularly suitable for searching large-scale farms and dangerous areas ( Hassler and Baysal-Gurel, 2019 ). The detailed information of the scene generated through the photos of the drone’s onboard camera has made great contributions to agriculture, search, and rescue ( Alzadjali et al, 2021 ). By merging a large number of scattered or even overlapping bounding boxes in the image to approximate as a convex obstacle, we need to select a set of suitable points from the bounding box.…”

Analysis of key factors affecting maize tassels detection and construction of shared dataset based on UAV

“…wheat ear [23][24] and rice panicle detection [18,25] have achieved high accuracy, which can provide reference for our research. However, it should be noted that the detection of maize tassels is more difficult than sorghum and wheat, because tassels are tiny in size, and their color, morphological characteristics have little differences from background [4]. As for maize tassels detection based on deep learning, the existing researches are as follows.…”