Corn Ear Detection and Orientation Estimation Using Deep Learning
This work addresses the time-consuming and error-prone manual monitoring of maize growth for agricultural researchers and producers, representing an incremental improvement with practical applications.
The paper tackled the problem of manually measuring corn ear orientation by developing a deep learning system that detects and estimates ear orientation in images, achieving 90% detection accuracy and a mean absolute error of 18 degrees for orientation estimation.
Monitoring growth behavior of maize plants such as the development of ears can give key insights into the plant's health and development. Traditionally, the measurement of the angle of ears is performed manually, which can be time-consuming and prone to human error. To address these challenges, this paper presents a computer vision-based system for detecting and tracking ears of corn in an image sequence. The proposed system could accurately detect, track, and predict the ear's orientation, which can be useful in monitoring their growth behavior. This can significantly save time compared to manual measurement and enables additional areas of ear orientation research and potential increase in efficiencies for maize production. Using an object detector with keypoint detection, the algorithm proposed could detect 90 percent of all ears. The cardinal estimation had a mean absolute error (MAE) of 18 degrees, compared to a mean 15 degree difference between two people measuring by hand. These results demonstrate the feasibility of using computer vision techniques for monitoring maize growth and can lead to further research in this area.