Determination of soil nutrients and pH level using image processing and artificial neural network

Puno, John Carlo V.; Sybingco, Edwin; Dadios, Elmer P.; Valenzuela, Ira C.; Cuello, Joel L.

doi:10.1109/hnicem.2017.8269472

Cited by 38 publications

(9 citation statements)

References 13 publications

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“…Supported by the findings that root traits were affected by induced stratification and fortification, 25% SNAP solution in tandem with cold seed stratification for the first 7 days and exposure to combined red and blue light spectrums yielded the best lettuce seedling root morphology. However, a prepared 25% SNAP solution may vary its nitrate, phosphate, and potassium concentrations depending on the environmental temperature (Puno, Sybingco, Dadios, Valenzuela, & Cuello, 2017), thus, the exact value of these macronutrients must be optimally determined through higher intelligence such as an evolutionary algorithm.…”

Section: Root Stratification and Fortification Bioassaymentioning

confidence: 99%

“…Lettuce (Lactuca sativa L.) root system is designed naturally to acquire nutrients by extracting nutrients and minerals enriched water from the growing bed (Kerbiriou, Stomph, Van Der Putten, Lammerts Van Bueren, & Struik, 2013;Puno, Sybingco, Dadios, Valenzuela, & Cuello, 2017). Healthy roots enable crops to contain sufficient concentration of vitamins, such as retinol, ascorbic acid, and phytonadione, on its leaves.…”

Section: Introductionmentioning

confidence: 99%

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Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums

Concepcion

Dadios

2021

Agrivita.J.Agr.Sci

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Ecophysiological stimulators directly affect root morphology, especially in the embryonic stage. To enhance crop germination, an understanding of the root traits under abiotic inducers is needed. In this study, the combined impacts of white and red-blue light spectrums, cold stratification, and seed fortification involving various concentrations of bioactive chemicals namely simple nutrient addition program solution, gibberellic acid, α-naphthaleneacetic acid with thiamine hydrochloride were evaluated on loose-leaf lettuce (Lactuca sativa var. Altima) seedling root architecture. The growth-promoting effects of these nutrients varied the growth rate and morphology of roots which are immediately shown during the radicle development. Integrated computer vision and computational intelligence were employed for phytomorphological signatures extraction of seedlings that were cultivated in a customized modulable spectrum experimental chamber (MSPEC). Root phenotype model was developed using graph-cut segmentation and region properties, and the ideal germination nutrient concentration was optimized using bioinspired models with firefly algorithm optimal result of 204.1 mg/L for nitrate, 238.15 mg/L for phosphate, and 158.08 mg/L for potassium. It was verified that lettuce seedlings can endure highly concentrated nutrients, however, it is more sensitive to phosphate as this macronutrient significantly promotes root growth with the increased whorl number on white light spectrum exposure with cold stratification.

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Section: Root Stratification and Fortification Bioassaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums

Concepcion

Dadios

2021

Agrivita.J.Agr.Sci

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“…Helong Yu et al in [ 13 ] reported the decline in the use of some models such as Support Vector Machines (SVM) and multivariate adaptive regression spline, giving way to more advanced alternatives such as Random Forest (RF). Other research showed higher effectiveness of Neural Networks (NN) [ 10 , 14 , 15 ], Decision Tree (DT) [ 16 ], Naive Bayes [ 17 , 18 ], etc. Recently, Convolutional Neural Networks (CNNs) have received much attention in object classification [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method

ml.¹,

Chambers²

2021

Sensors

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The research presented in this paper is based on the hypothesis that the machine learning approach improves the accuracy of soil properties prediction. The correlations obtained in this research are important for understanding the overall strategy for soil properties prediction using optical spectroscopy sensors. Several research results have been stated and investigated. A comparison is made between six commonly used techniques: Random Forest, Decision Tree, Naïve Bayes, Support Vector Machine, Least-Square Support Vector Machine and Artificial Neural Network, showing that the best prediction accuracy cannot always be achieved by the most common and complicated method. The influence of the chosen category for nutrient characterization was investigated, indicating better prediction when a multi-component strategy was used. In contrast, the prediction of single-component soil properties was less accurate. In addition, the influence of category levels was not as significant as expected when choosing between 3-level, 5-level or 13-level nutrient characterization for some nutrients, which can be used for a more precise nutrient characterization strategy. A comparative analysis was performed between soil from a local farm with similar texture and soils collected from different locations in Slovenia, which gave a better prediction for a local farm. Finally, the influence of principal component analysis was validated using 5, 10, 20 and 50 first principal components, indicating the better performance of machine learning when using the 50 principal components.

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“…Natural and anthropological processes can cause the changing of trophic state. Naturally, when a certain pond, lake, or body of water experiences an abrupt change in temperature and pH, and contamination of excessive dissolved nitrogen, nitrogen, and depletion of oxygen [2] [3]. The biotic actions performed by bacteria A R T I C L E I N F O result in different nutrient loading distributed on the body of water.…”

Section: Introductionmentioning

confidence: 99%

Trophic state assessment using hybrid classification tree-artificial neural network

Concepcion

Loresco

Bedruz

et al. 2020

Int. J. Adv. Intell. Informatics

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The trophic state is one of the significant environmental impacts that must be monitored and controlled in any aquatic environment. This phenomenon due to nutrient imbalance in water strengthened with global warming, inhibits the natural system to progress. With eutrophication, the mass of algae in the water surface increases and results to lower dissolved oxygen in the water that is essential for fishes. Numerous limnological and physical features affect the trophic state and thus require extensive analysis to asses it. This paper proposed a model of hybrid classification tree-artificial neural network (CT-ANN) to assess the trophic state based on the selected significant features. The classification tree was used as a multidimensional reduction technique for feature selection, which eliminates eight original features. The remaining predictors having high impacts are chlorophyll-a, phosphorus and Secchi depth. The two-layer ANN with 20 artificial neurons was constructed to assess the trophic state of input features. The neural network was modeled based on the key parameters of learning time, cross-entropy, and regression coefficient. The ANN model used to assess trophic state based on 11 predictors resulted in 81.3% accuracy. The modeled hybrid classification tree-ANN based on 3 predictors resulted to 88.8% accuracy with a cross-entropy performance of 0.096495. Based on the obtained result, the modeled hybrid classification tree-ANN provides higher accuracy in assessing the trophic state of the aquaponic system.

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Determination of soil nutrients and pH level using image processing and artificial neural network

Cited by 38 publications

References 13 publications

Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums

Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums

Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method

Trophic state assessment using hybrid classification tree-artificial neural network

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