Determination of Specific Parameters for Early Detection of Botrytis cinerea in Lettuce

In horticulture, the demand for efficient farming processes and food industries increases rapidly. Plant diseases cause severe crop production and economic losses. Therefore, early detection and identification of the diseases in plants are critical. This study aimed to determine the specific parameters for early detection of Botrytis cinerea in lettuce. The lettuce “Little Gem” was inoculated with B. cinerea isolate spore suspension and disc to evaluate the plant response to inner and outer infection, respectively. The non-destructive measurements of leaf spectral reflectance indices and biochemical compounds (phenols, proteins, DPPH, FRAP, chlorophyll, and carotenoids) were used to evaluate the plant physiological response to inoculation with B. cinerea after 12, 18, 36, 60, and 84 h. Our data showed that lettuce responded differently to inner and outer inoculation with B. cinerea. Therefore, the findings of this study allow for the inoculation method to be chosen to determine the early plant response to infection with B. cinerea according to specific leaf spectral reflectance indexes and phytochemicals in further research.

Pepper Plants Leaf Spectral Reflectance Changes as a Result of Root Rot Damage

Symptoms of root stress are hard to detect using non-invasive tools. This study reveals proof of concept for vegetation indices’ ability, usually used to sense canopy status, to detect root stress, and performance status. Pepper plants were grown under controlled greenhouse conditions under different potassium and salinity treatments. The plants’ spectral reflectance was measured on the last day of the experiment when more than half of the plants were already naturally infected by root disease. Vegetation indices were calculated for testing the capability to distinguish between healthy and root-damaged plants using spectral measurements. While no visible symptoms were observed in the leaves, the vegetation indices and red-edge position showed clear differences between the healthy and the root-infected plants. These results were achieved after a growth period of 32 days, indicating the ability to monitor root damage at an early growing stage using leaf spectral reflectance.

Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves

The purpose of this study is to determine a method for quickly and accurately estimating the chlorophyll content of peanut plants at different plant densities. This was explored using leaf spectral reflectance to monitor peanut chlorophyll content to detect sensitive spectral bands and the optimum spectral indicators to establish a quantitative model. Peanut plants under different plant density conditions were monitored during three consecutive growth periods; single-photon avalanche diode (SPAD) and hyperspectral data derived from the leaves under the different plant density conditions were recorded. By combining arbitrary bands, indices were constructed across the full spectral range (350–2500 nm) based on blade spectra: the normalized difference spectral index (NDSI), ratio spectral index (RSI), difference spectral index (DSI) and soil-adjusted spectral index (SASI). This enabled the best vegetation index reflecting peanut-leaf SPAD values to be screened out by quantifying correlations with chlorophyll content, and the peanut leaf SPAD estimation models established by regression analysis to be compared and analyzed. The results showed that the chlorophyll content of peanut leaves decreased when plant density was either too high or too low, and that it reached its maximum at the appropriate plant density. In addition, differences in the spectral reflectance of peanut leaves under different chlorophyll content levels were highly obvious. Without considering the influence of cell structure as chlorophyll content increased, leaf spectral reflectance in the visible (350–700 nm): near-infrared (700–1300 nm) ranges also increased. The spectral bands sensitive to chlorophyll content were mainly observed in the visible and near-infrared ranges. The study results showed that the best spectral indicators for determining peanut chlorophyll content were NDSI (R520, R528), RSI (R748, R561), DSI (R758, R602) and SASI (R753, R624). Testing of these regression models showed that coefficient of determination values based on the NDSI, RSI, DSI and SASI estimation models were all greater than 0.65, while root mean square error values were all lower than 2.04. Therefore, the regression model established according to the above spectral indicators was a valid predictor of the chlorophyll content of peanut leaves.

Prediction of N, P, and K Contents in Sugarcane Leaves by VIS-NIR Spectroscopy and Modeling of NPK Interaction Effects

Methods were studied to predict the N, P, and K contents in sugarcane leaves quickly and accurately at the seedling, tillering, and elongation stages from leaf spectral reflectance. A total of 117 valid leaf samples were used to obtain leaf spectral reflectance with an indoor VIS-NIR spectrophotometer. Using the spectral data processed by CARS-PCA as an independent variable, a six-fold cross-validated PLS model for N, P, and K contents was established. The R2 values of the CARS-PCA-PLS models for N, P, and K prediction were 0.859, 0.677, and 0.932, respectively. Correlation analysis of the predicted N, P, and K contents was performed to explore the interaction effects between N, P, and K. To simulate the interaction effects among the three nutrients, 19 factors were assumed, including possible linear, quadratic, and cubic relationships between N, P, and K, and multi-factor cubic polynomial PLS and MLR regression models were established from those factors. In the modified MLR models, the determinants of N, P, and K were 0.891, 0.802, and 0.944, respectively, which improved the performance of the models by 3.7%, 18.5%, and 1.3%, respectively, compared with the CARS-PCA-PLS models, which were based on the spectral reflectance data. The results showed that application of VIS-NIR spectra combined with interaction effects between the nutrients could effectively predict the N, P, and K contents in the early and middle growth stages of sugarcane.HighlightsCompetitive adaptive reweighted sampling (CARS) was adopted to select wavebands for nutrient prediction.N, P, and K interaction effects were simulated with 19 factors, including linear, quadratic, and cubic relationships.The interaction factors were used in multiple linear regression models, and improved prediction was achieved. Keywords: CARS-PCA, Interaction effect, NPK, Sugarcane, VIS-NIR spectroscopy.