Influence of environmental and instrumental variables on the non-invasive prediction of Brix in pineapple using near infrared spectroscopy

The Brix content of pineapple fruit can be non-invasively predicted from the second derivative of near infrared reflectance spectra. Correlations obtained using a NIRSystems 6500 spectrophotometer through multiple linear regression and modified partial least squares analyses using a post-dispersive configuration were comparable with that from a pre-dispersive configuration in terms of accuracy (e.g. coefficient of determination, R2, 0.73; standard error of cross validation, SECV, 1.01°Brix). The effective depth of sample assessed was slightly greater using the post-dispersive technique (about 20 mm for pineapple fruit), as expected in relation to the higher incident light intensity, relative to the pre-dispersive configuration. The effect of such environmental variables as temperature, humidity and external light, and instrumental variables such as the number of scans averaged to form a spectrum, were considered with respect to the accuracy and precision of the measurement of absorbance at 876 nm, as a key term in the calibration for Brix, and predicted Brix. The application of post-dispersive near infrared technology to in-line assessment of intact fruit in a packing shed environment is discussed.

Remote sensing of nitrogen and water stress in wheat

Nitrogen (N) is the largest agricultural input in many Australian cropping systems and applying the right amount of N in the right place at the right physiological stage is a significant challenge for wheat growers. Optimizing N uptake could reduce input costs and minimize potential off-site movement. Since N uptake is dependent on soil and plant water status, ideally, N should be applied only to areas within paddocks with sufficient plant available water. To quantify N and water stress, spectral and thermal crop stress detection methods were explored using hyperspectral, multispectral and thermal remote sensing data collected at a research field site in Victoria, Australia. Wheat was grown over two seasons with two levels of water inputs (rainfall/irrigation) and either four levels (in 2004; 0, 17, 39 and 163 kg/ha) or two levels (in 2005; 0 and 39 kg/ha N) of nitrogen. The Canopy Chlorophyll Content Index (CCCI) and modified Spectral Ratio planar index (mSRpi), two indices designed to measure canopy-level N, were calculated from canopy-level hyperspectral data in 2005. They accounted for 76% and 74% of the variability of crop N status, respectively, just prior to stem elongation (Zadoks 24). The Normalised Difference Red Edge (NDRE) index and CCCI, calculated from airborne multispectral imagery, accounted for 41% and 37% of variability in crop N status, respectively. Greater scatter in the airborne data was attributable to the difference in scale of the ground and aerial measurements (i.e., small area plant samples against whole-plot means from imagery). Nevertheless, the analysis demonstrated that canopy-level theory can be transferred to airborne data, which could ultimately be of more use to growers. Thermal imagery showed that mean plot temperatures of rainfed treatments were 2.7 °C warmer than irrigated treatments (P < 0.001) at full cover. For partially vegetated fields, the two-Dimensional Crop Water Stress Index (2D CWSI) was calculated using the Vegetation Index-Temperature (VIT) trapezoid method to reduce the contribution of soil background to image temperature. Results showed rainfed plots were consistently more stressed than irrigated plots. Future work is needed to improve the ability of the CCCI and VIT methods to detect N and water stress and apply both indices simultaneously at the paddock scale to test whether N can be targeted based on water status. Use of these technologies has significant potential for maximising the spatial and temporal efficiency of N applications for wheat growers. ‘Ground–breaking Stuff’- Proceedings of the 13th Australian Society of Agronomy Conference, 10-14 September 2006, Perth, Western Australia.