Applicability of Technological Tools for Digital Agriculture with a Focus on Estimating the Nutritional Status of Plants
Abstract
:1. Introduction
Precision Agriculture
2. Contribution of Artificial Intelligence to Precision Agriculture
3. Digital Agriculture and Precision Agriculture: Concepts, Differences, and Contributions to Plant Nutritional Assessment
3.1. What Is Digital Agriculture?
3.2. How Does It Differ from Precision Agriculture?
3.3. Why Can Digital Agriculture Make Significant Contributions to Assessing Plant Nutritional Status?
4. Machine Learning Algorithm Models
5. Challenges of Digital Agriculture
6. From Leaf to Harvest: How Interactions Between Spectral Readings, Leaf Nutrient Content, and Yield Affect the Robustness of Predictive Models
7. Final Considerations
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Nutrient Status/Plant Stress | Sensor and Spectral Bands | Data Type/Visualization | Machine Learning Algorithm/Model | Plant Species | Reference |
---|---|---|---|---|---|
Prediction of N, chlorophyll a + b, and carotenoid content | Hyperspectral sensor STS-VIS (Ocean Insight, USA). Spectral bands from 450 to 850 nm | Graphs generated from electromagnetic spectrum readings | Artificial Neural Networks (ANN); M5P decision tree; REPTree (REPT); Random Forest (RF); Polynomial Support Vector Machine (SVMP); ZeroR (ZR) | Maize (Zea mays L.) | [11] |
N, P, and K contents | Reflectance values obtained in the following spectral bands (SBs): red (660 nm), green (550 nm), near-infrared (NIR, 735 nm), and red edge (790 nm) | Spectral images, spectral bands | J48 decision tree and REPTree; Random Forest (RF); Artificial Neural Network (ANN); Support Vector Machine (SVM); Logistic Regression (LR, used as control) | Soybean genotypes (Glycine max L.) | [43] |
Boron deficiency or toxicity | MSI sensor (Sentinel-2) | Orbital images, Vegetation Indices (NDVI, NDRE, EVI, CI, PRSI, CCCI, MTCI, HMSSI) | Spectral Angle Mapping (SAM) algorithm | Ten-year-old clones of Eucalyptus MA-2000 | [44] |
P, K, Mg, Ca, S, B, Zn, Mn, Fe, Cu | 5-band multispectral camera (Micasense Altum, Micasense, USA): blue (465–485 nm), green (550–570 nm), red (663–673 nm), red edge (712–722 nm), and near-infrared (NIR, 820–1000 nm) | Multispectral images from Unmanned Aerial Vehicles (UAVs) | Multiple regression algorithms, such as ElasticNet, Lasso regression, Linear SVM, PLSR, Random Forest (RF), and Ridge regression | Sweet orange seedlings ‘Hamlin’ or ‘Valencia’ grafted onto more than 30 different rootstocks | [45] |
Elevated Nitrogen levels | The Sensefly Sequoia multispectral sensor evaluates vegetation indices in the following spectral bands: green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (NIR, 790 nm) | Image (based on reflectance data), followed by processing of vegetation index models (NDVI, NDRE) | Statistical Model: Yijkl = μ + Bk + Cl + Vi + Nj + CVil + CNjl + VNij + CVNijl + ϵijkl | Maize genotypes (Zea mays L.) | [46] |
N, P, K | Vis-NIR HSI sensor (Visible-Near-Infrared Hyperspectral Imaging) (400–1000 nm) | Visible-Near-Infrared Hyperspectral Image (Vis-NIR HSI) | Partial Least Squares Regression (PLSR) models | Cocoa (Theobroma cacao L.) | [47] |
Predicting leaf nitrogen concentration | Multispectral sensor MicaSense Red-Edge (2017/2018 season) and multispectral sensor Sensefly Parrot Sequoia (2018/2019 season). Bands (G, R, RE, NIR) used for vegetation index calculations, with slight variations between sensors/years (e.g., G: 560/550 nm, R: 668/660 nm, RE: 717/735 nm, NIR: 842/790 nm) | Images and spectral bands | REPTree (REPT); RF; K-Nearest Neighbor (K = 1, K = 5, K = 10) (1NN, 5NN, 10NN); SVM-RBF (SVMR); Polynomial Support Vector Machine (SVMP); Linear Regression (LR); Radial Basis Function Regression (RBF) | 11 Maize cultivars | [48] |
Predicting nitrogen content | Multispectral sensor Parrot Sequoia. Spectral bands captured: green (530–570 nm), red (640–680 nm), red edge (730–740 nm), and near-infrared (NIR, 770–810 nm) | Multispectral images | RF; ANN; SVM; Decision Trees (DT); RNA (Artificial Neural Network—same as ANN) | 33,600 Valencia orange trees (Citrus sinensis ‘Valencia’) planted on Citrumelo oscillating rootstock | [49] |
Predicting leaf nitrogen concentration: low (≤27 g·kg−1), medium (>27 & ≤29 g·kg−1), and high (>29 g·kg−1) | Parrot Sequoia sensor. Green (510–590 nm), red (620–700 nm), red edge (725–745 nm), and near-infrared (NIR, 750–830 nm) bands | Multispectral images, spectral bands | Constrained Energy Minimization; Linear Spectral Unmixing; Mixture-Tuned Matched Filtering; Minimum Distance; Orthogonal Subspace Projection; Spectral Angle Mapper; 1 Spectral Information Divergence | Valencia sweet orange (Citrus sinensis ‘Valencia’), grafted onto Citrumelo Swingle rootstock | [50] |
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Silva, B.C.d.; Prado, R.d.M.; Campos, C.N.S.; Baio, F.H.R.; Teodoro, L.P.R.; Teodoro, P.E.; Santana, D.C. Applicability of Technological Tools for Digital Agriculture with a Focus on Estimating the Nutritional Status of Plants. AgriEngineering 2025, 7, 161. https://doi.org/10.3390/agriengineering7050161
Silva BCd, Prado RdM, Campos CNS, Baio FHR, Teodoro LPR, Teodoro PE, Santana DC. Applicability of Technological Tools for Digital Agriculture with a Focus on Estimating the Nutritional Status of Plants. AgriEngineering. 2025; 7(5):161. https://doi.org/10.3390/agriengineering7050161
Chicago/Turabian StyleSilva, Bianca Cavalcante da, Renato de Mello Prado, Cid Naudi Silva Campos, Fábio Henrique Rojo Baio, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, and Dthenifer Cordeiro Santana. 2025. "Applicability of Technological Tools for Digital Agriculture with a Focus on Estimating the Nutritional Status of Plants" AgriEngineering 7, no. 5: 161. https://doi.org/10.3390/agriengineering7050161
APA StyleSilva, B. C. d., Prado, R. d. M., Campos, C. N. S., Baio, F. H. R., Teodoro, L. P. R., Teodoro, P. E., & Santana, D. C. (2025). Applicability of Technological Tools for Digital Agriculture with a Focus on Estimating the Nutritional Status of Plants. AgriEngineering, 7(5), 161. https://doi.org/10.3390/agriengineering7050161