Prediction and classification of sugar content of sugarcane based on skin scanning using visible and shortwave near infrared

• Published in: Biosystems engineering Vol. 115; no. 2; pp. 154 - 161
• Main Authors: Mat Nawi, Nazmi, Chen, Guangnan, Jensen, Troy, Mehdizadeh, Saman Abdanan
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.06.2013; Elsevier
• Subjects: Agricultural machinery and engineering; Agronomy. Soil science and plant productions; Biological and medical sciences; engineering; Fundamental and applied biological sciences. Psychology; Generalities. Biometrics, experimentation. Remote sensing; least squares; neural networks; prediction; reflectance; spectroscopy; sugar content; sugarcane; total soluble solids; Bioengineering; Monocotyledones; Vegetals; Biochemical compound; Prediction; Tropical crop; C4-Type; Electromagnetic wave; Sugar plant; Short wave; Near infrared radiation; Engineering; Saccharum officinarum; Gramineae; Content; Angiospermae; Classification; Spermatophyta; Agriculture; Sugar cane; Carbohydrate; Skin; Sugar

The potential application of a visible and shortwave near infrared (Vis/SWNIR) spectroscopic technique as a low cost alternative to predict sugar content based on skin scanning was evaluated. Two hundred and ninety one internode samples representing three different commercial sugarcane varieties wer...