Hyperspectral imagery to monitor crop nutrient status within and across growing seasons

Liu, Nanfeng; Townsend, Philip A.; Naber, Mack R.; Bethke, Paul C.; HILLS, WILLIAM B.; Wang, Yi

doi:10.1016/j.rse.2021.112303

Cited by 66 publications

(26 citation statements)

References 127 publications

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“…The wavelength of 870 nm was generally associated with fat [ 8 ], which is an important nutrient in seeds, and its model effect showed that that difference mainly lay in the seeds of sickle alfalfa and alfalfa, with hybrid alfalfa similar to alfalfa. The wavelength of 970 nm was generally related to moisture [ 45 ], and its effects on alfalfa and sickle alfalfa were opposite, with alfalfa similar to hybrid alfalfa, which may have been due to the higher moisture content of sickle alfalfa than of those in the others. In short, there was a closer relationship between alfalfa and hybrid alfalfa seeds at the bands of UV (365 nm) and NIR (870 nm), while sickle alfalfa and alfalfa seeds were similar at the VL region (515 and 570 nm).…”

Section: Discussionmentioning

confidence: 99%

Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning

Jia

Sun

et al. 2022

Sensors

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Multispectral imaging (MSI) has become a new fast and non-destructive detection method in seed identification. Previous research has usually focused on single models in MSI data analysis, which always employed all features and increased the risk to efficiency and that of system cost. In this study, we developed a stacking ensemble learning (SEL) model for successfully identifying a single seed of sickle alfalfa (Medicago falcata), hybrid alfalfa (M. varia), and alfalfa (M. sativa). SEL adopted a three-layer structure, i.e., level 0 with principal component analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) as models of dimensionality reduction and feature extraction (DRFE); level 1 with support vector machine (SVM), multiple logistic regression (MLR), generalized linear models with elastic net regularization (GLMNET), and eXtreme Gradient Boosting (XGBoost) as basic learners; and level 3 with XGBoost as meta-learner. We confirmed that the values of overall accuracy, kappa, precision, sensitivity, specificity, and sensitivity in the SEL model were all significantly higher than those in basic models alone, based on both spectral features and a combination of morphological and spectral features. Furthermore, we also developed a feature filtering process and successfully selected 5 optimal features out of 33 ones, which corresponded to the contents of chlorophyll, anthocyanin, fat, and moisture in seeds. Our SEL model in MSI data analysis provided a new way for seed identification, and the feature filter process potentially could be used widely for development of a low-cost and narrow-channel sensor.

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Section: Discussionmentioning

confidence: 99%

Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning

Jia

Sun

et al. 2022

Sensors

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“…For further investigation in tracking the nutritional status of lettuce grown in aquaponics, the leaf spectral reflectance properties were measured. The measurement of spectral reflectance properties has been demonstrated to be a very effective and reliable method for detecting and predicting the nutritional status of plants [46,47]. Pacumbaba Jr and Beyl [48] reported that leaf spectral reflectance measurements at the near-infrared (NIR) region (wavelength of 401.67 and 780.11 nm) have shown to be a valuable tool to identify the nutritional status of lettuce.…”

Section: Spectral Reflectance Measurement Of Lettuce Leavesmentioning

confidence: 99%

“…In the visible and middle infrared wavelength regions, the reflectance of lettuce grown in aquaponics was increased compared to lettuce grown in control system. The reflectance increase could be due to the decrease in nitrogen concentration in aquaponic lettuce [47]. The reason for this phenomenon is that the decrease in nitrogen concentrations leads to decreased chlorophyll content and photosynthesis, and thus decreases the visible light absorption, which causes an increase in the reflectance.…”

Section: Changes In Spectral Reflectance Under Npk Levelsmentioning

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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“…Hyperspectral sensors are moderately adopted for simplifying crop monitoring methods [104]. In a hyperspectral system, the camera acquires data from more wavelengths than the three commonly measured wavelengths (400-1300; 400-2350; and 1400-2350 nm wavelengths) [105]. Due to decreasing technology prices, the method is now economical and has thus become accessible to users globally.…”

Section: Hyperspectral Sensorsmentioning

confidence: 99%

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

et al. 2022

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Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters.

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Hyperspectral imagery to monitor crop nutrient status within and across growing seasons

Cited by 66 publications

References 127 publications

Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning

Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning

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

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

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