|PATHAK, HARSH - North Dakota State University|
|CANNAYEN, IGATHINATHANE - North Dakota State University|
|ZHANG, ZHAO - China Agricultural University|
Submitted to: Computers and Electronics in Agriculture
Publication Type: Review Article
Publication Acceptance Date: 5/15/2022
Publication Date: 5/25/2022
Citation: Pathak, H., Cannayen, I., Zhang, Z., Archer, D.W., Hendrickson, J.R. 2022. A review of unmanned aerial vehicle based methods for plant stand count evaluation in row crops. Computers and Electronics in Agriculture. 198:107064. https://doi.org/10.1016/j.compag.2022.107064.
Technical Abstract: Plant stand count helps in estimating the yield and evaluating the planter's efficiency and seed quality. Traditional methods of counting the plants by manual measurement are time-consuming, laborious, and error-prone. In contrast, the ground-based sensing methods are limited to smaller spaces. High spatial resolution images obtained from unmanned aerial vehicles (UAV) can be used in conjunction with computer vision algorithms to evaluate plant stand count, as it directly influences the yield. In spite of the importance of high-throughput plant stand count in row crop agriculture, no synthesized information in this specific subject matter is available. Therefore, the objective of this paper was to review the current studies that focus on evaluating plant stand count using UAV imagery to provide well-synthesized information, identify research gaps, and provide some recommendations. In this study, a comprehensive literature search was performed on three academic databases (Agricola, Web of Science, and Scopus), and a total of 29 articles were found based on search terms and selection criteria for review. From the systematic review, it can be concluded that: appropriate stage after plant emergence without canopy overlap is necessary for image acquisition; optimal flying height should be selected to balance the field coverage and accuracy; L*a*b* color space can provide better segmentation; hyperspectral camera imagery can provide good discrimination; deep learning with data augmentation and transfer learning models can be used to reduce the computational time and resources; the stand count methodology that is successful with corn and cotton could be extended to other row crops and horticultural crops; and application of direct image processing and use of open-source platforms is required for stakeholder participation. The review will be helpful to the farmers, producers, and researchers in selecting and employing the UAV-based algorithms for evaluating plant stand count.