Discriminant analysis in the selection of groups of peach cultivars

Melo, George Wellington de; Rozane, Danilo Eduardo; Brunetto, Gustavo; Lattuada, Daiane Silva

doi:10.17660/actahortic.2018.1217.42

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Research has shown the importance of phenological stage grouping for modeling [6,10,98]. Moreover, advances in fruit tree fertilizing indicate different nutrition profiles for each cultivar [12,99,100] or site-specific conditions [101] and, while requiring further elucidation, have the potential to leverage soil data predictive power.…”

Section: Discussionmentioning

confidence: 99%

Grape Yield Prediction Models: Approaching Different Machine Learning Algorithms

Andrade,

Moura-Bueno,

Comin

et al. 2023

Horticulturae

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Efficient marketing of winegrapes involves negotiating with potential buyers long before the harvest, when little is known about the expected vintage. Grapevine physiology is affected by weather conditions as well as by soil properties and such information can be applied to build yield prediction models. In this study, Partial Least Squares Regression (PLSR), Cubist (CUB) and Random Forest (RF) algorithms were used to predict yield from imputed weather station data and soil sample analysis reports. Models using only soil variables had the worst general results (R2 = 0.15, RMSE = 4.16 Mg ha−1, MAE = 3.20 Mg ha−1), while the use of only weather variables yielded the best performance (R2 = 0.52, RMSE = 2.99 Mg ha−1, MAE = 2.43 Mg ha−1). Models built with CUB and RF algorithms showed signs of overfitting, yet RF models achieved the best average results (R2 = 0.58, RMSE = 2.85 Mg ha−1, MAE = 2.24 Mg ha−1) using only weather variables as predictors. Weather data imputation affected RF and CUB models more intensely while PLSR remained fairly insensitive. Plant age, yield level group, vineyard plot, May temperatures, soil pH and exchangeable concentrations of Zn, Cu, K and Mn were identified as important predictors. This exploratory work offers insights for future research on grape yield predictive modeling and grouping strategies to obtain more assertive results, thus contributing to a more efficient grapevine production chain in southern Brazil and worldwide.

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Section: Discussionmentioning

confidence: 99%

Grape Yield Prediction Models: Approaching Different Machine Learning Algorithms

Andrade,

Moura-Bueno,

Comin

et al. 2023

Horticulturae

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“…Peach cultivars surveyed in Southern Brazil showed differential nutritional profiles, and it may lead to cultivar-specific fertilization [65]. 'Esmeralda' nutrient ranges (Table 4) were narrow and differed from nutrient standards suggested for Southern Brazil [37], or elaborated among peach cultivars surveyed in Rio Grande do Sul State, Brazil [39].…”

Section: Foliar Nutrientsmentioning

confidence: 99%

Esmeralda Peach (Prunus persica) Fruit Yield and Quality Response to Nitrogen Fertilization

Nava

Júnior

Parent

et al. 2022

Plants

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‘Esmeralda’ is an orange fleshed peach cultivar primarily used for juice extraction and secondarily used for the fresh fruit market. Fruit yield and quality depend on several local environmental and managerial factors, mainly on nitrogen, which must be balanced with other nutrients. Similar to other perennial crops, peach trees show carryover effects of carbohydrates and nutrients and of nutrients stored in their tissues. The aims of the present study are (i) to identify the major sources of seasonal variability in fruit yield and qu Fruit Tree Department of Federal University of Pelotas (UFPEL), Pelotas 96010610ality; and (ii) to establish the N dose and the internal nutrient balance to reach high fruit yield and quality. The experiment was conducted from 2014 to 2017 in Southern Brazil and it followed five N treatments (0, 40, 80, 120 and 160 kg N ha−1 year−1). Foliar compositions were centered log-ratio (clr) transformed in order to account for multiple nutrient interactions and allow computing distances between compositions. Based on the feature ranking, chilling hours, degree-days and rainfall were the most influential features. Machine learning models k-nearest neighbors (KNN) and stochastic gradient decent (SGD) performed well on yield and quality indices, and reached accuracy from 0.75 to 1.00. In 2014, fruit production did not respond to added N, and it indicated the carryover effects of previously stored carbohydrates and nutrients. The plant had a quadratic response (p < 0.05) to N addition in 2015 and 2016, which reached maximum yield of 80 kg N ha−1. In 2017, harvest was a failure due to the chilling hours (198 h) and the relatively small number of fruits per tree. Fruit yield and antioxidant content increased abruptly when foliar clrCu was >−5.410. The higher foliar P linearly decreased total titratable acidity and increased pulp firmness when clrP > 0.556. Foliar N concentration range was narrow at high fruit yield and quality. The present results have emphasized the need of accounting for carryover effects, nutrient interactions and local factors in order to predict peach yield and nutrient dosage.

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“…Parent and Dafir [23] developed the compositional nutrient diagnosis in plants using row-centered log-ratios (clr). Thereafter, compositional data transformation has been used to preprocess combined nutrients traits of plant species and cultivars [13,[24][25][26] as well as animal species [27], and human food [28,29].…”

Section: Introductionmentioning

confidence: 99%

Cultivar-specific nutritional status of potato (Solanum tuberosum L.) crops

2020

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Gradients in the elemental composition of a potato leaf tissue (i.e. its ionome) can be linked to crop potential. Because the ionome is a function of genetics and environmental conditions, practitioners aim at fine-tuning fertilization to obtain an optimal ionome based on the needs of potato cultivars. Our objective was to assess the validity of cultivar grouping and predict potato tuber yields using foliar ionomes. The dataset comprised 3382 observations in Qué bec (Canada) from 1970 to 2017. The first mature leaves from top were sampled at the beginning of flowering for total N, P, K, Ca, and Mg analysis. We preprocessed nutrient concentrations (ionomes) by centering each nutrient to the geometric mean of all nutrients and to a filling value, a transformation known as row-centered log ratios (clr). A densitybased clustering algorithm (dbscan) on these preprocessed ionomes failed to delineate groups of high-yield cultivars. We also used the preprocessed ionomes to assess their effects on tuber yield classes (high-and low-yields) on a cultivar basis using k-nearest neighbors, random forest and support vector machines classification algorithms. Our machine learning models returned an average accuracy of 70%, a fair diagnostic potential to detect in-season nutrient imbalance of potato cultivars using clr variables considering potential confounding factors. Optimal ionomic regions of new cultivars could be assigned to the one of the closest documented cultivar.

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Discriminant analysis in the selection of groups of peach cultivars

Cited by 3 publications

References 9 publications

Grape Yield Prediction Models: Approaching Different Machine Learning Algorithms

Grape Yield Prediction Models: Approaching Different Machine Learning Algorithms

Esmeralda Peach (Prunus persica) Fruit Yield and Quality Response to Nitrogen Fertilization

Cultivar-specific nutritional status of potato (Solanum tuberosum L.) crops

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