Diagnosis of nutrient composition in fruit crops: Major developments

Parent, Léon E.; Rozane, Danilo Eduardo; Deus, José Aridiano Lima de; Natale, William

doi:10.1016/b978-0-12-818732-6.00012-5

Cited by 13 publications

(10 citation statements)

References 34 publications

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“…The model is informative if AUC lies between 0.7 and 0.9 [69]. Classification accuracy (CA) computed as (TN + TP)/(TN + FN + TP + FP) was compared to the CA of other crops [70]. Nutrient ranges were assessed as the quartile concentration values of TN specimens.…”

Section: Discussionmentioning

confidence: 99%

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

Neto

Deus

Filho³

et al. 2020

Plants

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Fertigation management of banana plantations at a plot scale is expanding rapidly in Brazil. To guide nutrient management at such a small scale, genetic, environmental and managerial features should be well understood. Machine learning and compositional data analysis (CoDa) methods can measure the effects of feature combinations on banana yield and rank nutrients in the order of their limitation. Our objectives are to review ML and CoDa models for application at regional and local scales, and to customize nutrient diagnoses of fertigated banana at the plot scale. We documented 940 “Prata” and “Cavendish” plot units for tissue and soil tests, environmental and managerial features, and fruit yield. A Neural Network informed by soil tests, tissue tests and other features was the most proficient learner (AUC up to 0.827). Tissue nutrients were shown to have the greatest impact on model accuracy. Regional nutrient standards were elaborated as centered log ratio means and standard deviations of high-yield and nutritionally balanced specimens. Plot-scale diagnosis was customized using the closest successful factor-specific tissue compositions identified by the smallest Euclidean distance from the diagnosed composition using centered or isometric log ratios. Nutrient imbalance differed between regional and plot-scale diagnoses, indicating the profound influence of local factors on plant nutrition. However, plot-scale diagnoses require large, reliable datasets to customize nutrient management using ML and CoDa models.

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Section: Discussionmentioning

confidence: 99%

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

Neto

Deus

Filho³

et al. 2020

Plants

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“…The AUC of the RF model that included features available in the data set was 0.78, indicating that the model was informative. The accuracy of the RF classification model was 0.72 compared to more than 0.80 for most tested crops [ 48 ]. Raw concentrations with no need to impute missing values returned higher model accuracy than log ratios.…”

Section: Discussionmentioning

confidence: 99%

Nutrient Diagnosis of Eucalyptus at the Factor-Specific Level Using Machine Learning and Compositional Methods

Paula

Arruda

Parent

et al. 2020

Plants

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Brazil is home to 30% of the world’s Eucalyptus trees. The seedlings are fertilized at plantation to support biomass production until canopy closure. Thereafter, fertilization is guided by state standards that may not apply at the local scale where myriads of growth factors interact. Our objective was to customize the nutrient diagnosis of young Eucalyptus trees down to factor-specific levels. We collected 1861 observations across eight clones, 48 soil types, and 148 locations in southern Brazil. Cutoff diameter between low- and high-yielding specimens at breast height was set at 4.3 cm. The random forest classification model returned a relatively uninformative area under the curve (AUC) of 0.63 using tissue compositions only, and an informative AUC of 0.78 after adding local features. Compared to nutrient levels from quartile compatibility intervals of nutritionally balanced specimens at high-yield level, state guidelines appeared to be too high for Mg, B, Mn, and Fe and too low for Cu and Zn. Moreover, diagnosis using concentration ranges collapsed in the multivariate Euclidean hyper-space by denying nutrient interactions. Factor-specific diagnosis detected nutrient imbalance by computing the Euclidean distance between centered log-ratio transformed compositions of defective and successful neighbors at a local scale. Downscaling regional nutrient standards may thus fail to account for factor interactions at a local scale. Documenting factors at a local scale requires large datasets through close collaboration between stakeholders.

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“…Locally or regionally developed norms produce a higher degree of precision because many factors related with yield or date quality may change: cultivar, rootstock, climate, soil, and crop management [15,16,39]. Although it has been criticized [40,41], the CVA approach remains the most commonly used to diagnose foliar status. Table 1 is useful for advisory services for the quantitative diagnosis of palms.…”

Section: Critical Value Approach (Cva)mentioning

confidence: 99%

Nutrient Diagnosis Norms for Date Palm (Phoenix dactylifera L.) in Tunisian Oases

et al. 2020

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Several studies have pointed out the promising use of nutritional diagnosis methods for the determination of optimum nutrient contents in plant tissues. The present investigation was carried out in different oases in Southern Tunisia to determine reference values for the interpretation of leaf analyses of date palm (Phoenix dactylifera) Deglet Nour cultivar with the Critical Value Approach (CVA) and the Compositional Nutrient Diagnosis (CND). A database (n = 100) of yield and mineral concentrations taken from date palm leaflets in October, at the maturity stage of dates, was used. The yield cut-off between low-yield and high-yield subpopulations, selected from cumulative variance ratio functions across survey data, was 76 kg palm−1 and the global nutrient imbalance index (CNDr2) was 10.06. Critical CND nutrient indices were found to be symmetrical around zero as follows: (1.59; +1.59) for IN, (−0.44, +0.44) for IP, (−0.63, +0.63) for IK, (−0.94, +0.94) for ICa, (−1.05, +1.05) for IMg, (−0.80, +0.80) for IFe, (−0.74, +0.74) for ICu, (−0.80, +0.80) for IB, (−0.93, +0.93) for IZn, (−1.04, +1.04) for IMn, and (−1.03, +1.03) for the residual value. Compared to CND, the CVA approach shows weak detection of the nutrients that cause nutritional imbalance. CND indices revealed, except for N, the presence of nutrient imbalances and the necessity to correct the mineral nutrition of date palm in the Kebeli oases.

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Diagnosis of nutrient composition in fruit crops: Major developments

Cited by 13 publications

References 34 publications

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

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

Nutrient Diagnosis of Eucalyptus at the Factor-Specific Level Using Machine Learning and Compositional Methods

Nutrient Diagnosis Norms for Date Palm (Phoenix dactylifera L.) in Tunisian Oases

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