Non-Destructive Monitoring of Crop Fresh Weight and Leaf Area with a Simple Formula and a Convolutional Neural Network

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 20; p. 7728
• Main Authors: Moon, Taewon, Kim, Dongpil, Kwon, Sungmin, Ahn, Tae In, Son, Jung Eek
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 12.10.2022; MDPI
• Subjects: Algorithms; artificial intelligence; Calibration; Crops; Data collection; Data entry; Deep learning; Greenhouses; Growth factors; Information management; Investigations; machine learning; Measurement methods; Moisture content; Monitoring; Monitoring systems; Neural networks; Nondestructive testing; Parameters; Peppers; plant environment; precision agriculture; Radiation; Vegetables; Versatility; South Korea; United States > US; Netherlands

Crop fresh weight and leaf area are considered non-destructive growth factors due to their direct relation to vegetative growth and carbon assimilation. Several methods to measure these parameters have been introduced; however, measuring these parameters using the existing methods can be difficult....