Automation for water and nitrogen deficit stress detection in soilless tomato crops based on spectral indices

Water and nitrogen deficit stress are some of the most important growth limiting factors in crop production. Several methods have been used to quantify the impact of water and nitrogen deficit stress on plant physiology. However, by performing machine learning with hyperspectral sensor data, crop physiology management systems are integrated into real artificial intelligence systems, providing richer recommendations and insights into implementing appropriate irrigation and environment control management strategies. In this study, the Classification Tree model was used to group complex hyperspectral datasets in order to provide remote visual results about plant water and nitrogen deficit stress. Soilless tomato crops are grown under varying water and nitrogen regimes. The model that we developed was trained using 75% of the total sample dataset, while the rest (25%) of the data were used to validate the model. The results showed that the combination of MSAVI, mrNDVI, and PRI had the potential to determine water and nitrogen deficit stress with 89.6% and 91.4% classification accuracy values for the training and testing samples, respectively. The results of the current study are promising for developing control strategies for sustainable greenhouse production. © 2018 by the authors

Contribution of hyperspectral imaging to monitor water content in soilless growing cucumber crop

Drought stress in soilless cultivation plants causes various symptoms. Until now, in the majority of the greenhouses irrigation control is based either on water content measurements in root zone or on air temperature and relative humidity records both performed at a single point located at the center of the cultivated area. However, this methodology is not workable in the recent infrastructures, since their size has greatly increased and large water consumption gradient has been performed. Thus, direct and real-time monitoring systems of plant response in different location within the greenhouse are required. Hyperspectral machine vision is a non-contact and nondestructive sensing technology that pave the way for the commercialisation of robotic machine vision. The objective of this work was to map through hyperspectral camera the water content gradient observed in cucumbers cultivated in a greenhouse. Plants of different irrigation regimes were imaged in different indoor positions. The gradient of plant physiology response was also studied. During the measurements, the impact of shadows to the targeted object was eliminated by placing a black surface as background. The results received within the framework of the current analysis perform a sound way for proceeding in more sustainable irrigation control system. © 2020 International Society for Horticultural Science. All rights reserved

Calibration methodology of a hyperspectral imaging system for greenhouse plant water status assessment

Although much progress has been made on optimizing plant water supply, only a limited number of methods use plant-based physiological indicators to detect plant water stress and adapt irrigation scheduling accordingly. In addition, even fewer indicators can be estimated remotely without contact and effect on plant development. Hyperspectral imaging could be an accurate technique to detect plant water status, taking into account crop characteristics. In this work, a methodology of hyperspectral imaging calibration and acquisition is presented. The technique uses the crop reflectance characteristics from 400 to 1000 nm and incorporates the appropriate radiometric and geometric corrections. It was confirmed that sensor's dark current noise is proportional to exposure time and frame rate values, while CCD silicon detector is wavelength-dependent. The basic statistical parameters of mean and standard deviation values were used to estimate spatial and spectral correlation of each band on the extracted areas/pixels of interest. Several statistical techniques were used for the selection of optimal features that would lead to the development of appropriate plant water stress indices that could be used for incipient water stress detection in optimal irrigation scheduling systems. The images were clearer when the exposure time was 130 ms and the speed of the scanner was set at 0.16 mm s-1 with a frame rate of 500 Hz. NDVI, rNDVI and mrNDVI indices proved to be independent of light signal variation

Crop reflectance measurements for nitrogen deficiency detection in a soilless tomato crop

Early detection of nitrogen deficit stress is essential to effectively and precisely manage crop production under greenhouse conditions. This article demonstrates the use of hyperspectral machine vision as a non-contact technique for detecting crop nitrogen deficit in a soilless tomato crop. Three different levels of nitrogen concentration were applied in tomato plants grown in a growth chamber under controlled environment conditions. The results demonstrate that crop reflectance increased due to nitrogen deficiency, mostly in the wavelength bands between 775 nm–850 nm and 910 nm–960 nm. Based on the reflectance measurements several reflectance indices were calculated and correlated with the tomato leaf chlorophyll or nitrogen content and with the leaf photosynthesis rate (As). The results showed that when the As and the chlorophyll content values changed more than 0.5 μmol m−2 s−1 and 2.8 μg cm−2, respectively, the photochemical reflectance index (PRI) and the transformed chlorophyll absorption in reflectance index (TCARI) values varied more than 0.05 and 2.8, respectively. In addition, the results showed that for N changes higher than 0.20%, the optimised soil adjusted vegetation index (OSAVI) and the modified soil adjusted vegetation index (MSAVI) values varied more than 0.05 and 0.25, respectively. A new spectral index (background adjustment nitrogen index – BANI) was developed and validated under experimental conditions for the estimation of tomato plant nitrogen concentration. © 2018 IAgr

