Predicting Peach Fruit Ripeness Using Explainable Machine Learning

Ljubobratović, Dejan; Zhang, Guoxiang; Bakarić, Marija Brkić; Jemrić, Tomislav; Matetić, Maja

doi:10.2507/31st.daaam.proceedings.099

Cited by 3 publications

(4 citation statements)

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“…According to Nascimento Nunes [ 61 ], the development of peach blush color is related to the light exposure rather than to the fruit maturation. The fact that peaches can be harvested from different canopy positions and orchards with or without applied nets (different light growing conditions), as indicated in the previous study by Ljubobratović et al [ 14 ], explains why Group 2 (additional color) was not the most important for the correct prediction of peach maturity.…”

Section: Discussionmentioning

confidence: 99%

“…In a study conducted by Sohaib et al [ 13 ], spectral information was used to develop an NIR-based maturity estimator of various fruits (apple, mango, grapes, peaches, pears, and melons) using least squares support vector machine learning techniques. The RF machine learning algorithm was used by Ljubobratović et al [ 14 ] for the prediction of ‘Spring Belle’ peach maturity, while RF and KNN models were successfully established to predict the maturity of peaches during shelf-life in another study [ 15 ]. Voss et al [ 16 ] used three machine learning models, i.e., extreme learning machine, KNN, and support vector machines (SVM), for the prediction of peach fruit growth and maturation based on data collected using the E-nose prototype.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data

Ljubobratović

Vuković

Bakarić

et al. 2022

Sensors

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To date, many machine learning models have been used for peach maturity prediction using non-destructive data, but no performance comparison of the models on these datasets has been conducted. In this study, eight machine learning models were trained on a dataset containing data from 180 ‘Suncrest’ peaches. Before the models were trained, the dataset was subjected to dimensionality reduction using the least absolute shrinkage and selection operator (LASSO) regularization, and 8 input variables (out of 29) were chosen. At the same time, a subgroup consisting of the peach ground color measurements was singled out by dividing the set of variables into three subgroups and by using group LASSO regularization. This type of variable subgroup selection provided valuable information on the contribution of specific groups of peach traits to the maturity prediction. The area under the receiver operating characteristic curve (AUC) values of the selected models were compared, and the artificial neural network (ANN) model achieved the best performance, with an average AUC of 0.782. The second-best machine learning model was linear discriminant analysis with an AUC of 0.766, followed by logistic regression, gradient boosting machine, random forest, support vector machines, a classification and regression trees model, and k-nearest neighbors. Although the primary parameter used to determine the performance of the model was AUC, accuracy, F1 score, and kappa served as control parameters and ultimately confirmed the obtained results. By outperforming other models, ANN proved to be the most accurate model for peach maturity prediction on the given dataset.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data

Ljubobratović

Vuković

Bakarić

et al. 2022

Sensors

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“…The use of local explanation methods in this study is in line with recent work in the field of XAI, to improve the explanation of model reasoning to make it understandable by stakeholders and other participants without the need for machine learning training (Ghai et al., 2021). Although local explainable machine learning is a relatively new approach to veterinary epidemiology, local interpretation methods have previously been applied to the agricultural sector to quantify the importance of hydro‐climatic factors on crop evapotranspiration, detect estrus in cattle and predict peach fruit ripeness (Chakraborty et al., 2021; Fauvel et al., 2019; Ljubobratovic et al., 2020).…”

Section: Discussionmentioning

confidence: 99%

Interpretable machine learning applied to on‐farm biosecurity and porcine reproductive and respiratory syndrome virus

Sykes

Silva

Holtkamp

et al. 2021

Transbounding Emerging Dis

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Effective biosecurity practices in swine production are key in preventing the introduction and dissemination of infectious pathogens. Ideally, on-farm biosecurity practices should be chosen by their impact on bio-containment and bio-exclusion, however quantitative supporting evidence is often unavailable. Therefore, the development of methodologies capable of quantifying and ranking biosecurity practices according to their efficacy in reducing disease risk have the potential to facilitate better informed choices of biosecurity practices.Using survey data on biosecurity practices, farm demographics, and previous outbreaks from 139 herds, a set of machine learning algorithms were trained to classify farms by porcine reproductive and respiratory syndrome virus status, depending on their biosecurity practices and farm demographics, to produce a predicted outbreak risk. A novel interpretable machine learning toolkit, MrIML-biosecurity, was developed to benchmark farms and production systems by predicted risk, and quantify the impact of biosecurity practices on disease risk at individual farms.Quantifying the variable impact on predicted risk 50% of 42 variables were associated with fomite spread while 31% were associated with local transmission. Results from machine learning interpretations identified similar results, finding substantial contribution to predicted outbreak risk from biosecurity practices relating to: the turnover and number of employees; the surrounding density of swine premises and pigs; the sharing of haul trailers; distance from the public road; and farm production type. In addition, the development of individualized biosecurity assessments provides the opportunity to better guide biosecurity implementation on a case-by-case basis. Finally, the flexibility of the MrIML-biosecurity toolkit gives it the potential to be applied to wider areas of biosecurity benchmarking, to address biosecurity weaknesses in other livestock systems and industry relevant diseases.

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“…Another system for determining fruit ripeness using the DNN model based on images obtained with a hyperspectral camera is created in [21]. In [22], it is pointed out that the ripeness of peaches can be approximately determined by means of electrical impedance, using peach firmness as a measure of maturity and the Random Forest method as one of the black box machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction

et al. 2021

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Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. The main goal of this study is to develop a machine learning model that will detect the most important features for predicting peach maturity by first training models and then using the importance ratings of these models to detect nonlinear (and linear) relationships. Thus, the most important peach features at a given stage of its ripening could be revealed. To date, this method has not been used for this purpose, and at the same time, it has the potential to be applied to other similar peach varieties. A total of 33 fruit features are measured on the harvested peaches, and three imbalanced datasets are created using firmness thresholds of 1.84, 3.57, and 4.59 kg·cm−2. These datasets are balanced using the SMOTE and ROSE techniques, and the Random Forest machine learning model is trained on them. Permutation Feature Importance (PFI), Variable Importance (VI), and LIME interpretability methods are used to detect variables that most influence predictions in the given machine learning models. PFI shows that the h° and a* ground color parameters, COL ground color index, SSC/TA, and TA inner quality parameters are among the top ten most contributing variables in all three models. Meanwhile, VI shows that this is the case for the a* ground color parameter, COL and CCL ground color indexes, and the SSC/TA inner quality parameter. The fruit flesh ratio is highly positioned (among the top three according to PFI) in two models, but it is not even among the top ten in the third.

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Predicting Peach Fruit Ripeness Using Explainable Machine Learning

Cited by 3 publications

References 0 publications

Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data

Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data

Interpretable machine learning applied to on‐farm biosecurity and porcine reproductive and respiratory syndrome virus

Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction

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