Quantitative Evaluation of Maize Emergence Using UAV Imagery and Deep Learning

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 8; p. 1979
• Main Authors: Liu, Minguo, Su, Wen-Hao, Wang, Xi-Qing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Accuracy; Agricultural production; Algorithms; case studies; Cereal crops; Climate change; Coefficient of variation; Color imagery; Corn; Cotton; crop emergence; Crop yields; Crops; Data collection; Data processing; Deep learning; Drone aircraft; Environmental aspects; Experiments; field phenotyping; Food; Genotypes; Global positioning systems; GPS; Growth; Growth stage; Image acquisition; Image processing; Irrigation; Machine learning; Mathematical analysis; Mean square errors; Methods; Morphology; Performance prediction; phenotype; Phenotyping; Planting; Planting density; prediction; Predictions; quantitative analysis; Remote sensing; Seeding; seedling emergence; Seedlings; Software; Spatial distribution; TOPSIS analysis; Unmanned aerial vehicles; unmanned aerial vehicles (UAV); Variation; Wheat; YOLO model; China
• DOI: 10.3390/rs15081979
• ISSN: 2072-4292

Accurate assessment of crop emergence helps breeders select appropriate crop genotypes, and farmers make timely field management decisions to increase maize yields. Crop emergence is conventionally quantified by manual calculations to quantify the number and size of seedlings, which is laborious, inefficient, and unreliable and fails to visualize the spatial distribution and uniformity of seedlings. Phenotyping technology based on remote sensing allows for high-throughput evaluation of crop emergence at the early growth stage. This study developed a system for the rapid estimation of maize seedling emergence based on a deep learning algorithm. The RGB images acquired from an unmanned aerial vehicle (UAV) were used to develop the optimal model for the recognition of seedling location, spacing, and size, and the prediction performance of the system was evaluated in three stations during 2021–2022. A case study was conducted to show the evaluation of the system for maize seedlings and combined with TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis. The results show that the system has good prediction performance for maize seedling count with an average R2 value of 0.96 and an accuracy of 92%; however, shadows and planting density influence its accuracy. The prediction accuracy reduces significantly when the planting density is above 90,000 plants/ha. The distribution characteristics of seedling emergence and growth were also calculated based on the average value and variation coefficient of seedling spacing, seedling area, and seedling length. The estimation accuracies for the average value of seedling spacing, the coefficient of variation of seedling spacing, the average value of the seedling area, the coefficient of variation of the seedling area, and the average value of the seedling length were 87.52, 87.55, 82.69, 84.51, and 90.32%, respectively. In conclusion, the proposed system can quickly analyze the maize seeding growth and uniformity characteristics of experimental plots and locate plots with poor maize emergence.