Crop water status assessment in controlled environment using crop reflectance and temperature measurements

Crop water status is an important parameter for plant growth and yield performance in greenhouses. Thus, early detection of water stress is essential for efficient crop management. The dynamic response of plants to changes of their environment is called ‘speaking plant’ and multisensory platforms for remote sensing measurements offer the possibility to monitor in real-time the crop health status without affecting the crop and environmental conditions. Therefore, aim of this work was to use crop reflectance and temperature measurements acquired remotely for crop water status assessment. Two different irrigation treatments were imposed in tomato plants grown in slabs filed with perlite, namely tomato plants under no irrigation for a certain period; and well-watered plants. The plants were grown in a controlled growth chamber and measurements were carried out during August and September of 2014. Crop reflectance measurements were carried out by two types of sensors: (i) a multispectral camera measuring the radiation reflected in three spectral bands centred between 590–680, 690–830 and 830–1000 nm regions, and (ii) a spectroradiometer measuring the leaf reflected radiation from 350 to 2500 nm. Based on the above measurements several crop indices were calculated. The results showed that crop reflectance increased due to water deficit with the detected reflectance increase being significant about 8 h following irrigation withholding. The results of a first derivative analysis on the reflectance data showed that the spectral regions centred at 490–510, 530–560, 660–670 and 730–760 nm could be used for crop status monitoring. In addition, the results of the present study point out that sphotochemical reflectance index, modified red simple ratio index and modified ratio normalized difference vegetation index could be used as an indicator of plant water stress, since their values were correlated well with the substrate water content and the crop water stress index; the last being extensively used for crop water status assessment in greenhouses and open field. Thus, it could be concluded that reflectance and crop temperature measurements might be combined to provide alarm signals when crop water status reaches critical levels for optimal plant growth. © 2017, Springer Science+Business Media New York

Crop temperature measurements for crop water status identification in greenhouses

Proper irrigation management in hydroponics through efficient use of water and fertilizers is crucial to the production of high quality products. Low water supply rates may lead to crop water stress and reduction of production. The temperature of the plant has been identified as a good indication of plant water status and has been included in several crop water status related indices. However, measuring crop temperature is a complex task, as contact sensors must be small and can lose contact with the plant. Recently, remote sensing methods have offered a promising alternative for crop temperature measurements. This work was to assess the reliability of crop temperature measurements obtained by different infrared thermometers, either as single sensors (infrared thermograph and infrared thermocamera) or infrared thermometers connected to a wireless sensor network (WSN), and this work was to compare and evaluate different crop water status indices which are based on crop temperature. Experiments were conducted in a hydroponic cultivation system with tomato crops. The crop water status indexes used were: stress degree day (SDD), temperature stress day (TSD) and crop water stress index (CWSI). The indices were evaluated using plant physiological characteristics like crop transpiration, sap flow and crop photosynthesis, and were able to early detect crop water stress. The goal was to study the effective performance of the thermal indicators in detecting crop water stress on greenhouse conditions, in which, air temperature, humidity, vapor pressure deficit and solar radiation vary greatly. Measurements from all single sensors and the WSN were used for that purpose. It was concluded that crop temperature may be a proper indicator to detect water stress on a daily basis. On an hourly basis, different forms of stress cause an increase of crop temperature, therefore further analysis is required. © 2017 ISHS

Reflectance indices for the detection of water stress in greenhouse tomato (Solanum lycopersicum)

This study determined various water stress related indices, which are based on crop-reflected radiation in different wavebands, for early plant water stress detection in greenhouse tomato through portable spectroradiometer and multispectral camera. It also provides recommendations for the most appropriate reflectance indices to be used for irrigation scheduling in greenhouses. Reflectance indices were evaluated according to recent literature, including PRI, NDVI, WI, VOGREI and SRI. It was concluded that the near-infrared region was the most suitable spectrum for early water stress detection in greenhouse tomatoes while NDVI800 and rNDVI showed high correlation with soil water content with R2 values of 0.85 and 0.83, respectively. The results suggested that different protocol, based on hourly variation, must be conducted and reflectance indices based on hyperspectral camera measurements, should be studied, in order to create a proper tool for irrigation management in greenhouse plants

