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

Elvanidi, A.; Katsoulas, Ν.; Bartzanas, Thomas; Ferentinos, Konstantinos P.; Kittas, C.

doi:10.1007/s11119-016-9492-3

Cited by 31 publications

(20 citation statements)

References 34 publications

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“…For spectral analysis, the normalized difference vegetation index (NDVI) [52] is one of the most common and widely used index reported in scientific literature on vegetable crops. NDVI has been tested for the assessment of plant water status [68], leaf chlorophyll content [69], plant mineral nutrition [67], and plant biotic and abiotic stress [48,70,71]. In the GNDVI version, the red band is replaced by the green one [72].…”

Section: Performance Indicesmentioning

confidence: 99%

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Tripodi¹,

Massa²,

Venezia³

et al. 2018

Agronomy

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Abstract:Increasing the ability to investigate plant functions and structure through non-invasive methods with high accuracy has become a major target in plant breeding and precision agriculture. Emerging approaches in plant phenotyping play a key role in unraveling quantitative traits responsible for growth, production, quality, and resistance to various stresses. Beyond fully automatic phenotyping systems, several promising technologies can help accurately characterize a wide range of plant traits at affordable costs and with high-throughput. In this review, we revisit the principles of proximal and remote sensing, describing the application of non-invasive devices for precision phenotyping applied to the protected horticulture. Potentiality and constraints of big data management and integration with "omics" disciplines will also be discussed.

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Section: Performance Indicesmentioning

confidence: 99%

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Tripodi¹,

Massa²,

Venezia³

et al. 2018

Agronomy

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“…Two units comprised of two crop lines each (18 plants per line) were used. The precise cultivation practices followed are described in References [1,7,13].…”

Section: Experimental Set-upmentioning

confidence: 99%

“…to predict green biomass (R 2 > 0.70). Elvanidi et al [1,7,13] also showed that Modified red normalised vegetation and simple ration indices…”

Section: Introductionmentioning

confidence: 99%

“…The current development of computational hyperspectral machine vision systems allows us to build a real-time plant canopy health, growth, and quality monitoring multisensory platform equipped with remote technologies [5][6][7]. Hyperspectral machine vision systems allow recording in large scale, the spatial interaction of sunlight with crop canopies and plant leaves providing valuable information about plant growth and health status [6,8].…”

Section: Introductionmentioning

confidence: 99%

“…In order to provide more precise information for stress detection, certain parts of the spectrum can be combined to form reflectance indices (RIs) [2,7,9,10]. In this way, the spectral differences detected are amplified while the resulting RIs are used directly as a metric to quantify different aspects of plant physiology response.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automation for Water and Nitrogen Deficit Stress Detection in Soilless Tomato Crops Based on Spectral Indices

2018

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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.

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Spectral Reflectance Measurements

Stamford,

Kasznicki,

Lawson

2024

Methods in Molecular Biology

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

Cited by 31 publications

References 34 publications

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Automation for Water and Nitrogen Deficit Stress Detection in Soilless Tomato Crops Based on Spectral Indices

Spectral Reflectance Measurements

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