Plant Counting of Cotton from UAS Imagery Using Deep Learning-Based Object Detection Framework

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 18; p. 2981
• Main Authors: Oh, Sungchan, Chang, Anjin, Ashapure, Akash, Jung, Jinha, Dube, Nothabo, Maeda, Murilo, Gonzalez, Daniel, Landivar, Juan
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.09.2020
• Subjects: agriculture; algorithms; automation; canopy; China; cotton; data collection; detection; geometry; information; labor; lighting; measurement; photogrammetry; plant count; plant population assessment; planting; population; remote sensing; seedlings; unmanned aerial vehicles; unmanned aircraft systems; variability; China

Assessing plant population of cotton is important to make replanting decisions in low plant density areas, prone to yielding penalties. Since the measurement of plant population in the field is labor intensive and subject to error, in this study, a new approach of image-based plant counting is propo...