Implementation of the circular economy concept in greenhouse hydroponics for ultimate use of water and nutrients

The circular economy in agriculture aims to reduce waste while also making best use of residues by using economically viable processes and procedures to increase their value. In this study a two-level cascade cultivation system was set up under greenhouse conditions. The research was focused on the identification of crop species as secondary crops and the development/iterative optimization of cultivation practices. For this purpose, different crop-combinations with a primary and different secondary crops were investigated using different system-layouts. Measurements were carried out during two cultivation periods. During the 1st Period a combination of cucumber (Cucumis sativus) as primary crop, with rosemary (Rosmarinus officinalis), basil (Ocimum basilicum), and peppermint (Mentha piperita) as secondary crops, was evaluated. In the 2nd Period the drainage of tomato (Solanum lycopersicum) plants was re-used to irrigate spearmint (Mentha spicata), dill (Anethum graveolens), celery (Apium graveolens) and parsley (Petroselinum crispum) plants. In both periods, different fertigation management strategies based on the drainage solution of the primary crop were employed. The use of the cascade hydroponic system improved both crop water and nutrient use efficiency. Notably, the NO3 disposal was about 40% less as compared to a monoculture. Average fresh water consumption of secondary crop plants irrigated with diluted drainage solution was reduced by 30% in comparison to plants irrigated with fresh water. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Hyperspectral machine vision as a tool for water stress severity assessment in soilless tomato crop

Early detection of water deficit stress is essential for efficient crop management. In this study, hyperspectral machine vision was used as a non-contact technique for detecting changes in spectral reflectance of a soilless tomato crop grown under varying irrigation regimes. Four different irrigation treatments were imposed in tomato plants grown in slabs filled with perlite. The plants were grown in a growth chamber under controlled temperature and light conditions, and crop reflectance measurements were made using a hyperspectral camera to measure the radiation reflected by the crop from 400 nm to 1000 nm. The results showed that crop reflectance increased with increasing water deficit, and the detected reflectance increase was significant during the first day of irrigation was withheld. Based on the reflectance measurements, several crop indices were calculated and correlated with substrate volumetric water content and tomato leaf chlorophyll content. The results showed that when the modified red simple ratio tndex (mrSRI) and the modified red normalized vegetation index (mrNDVI) values increased by more than 2.5% and 23% respectively, the substrate volumetric water content decreased by more than 3%. In addition, when the Transformed Chlorophyll Absorption Reflectance Index (TCARI) value increased by about 16%, the leaf chlorophyll content decreased by about 3%. These results of the present study are promising for the development of a non-contact method for estimating plant water status in tomato crops grown under controlled environment. © 2017 IAgr

Assessment of crop water status by means of crop reflectance

Aim of this work is to present the procedure for precise plant reflectance measurements in order to estimate the plant water and chlorophyll status in a controlled environment. The method could be applied to conventional or organic greenhouse conditions. A hyperspectral camera was used to provide remotely plant reflectance measurements during periods with normal or low substrate water content. The optic sensor was calibrated into a light-controlled growth chamber. Reflectance measurements were carried out in tomato plants (Solanum lycopersicum 'Elpida') grown on perlite slabs. Well-irrigated pants were used as a reference point during the experimental period, while water stress was applied by withholding irrigation. Radiometric calibration includes the elimination of a variety of noise sources, such as photon noise, thermal noise, read out noise and quantisation noise. The proper number of lens aperture (f/) and exposure time (ms) ranges of the camera for the specific light signal conditions were evaluated, in order to achieve the most suitable readout values. Different algorithms and statistical methods (spectral threshold methods, supervised classification algorithms, unsupervised clustering algorithms) were used to detect and classify the object and extract the suitable information from the plant. Crop reflectance tended to increase as the substrate moisture content decreased from the first hours of irrigation pause. The combination of more than one spectral regions led to reflectance index estimations. The best indices for plant water stress detection were the mrNDVI and mrSRI values as they had the higher correlation with substrate water content. VOGREI and TCARI gave good correlation with plant chlorophyll and nitrogen content, with correlation coefficients up to 0.70