Hyperspectral imaging techniques for rapid detection of nutrient content of hydroponically grown lettuce cultivars

Eshkabilov, Sulaymon; Lee, Arim; Sun, Xiaorui; Lee, Chiwon W.; Simsek, Halis

doi:10.1016/j.compag.2020.105968

Cited by 53 publications

(43 citation statements)

References 31 publications

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“…Thus, developing an accurate, automatic, non-contact, and non-destructive technique to detect phytonutrient deficiency is substantially crucial [10,11]. Different non-destructive methods have been proposed for detecting plant nutrient deficiencies, including portable chlorophyll and nitrogen meters such as the SPAD-502 (Soil Plant Analysis Development), hyperspectral imaging, and in situ measurements using spectral remote sensing [12]. Besides being complicated in data processing, they require advanced, high, and costly imaging techniques and are only suitable for specific circumstances.…”

Section: Introductionmentioning

confidence: 99%

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

et al. 2022

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In the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. This study aimed to integrate color imaging and deep convolutional neural networks (DCNNs) to diagnose the nutrient status of lettuce grown in aquaponics. Our approach consists of multi-stage procedures, including plant object detection and classification of nutrient deficiency. The robustness and diagnostic capability of proposed approaches were evaluated using a total number of 3000 lettuce images that were classified into four nutritional classes—namely, full nutrition (FN), nitrogen deficiency (N), phosphorous deficiency (P), and potassium deficiency (K). The performance of the DCNNs was compared with traditional machine learning (ML) algorithms (i.e., Simple thresholding, K-means, support vector machine; SVM, k-nearest neighbor; KNN, and decision Tree; DT). The results demonstrated that the deep proposed segmentation model obtained an accuracy of 99.1%. Also, the deep proposed classification model achieved the highest accuracy of 96.5%. These results indicate that deep learning models, combined with color imaging, provide a promising approach to timely monitor nutrient status of the plants grown in aquaponics, which allows for taking preventive measures and mitigating economic and production losses. These approaches can be integrated into embedded devices to control nutrient cycles in aquaponics.

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Section: Introductionmentioning

confidence: 99%

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

et al. 2022

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“…In recent years, hyperspectral imaging (HSI) technology has provided an opportunity for rapid crop classi cation and content detection (Ebbers et al, 2002;Eshkabilov et al, 2021;Ferreira et al, 2019). The advantage of HSI technology is that it can reveal the relationship between spectral absorption and different chemical components, and provide the spatial distribution of the corresponding chemical components (Curran, 1989;Griffel et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Quality Identification of Kale Cultivated under Different Light Treatments by Hyperspectral Imaging Technology

Yao

Jiang

Wu³

et al. 2022

Preprint

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The development of a new method to accurately and non-destructively identify the quality of vegetables cultivated under different light treatments is urgent because traditional methods of quality identification are time-consuming, costly and destructively. A method based on hyperspectral imaging technology combined with machine learning was developed in this paper to rapidly identify the quality of kale cultivated under different light treatments in a plant factory. UV-A supplementation in different photoperiods was used to regulate the quality of kale by improving the contents of moisture, photosynthetic pigments and the phytochemicals accumulation, and the quality grades were first established based on these indicators obtained by the traditional method. Then, a non-destructive quality identification method was presented by constructing an identification model based on the hyperspectral images of kale leaves and machine learning. It was revealed that the accuracy of the identification model based on linear discriminant analysis reached a high value of 95% by employing different feature selection methods to optimize the model. The differences of model accuracy were also investigated when the reflectance spectra extracted from different regions of interest (ROIs) such as whole leaf, mesophyll and leaf veins were used for modeling. It was shown that the quality identification models had higher accuracy when the mesophyll was used as ROI than others ROIs, indicating that the selection of ROI-mesophyll was an efficient way to improve the accuracy of the model. These results demonstrate that the proposed method combining hyperspectral imaging technology and machine learning can be used to rapidly and accurately identify the quality of vegetables cultivated under different light treatments.

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“…Among the non-destructive methods, the digital image processing technique has been employed effectively for predicting the N status of crops. For instance, using a hyperspectral imaging technique of freshly cut lettuce leaves was found to be not only highly accurate in nutrient level determination but also in predicting nutrient changes with respect to the amount of applied fertilizers, evaluating contamination, and determining shelf-life [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Models to Determine Nutrient Concentration in Hydroponically Grown Lettuce Cultivars (Lactuca sativa L.)

et al. 2021

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Deep learning (DL) and computer vision applications in precision agriculture have great potential to identify and classify plant and vegetation species. This study presents the applicability of DL modeling with computer vision techniques to analyze the nutrient levels of hydroponically grown four lettuce cultivars (Lactuca sativa L.), namely Black Seed, Flandria, Rex, and Tacitus. Four different nutrient concentrations (0, 50, 200, 300 ppm nitrogen solutions) were prepared and utilized to grow these lettuce cultivars in the greenhouse. RGB images of lettuce leaves were captured. The results showed that the developed DL’s visual geometry group 16 (VGG16) and VGG19 architectures identified the nutrient levels of lettuces with 87.5 to 100% accuracy for four lettuce cultivars, respectively. Convolution neural network models were also implemented to identify the nutrient levels of the studied lettuces for comparison purposes. The developed modeling techniques can be applied not only to collect real-time nutrient data from other lettuce type cultivars grown in greenhouses but also in fields. Moreover, these modeling approaches can be applied for remote sensing purposes to various lettuce crops. To the best knowledge of the authors, this is a novel study applying the DL technique to determine the nutrient concentrations in lettuce cultivars.

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Hyperspectral imaging techniques for rapid detection of nutrient content of hydroponically grown lettuce cultivars

Cited by 53 publications

References 31 publications

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

Quality Identification of Kale Cultivated under Different Light Treatments by Hyperspectral Imaging Technology

Deep Learning Models to Determine Nutrient Concentration in Hydroponically Grown Lettuce Cultivars (Lactuca sativa L.)

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