Predictive models in horticulture: A case study with Royal Gala apples

Logan, Tom; McLeod, Stephen K.; Guikema, Seth D.

doi:10.1016/j.scienta.2016.06.033

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…Nevertheless, the use of LM can limit the accuracy of the estimation results, as the relationships between orchard yield and input variables are not always linear (Ballesteros et al, 2020). Logan et al (2016) carried out a case study with linear (generalized linear model) and nonlinear (Random Forest) models to estimate the average fruit weight in an apple orchard of cultivar 'Gala'. The generalized linear model showed good results with data collected ve days before harvest.…”

Section: Yield Estimation Modelingmentioning

confidence: 99%

“…More importantly, this task requires high-quality yield data over many years. Thus, it is possible to establish causal relationships between the yield and environmental variables or management practices (Logan et al, 2016;Brinkhoff & Robson, 2021) to train yield forecasting models.…”

Section: Challenges On the Temporal Scale For Yield Forecasting Modelsmentioning

confidence: 99%

“…Additionally, considering temporal and spatial aspects for developing more e cient and relevant methods of evaluating the yield of perennial crops can contribute to their better operational use (Zhu et al, 2022;Sartore et al, 2022). These more advanced evaluation methods can increase the e ciency of decision-making processes and contribute to increased productivity (Logan et al, 2016).In this context, there are a growing interest on the part of the industry and the academic community in the investigation of yield assessment models for apple, coffee, citrus, and even jujube crops ( the literature regarding the use of computational models to evaluate the yield of perennial fruit crops, to provide insights into the main strategies and technologies used for estimating and forecasting yield. In addition, we presented directions for future studies.…”

Section: Introductionmentioning

confidence: 99%

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Computational Models in Precision Fruit Growing: Reviewing the Impact of Temporal Variability on Perennial Crop Yield Assessment

2023

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Early yield information of perennial crops is crucial for growers and the industry, which allows cost reduction and bene ts crop planning. However, the yield assessment of perennial crops by computational models can be challenging due to diverse aspects of interannual variability that act on the crops. This review aimed to investigate and analyze the literature on yield estimation and forecasting modeling of perennial cropping systems. We reviewed 49 articles and categorized them according to their yield assessment strategy, modeling class used, and input variable characteristics. The strategies of yield assessment were discussed in the context of their principal improvement challenges. According to our investigation, image processing and deep learning models are emerging techniques for yield estimation.On the other hand, machine learning algorithms, such as Arti cial Neural Networks and Decision Trees, were applied to yield forecasting with reasonable time in advance of harvest. Emphasis is placed on the lack of representative long-term datasets for developing computational models, which can lead to accurate early yield forecasting of perennial crops.

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Section: Yield Estimation Modelingmentioning

confidence: 99%

Section: Challenges On the Temporal Scale For Yield Forecasting Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computational Models in Precision Fruit Growing: Reviewing the Impact of Temporal Variability on Perennial Crop Yield Assessment

2023

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“…The partial dependence plots illustrate the marginal influence when a variable change and the other variables remain constant. Logan et al [21] proposed an alternative to measure the marginal influence with equation (6).…”

Section: A Model Evaluationmentioning

confidence: 99%

Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia

2020

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The identification of influencing factors in crop yield (kg·ha-1) provides essential information for decision-making processes related to the prediction and improvement of productivity, which gives farmers the opportunity to increase their income. The current study investigates the application of multiple machine learning algorithms for cocoa yield prediction and influencing factors identification. The Support Vector Machines (SVM) and Ensemble Learning Models (Random Forests, Gradient Boosting) are compared with Least Absolute Shrinkage and Selection Operator (LASSO) regression models. The considered predictors were climate conditions, cocoa variety, fertilization level and sun exposition in an experimental crop located in Rionegro, Santander. Results showed that Gradient Boosting is the best prediction alternative with Coefficient of determination (R2) = 68%, Mean Absolute Error (MAE) = 13.32, and Root Mean Square Error (RMSE) = 20.41. The crop yield variability is explained mainly by the radiation one month before harvest, the accumulated rainfall on the harvest month, and the temperature one month before harvest. Likewise, the crop yields are evaluated based on the kind of sun exposure, and it was found that radiation one month before harvest is the most influential factor in shade-grown plants. On the other hand, rainfall and soil moisture are determining variables in sun-grown plants, which is associated with the water requirements. These results suggest a differentiated management for crops depending on the kind of sun exposure to avoid compromising productivity, since there is no significant difference in the yield of both agricultural managements.

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“…Por ello, los modelos predictivos en la actividad de la manzana pueden beneficiar a los productores, empacadores y compradores, dado que puede mejorar la eficacia en la toma de decisiones tanto de mercado como de planeación de la producción (Logan et al, 2016).…”

Section: Introductionunclassified

Análisis del precio de la manzana mediante un modelo SARIMA

Hernandez

Puente

Gómez

2019

Remexca

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El seguimiento y predicción de los precios es una importante herramienta en la toma de decisiones en las actividades productivas. Con base a los datos del precio promedio mensuales de la manzana red delicious a nivel nacional, de enero de 1998 a julio de 2017, publicados por el sistema nacional de información e integración de mercados, con el software Gretlâ. El objetivo de la investigación fue generar información sobre el comportamiento del precio de la manzana mediante un análisis de series de tiempo generándose una predicción para los 12 meses posteriores con un modelo Sarima . Los resultados señalaron que el error relativo para la predicción es 2%, lo cual garantiza pronósticos relativamente buenos, se pronosticó que el precio para julio de 2018 sería $31.99.

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Predictive models in horticulture: A case study with Royal Gala apples

Cited by 11 publications

References 22 publications

Computational Models in Precision Fruit Growing: Reviewing the Impact of Temporal Variability on Perennial Crop Yield Assessment

Computational Models in Precision Fruit Growing: Reviewing the Impact of Temporal Variability on Perennial Crop Yield Assessment

Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia

Análisis del precio de la manzana mediante un modelo SARIMA

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