Nutrient Diagnosis of Fertigated “Prata” and “Cavendish” Banana (Musa spp.) at Plot-Scale

Neto, Antonio João de Lima; Deus, José Aridiano Lima de; Filho, Vagner Alves Rodrigues; Natale, William; Parent, Léon E.

doi:10.3390/plants9111467

Cited by 17 publications

(10 citation statements)

References 58 publications

(95 reference statements)

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“…Random Forest, k-nearest neighbors (KNN), support vector machine (SVM), gradient boosting, stochastic gradient descent (SGD), Adaboost, linear regression and Neural Networks were the ML models available in Orange data mining freeware vs. 3.23. Decision trees were commonly used in plant nutrition studies [ 35 , 39 ].…”

Section: Methodsmentioning

confidence: 99%

Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models

Hahn

Parent

Paviani³

et al. 2022

PLoS ONE

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Brazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the current study is to customize fertilizer recommendations to reach high garlic marketable yield at local scale in a pilot study. Thus, collected 15 nitrogen (N), 24 phosphorus (P), and 27 potassium (K) field experiments conducted during the 2015 to 2017 period in Santa Catarina state, Brazil. In addition, 61 growers’ observational data were collected in the same region in 2018 and 2019. The data set was split into 979 experimental and observational data for model calibration and into 45 experimental data (2016) to test ML models and compare the results to state recommendations. Random Forest (RF) was the most accurate ML to predict marketable yield after cropping system (cultivar, preceding crops), climatic indices, soil test and fertilization were included features as predictor (R2 = 0.886). Random Forest remained the most accurate ML model (R2 = 0.882) after excluding cultivar and climatic features from the prediction-making process. The model suggested the application of 200 kg N ha-1 to reach maximum marketable yield in a test site in comparison to the 300 kg N ha-1 set as state recommendation. P and K fertilization also seemed to be excessive, and it highlights the great potential to reduce production costs and environmental footprint without agronomic loss. Garlic root colonization by arbuscular mycorrhizal fungi likely contributed to P and K uptake. Well-documented data sets and machine learning models could support technology transfer, reduce costs with fertilizers and yield gaps, and sustain the Brazilian garlic production.

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Section: Methodsmentioning

confidence: 99%

Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models

Hahn

Parent

Paviani³

et al. 2022

PLoS ONE

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“…Compositional Data Analysis can be combined with machine learning methods to customize plant nutrient requirements for application at local scale where factor interactions shape fertilization decisions [17,46,[83][84][85][86]. After running ML methods, it was suggested to use the ilr transformation to compute the Euclidean distance between the diagnosed (X) and successful (x) compositions, then compute the corresponding perturbation vector to rank nutrients in the order of their limitations to yield [44].…”

Section: Machine Learning Methods To Process Large Datasetsmentioning

confidence: 99%

“…A flow of information from data acquisition to dataset organization and fertilizer recommendations at subfield level was described for lowbush blueberry (Vaccinium angustifolium) in Quebec [46], cranberry (Vaccinium macrocarpon) in Quebec and Wisconsin [85], and several crops in Brazil [17,83,84]. Nutrient diagnosis at local scale requires a well-documented dataset, an accurate machine learning model, a reliable model prediction algorithm, and a large set of ecologically diversified true negative specimens (Figure 6).…”

Section: Information Flowmentioning

confidence: 99%

Machine Learning, Compositional and Fractal Models to Diagnose Soil Quality and Plant Nutrition

Parent¹,

Natale²,

Brunetto³

2022

Soil Science - Emerging Technologies, Global Perspectives and Applications

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Soils, nutrients and other factors support human food production. The loss of high-quality soils and readily minable nutrient sources pose a great challenge to present-day agriculture. A comprehensive scheme is required to make wise decisions on system’s sustainability and minimize the risk of crop failure. Soil quality provides useful indicators of its chemical, physical and biological status. Tools of precision agriculture and high-throughput technologies allow acquiring numerous soil and plant data at affordable costs in the perspective of customizing recommendations. Large and diversified datasets must be acquired uniformly among stakeholders to diagnose soil quality and plant nutrition at local scale, compare side-by-side defective and successful cases, implement trustful practices and reach high resource-use efficiency. Machine learning methods can combine numerous edaphic, managerial and climatic yield-impacting factors to conduct nutrient diagnosis and manage nutrients at local scale where factors interact. Compositional data analysis are tools to run numerical analyses on interacting components. Fractal models can describe aggregate stability tied to soil conservation practices and return site-specific indicators for decomposition rates of organic matter in relation to soil tillage and management. This chapter reports on machine learning, compositional and fractal models to support wise decisions on crop fertilization and soil conservation practices.

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“…The perturbation vector can rank nutrients in the order of their limitation to yield as relative shortage or excess. It is a scaling operation between diagnosed (X) and reference (x) compositional vectors computed as follows [51]:…”

Section: And Other Features;mentioning

confidence: 99%

Current and next-year cranberry yields predicted from local features and carryover effects

et al. 2021

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Wisconsin and Quebec are the world leading cranberry-producing regions. Cranberries are grown in acidic, naturally low-fertility sandy beds. Cranberry fertilization is guided by general soil and tissue nutrient tests in addition to yield target and vegetative biomass. However, other factors such as cultivar, location, and carbon and nutrient storage impact cranberry nutrition and yield. The objective of this study was to customize nutrient diagnosis and fertilizer recommendation at local scale and for next-year cranberry production after accounting for local factors and carbon and nutrient carryover effects. We collected 1768 observations from on-farm surveys and fertilizer trials in Quebec and Wisconsin to elaborate a machine learning model using minimum datasets. We tested carryover effects in a 5-year Quebec fertilizer experiment established on permanent plots. Micronutrients contributed more than macronutrients to variation in tissue compositions. Random Forest model related accurately current-year berry yield to location, cultivars, climatic indices, fertilization, and tissue and soil tests as features (classification accuracy of 0.83). Comparing compositions of defective and successful tissue compositions in the Euclidean space of tissue compositions, the general across-factor diagnosis differed from the local factor-specific diagnosis. Nutrient standards elaborated in one region could hardly be transposed to another and, within the same region, from one bed to another due to site-specific characteristics. Next-year yield and nutrient adjustment could be predicted accurately from current-year yield and tissue composition and other features, with R2 value of 0.73 in regression mode and classification accuracy of 0.85. Compositional and machine learning methods proved to be effective to customize nutrient diagnosis and predict site-specific measures for nutrient management of cranberry stands. This study emphasized the need to acquire large experimental and observational datasets to capture the numerous factor combinations impacting current and next-year cranberry yields at local scale.

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Nutrient Diagnosis of Fertigated “Prata” and “Cavendish” Banana (Musa spp.) at Plot-Scale

Cited by 17 publications

References 58 publications

Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models

Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models

Machine Learning, Compositional and Fractal Models to Diagnose Soil Quality and Plant Nutrition

Current and next-year cranberry yields predicted from local features and carryover effects

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