Detection of tomato water stress based on terahertz spectroscopy

Abstract

China’s tomato cultivation area is nearly 15 thousand km2, and its annual tomato output is about 55 million tons, accounting for 7% of its total vegetable production. Because of the high drought sensitivity of tomatoes, water stress inhibits their nutrient uptake, leading to a decrease in tomato quality and yield. Therefore, the rapid, accurate and non-destructive detection of water status is important for scientifically and effectively managing tomato water and fertilizer, improving the efficiency of water resource utilization, and safeguarding tomato yield and quality. Because of the extreme sensitivity of terahertz spectroscopy to water, we proposed a tomato leaf moisture detection method based on terahertz spectroscopy and made a preliminary exploration of the relationship between tomato water stress and terahertz spectral data. Tomato plants were grown at four levels of water stress. Fresh tomato leaves were sampled at fruit set, moisture content was calculated, and spectral data were collected through a terahertz time-domain spectroscope. The raw spectral data were smoothed using the Savitzky–Golay algorithm to reduce interference and noise. Then the data were divided by the Kennard–Stone algorithm and the sample set was partitioned based on the joint X-Y distance (SPXY) algorithm into a calibration set and a prediction set at a ratio of 3:1. SPXY was found to be the better approach for sample division. On this basis, the stability competitive adaptive re-weighted sampling algorithm was used to extract the feature frequency bands of moisture content, and a multiple linear regression model of leaf moisture content was established under the single dimensions of power, absorbance and transmittance. The absorbance model was the best, with a prediction set correlation coefficient of 0.9145 and a root mean square error of 0.1199. To further improve the modeling accuracy, we used a support vector machine (SVM) to establish a tomato moisture fusion prediction model based on the fusion of three-dimensional terahertz feature frequency bands. As water stress intensified, the power and absorbance spectral values both declined, and both were significantly and negatively correlated with leaf moisture content. The transmittance spectral value increased gradually with the intensification of water stress, showing a significant positive correlation. The SVM-based three-dimensional fusion prediction model showed a prediction set correlation coefficient of 0.9792 and a root mean square error of 0.0531, indicating that it outperformed the three single-dimensional models. Hence, terahertz spectroscopy can be applied to the detection of tomato leaf moisture content and provides a reference for tomato moisture detection.

Study on the detection of water status of tomato (Solanum lycopersicum L.) by multimodal deep learning

“…The experiments were conducted in the Venlo continuous glass greenhouse at Jiangsu University from June 2021 to January 2022, and the tomato variety used was “Pink Crown F1” (Shouhe). The substrate used was perlite and the nutrient solution formulation was “Yamazaki Nutrient Solution Formula” ( Zhang X. et al., 2021 ). Five irrigation levels were set, with five tomato plants at each irrigation level, irrigated at 50%, 75%, 100%, 125%, and 150% of the reference evapotranspiration of tomatoes, corresponding to the water status of tomatoes as severely irrigated deficit, slightly irrigated deficit, moderately irrigated, slightly over-irrigated, and severely over-irrigated.…”

“…Infrared thermography detects the temperature information of the crop and thus determines the water status of the crop. NIR spectroscopy and infrared thermography for crop moisture detection are based on the principle of thermal radiation, which is influenced by environmental changes ( Zhang X. et al., 2021 ). It is possible to determine the water status of a crop based on its 3-D morphology, but it is difficult and complicated to obtain 3-D images and process them ( Zhao et al., 2016 ).…”

Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review

“…Although the use of THz in food safety and agriculture is still in its early stages due to high research costs, it shows potential for detecting food adulteration and additives and classifying genetically modified plants [110][111][112][113]. In food safety, THz is effective for identifying food adulteration and additives, while in agriculture, it aids in detecting crop moisture and sugar content [114,115]. However, the exploration of low-content components in fruits and vegetables remains an ongoing area of study.…”

Far IR field generation by mixing of cosh-Gaussian and dark hollow Gaussian laser in encapsulated plasma

“…THz frequency band is not only significant for identification and sample analysis but also promises extensive utility in emerging domains such as 6G technology to achieve a remarkable rapid data transfer rate up to 6.4 Terabit/s using a 20 km fiber optic connection and 54 m wireless setup [1]. Concurrently, numerous research teams are actively engaged in exploring agricultural applications, such as the detection of pesticides in soil [2] and identifying grass seed infestations [3], aimed at averting potential health hazards, including biomedical applications, spectroscopy [4,5], secure fingerprinting, neuromodulation [6] and signal processing [7], etc.…”