title: [Prediction of chlorophyll content of leaves of oil camelliae after being infected with anthracnose based on Vis/NIR spectroscopy].
abstract: The prediction model of chlorophyll content of leaves in canopies of oil camelliae under disease was explored and built by analyzing the Vis/NIR spectroscopy characteristics of oil camelliae canopies after being injected with anthracnose. Through field survey of disease index (DI), chlorophyll content and spectral data of leaves in canopies surviving different severity of disease were acquired. The first order differential of spectral data combined with moving average filter was pretreated. The prediction model of BP neural network of chlorophyll content was built by extracting sensitive wave band from spectral resample data. The results showed that with the disease being aggravated, reflection peaks and valleys of spectra of oil camelliae canopies in visible-light region vanished gradually, steep red edges from red light to near infrared leveled little by little, and reflectivity of healthy oil camelliae was far larger than that of ill ones. The sensitive wave band of absorption and reflection of chlorophyll lay in the region of 84-512, 533-565, 586-606 and 672-724 nm. The correlation coefficient r and RMSE between predictive values calculated from BP neural network using sensitive wave band as input variables and observed values was 0.9921 and 0.0458 respectively. It was therefore feasible to utilize Vis/NIR spectroscopy technology to forecast the chlorophyll content of oil camelliae after being infected with anthracnose.
authors: Hunan Provincial Key Laboratory of Forestry Biotechnology, Central South University of Forestry and Technology, Changsha 410004, China.
authors: Nan
authors: N
authors: Wu
authors: Jun-ang
authors: JA
authors: Liu
authors: Guo-ying
authors: GY
authors: Zhou
authors: Rui-kun
authors: RK
authors: Yan
authors: Lei
authors: L
authors: Zhang
mesh_terms: D028244:Camellia / Q000737:chemistry* / Q000382:microbiology; D002734:Chlorophyll / Q000032:analysis*; D016571:Neural Networks, Computer; D010935:Plant Diseases; D018515:Plant Leaves / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared
chemical_list: D002734:Chlorophyll