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Fusarium-damaged kernels (FDK) contain a wide spectrum of mycotoxins, affecting the quality and safety of wheat used as food and feed. At present, traditional methods to detect FDK are time-consuming and laborious. Therefore, we propose herein a new spectral classification index (NSCI) method that can provide simple and low-cost FDK detection by analysing spectra in the wavelength range 350–2500 nm. The proposed index was based on the spectral reflectance and its first derivative. Frequency histograms were plotted for each class of index value, and Gaussian curve fitting was carried out for each histogram. Wheat kernels were then classified by using the intersection of the Gaussian curves as a threshold. The classification of NSCI for spectral data obtained the detection accuracy of 0.97, with a specificity of 0.99, a sensibility of 0.93 and a training time of 15.07 s. Compared with other spectral indexes and machine learning methods, the NSCI was more equilibrated in terms of efficiency and accuracy. Meanwhile, the threshold could be tuned to adjust accuracy, sensitivity or specificity to satisfy different practical needs. We also applied the NSCI for kernel hyperspectral data in another year, and the classification results is promising. The proposed method has the potential for the rapid and simple detection of FDK in wheat. [ABSTRACT FROM AUTHOR]

Irrigation management continues to be an important issue for tomato cultivation, especially in plant factories. Accurate and timely assessment of tomato leaf water status is a key factor in enabling appropriate irrigation, which can save nutrition solution and labor. In recent decades, hyperspectral imaging has been widely used as a nondestructive measurement method in agriculture to obtain plant biological information. The objective of this research was to establish an approach to obtain the tomato leaf water status—specifically, the relative water content (WC) and equivalent water thickness (MC)—for five different tomato cultivars in real time by using hyperspectral imaging. The normalized difference vegetation index (NDVI) and two-band vegetation index (TBI) analyses were performed on the tomato leaf raw relative reflection (RAW), the inversion-logarithm relative reflection (LOG), and the first derivative of relative reflection (DIFF) from wavelengths of 900 nm to 1700 nm. The best regression model for WC assessment was obtained by TBI regression using DIFF at wavelengths of 1410 nm and 1520 nm, and the best regression model for MC assessment was obtained by NDVI regression using RAW at wavelengths of 1300 nm and 1310 nm. Higher model performance was obtained with MC assessment than with WC assessment. The results will help improve our understanding of the relationship between hyperspectral reflectance and leaf water status. [ABSTRACT FROM AUTHOR]

Irrigation management continues to be an important issue for tomato cultivation, especially in plant factories. Accurate and timely assessment of tomato leaf water status is a key factor in enabling appropriate irrigation, which can save nutrition solution and labor. In recent decades, hyperspectral imaging has been widely used as a nondestructive measurement method in agriculture to obtain plant biological information. The objective of this research was to establish an approach to obtain the tomato leaf water status—specifically, the relative water content (WC) and equivalent water thickness (MC)—for five different tomato cultivars in real time by using hyperspectral imaging. The normalized difference vegetation index (NDVI) and two-band vegetation index (TBI) analyses were performed on the tomato leaf raw relative reflection (RAW), the inversion-logarithm relative reflection (LOG), and the first derivative of relative reflection (DIFF) from wavelengths of 900 nm to 1700 nm. The best regression model for WC assessment was obtained by TBI regression using DIFF at wavelengths of 1410 nm and 1520 nm, and the best regression model for MC assessment was obtained by NDVI regression using RAW at wavelengths of 1300 nm and 1310 nm. Higher model performance was obtained with MC assessment than with WC assessment. The results will help improve our understanding of the relationship between hyperspectral reflectance and leaf water status.