ABSTRACT
Drought and water scarcity due to global warming, climate change, and social development have been the most death-defying threat to global agriculture production for the optimization of water and food security. Reflectance indices obtained by an Analytical Spectral Device (ASD) Spec 4 hyperspectral spectrometer from tomato growth in two soil texture types exposed to four water stress levels (70-100% FC, 60-70% FC, 50-60% FC, and 40-50% FC) was deployed to schedule irrigation and management of crops' water stress. The treatments were replicated four times in a randomized complete block design (RCBD) in a 2 × 4 factorial experiment. Water stress treatments were monitored with Time Domain Reflectometer (TDR) every 12 h before and after irrigation to maintain soil water content at the desired (FC%). Soil electrical conductivity (Ec) was measured daily throughout the growth cycle of tomatoes in both soil types. Ec was revealing a strong correlation with water stress at R2 above 0.95 p < 0.001. Yield was measured at the end of the end of the growing season. The results revealed that yield had a high correlation with water stress at R2 = 0.9758 and 0.9816 p < 0.01 for sandy loam and silty loam soils, respectively. Leaf temperature (LT °C), relative leaf water content (RLWC), leaf chlorophyll content (LCC), Leaf area index (LAI), were measured at each growth stage at the same time spectral reflectance data were measured throughout the growth period. Spectral reflectance indices used were grouped into three: (1) greenness vegetative indices; (2) water overtone vegetation indices; (3) Photochemical Reflectance Index centered at 570 nm (PRI570), and normalized PRI (PRInorm). These reflectance indices were strongly correlated with all four water stress indicators and yield. The results revealed that NDVI, RDVI, WI, NDWI, NDWI1640, PRI570, and PRInorm were the most sensitive indices for estimating crop water stress at each growth stage in both sandy loam and silty loam soils at R2 above 0.35. This study recounts the depth of 858 to 1640 nm band absorption to water stress estimation, comparing it to other band depths to give an insight into the usefulness of ground-based hyperspectral reflectance indices for assessing crop water stress at different growth stages in different soil types.
Subject(s)
Soil , Solanum lycopersicum , Dehydration , Hyperspectral Imaging , SandABSTRACT
Non-destructive, simple and fast techniques for identifying authentic palm oil and those adulterated with Sudan dyes using portable NIR spectroscopy would be very beneficial to West Africa countries and the world at large. In this study, a portable NIR spectroscopy coupled with multivariate models were developed for detecting palm oil adulteration. A total of 520 samples of palm oil were used comprising; 40 authentic samples together with 480 adulterated samples containing Sudan dyes (I, II, III, IV of 120 samples each). Multiplicative scatter correction (MSC) preprocessing technique plus Principal component analysis (PCA) was used to extract relevant spectral information which gave visible cluster trends for authentic samples and adulterated ones. The performance of Linear discriminant analysis (LDA) and Support vector machine (SVM) were compared, and SVM showed superiority over LDA. The optimised results by cross-validation revealed that MSC-PCA + SVM gave an identification rate above 95% for both calibration and prediction sets. The overall results show that portable NIR spectroscopy together with MSC-PCA + SVM model could be used successfully to identify authentic palm oils from adulterated ones. This would be useful for quality control officers and consumers to manage and control Sudan dyes adulteration in red palm oil.
