Automatic Monitoring of Maize Seedling Growth Using Unmanned Aerial Vehicle-Based RGB Imagery

Abstract

Accurate and rapid monitoring of maize seedling growth is critical in early breeding decision making, field management, and yield improvement. However, the number and uniformity of seedlings are conventionally determined by manual evaluation, which is inefficient and unreliable. In this study, we proposed an automatic assessment method of maize seedling growth using unmanned aerial vehicle (UAV) RGB imagery. Firstly, high-resolution images of maize at the early and late seedling stages (before and after the third leaf) were acquired using the UAV RGB system. Secondly, the maize seedling center detection index (MCDI) was constructed, resulting in a significant enhancement of the color contrast between young and old leaves, facilitating the segmentation of maize seedling centers. Furthermore, the weed noise was removed by morphological processing and a dual-threshold method. Then, maize seedlings were extracted using the connected component labeling algorithm. Finally, the emergence rate, canopy coverage, and seedling uniformity in the field at the seedling stage were calculated and analyzed in combination with the number of seedlings. The results revealed that our approach showed good performance for maize seedling count with an average R2 greater than 0.99 and an accuracy of F1 greater than 98.5%. The estimation accuracies at the third leaf stage (V3) for the mean emergence rate and the mean seedling uniformity were 66.98% and 15.89%, respectively. The estimation accuracies at the sixth leaf stage (V6) for the mean seedling canopy coverage and the mean seedling uniformity were 32.21% and 8.20%, respectively. Our approach provided the automatic monitoring of maize growth per plot during early growth stages and demonstrated promising performance for precision agriculture in seedling management.

A New Method for Estimating Groundwater Changes Based on Optimized Deep Learning Models—A Case Study of Baiquan Spring Domain in China

“…The batch size was determined to ing the computational load and the model's ability to generalize from th Additionally, the model was trained for 1000 epochs to ensure thorough le data without overfi ing. This combination of learning rate, batch size, an cial for the model's ability to accurately predict future GWLs, striking a b complexity, computational efficiency, and prediction accuracy [35,36].…”

“…Additionally, the model was trained for 1000 epochs to ensure thorough learning from the data without overfitting. This combination of learning rate, batch size, and epochs is crucial for the model's ability to accurately predict future GWLs, striking a balance between complexity, computational efficiency, and prediction accuracy [35,36].…”