A review of unmanned aerial vehicle-based methods for plant stand count evaluation in row crops

Plant stand count helps in estimating the yield and evaluating planter’s efficiency seed quality. Traditional methods of counting plants by manual measurement are time-consuming, laborious, error-prone. In contrast, ground-based sensing limited to smaller spaces. High spatial resolution images obtained from unmanned aerial vehicles (UAV) can be used conjunction with computer vision algorithms evaluate plant count, as it directly influences yield. spite importance high-throughput row crop agriculture, no synthesized information this specific subject matter is available. Therefore, objective paper was perform a systematic literature review current studies that focus on using UAV imagery provide well-synthesized information, identify research gaps, suitable recommendations. study, comprehensive search performed three academic databases (Agricola, Web Science, Scopus), total 29 articles were found based terms selection criteria for review. From review, concluded that: an appropriate stage after emergence without canopy overlap necessary image acquisition; optimal flying height should selected balance field coverage accuracy; L*a*b* color space better segmentation; hyperspectral camera good discrimination; deep learning data augmentation transfer models reduce computational time resources; methodology successful corn cotton could extended other crops horticultural crops; application direct processing use open-source platforms required stakeholder participation. The will helpful farmers, producers, researchers selecting employing UAV-based count.