Machine Learning Methods for Woody Volume Prediction in Eucalyptus

Santana, Dthenifer Cordeiro; Santos, Regimar Garcia dos; Silva, Pedro Henrique Neves da; Pistori, Hemerson; Teodoro, Larissa Pereira Ribeiro; Poersch, Nerison Luís; Azevedo, Gileno Brito de; Azevedo, Glauce Táıs de Oliveira Sousa; Silva, Carlos Antônio da; Teodoro, Paulo Eduardo

doi:10.3390/su151410968

Cited by 4 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From this approach, all data points are predicted and validated, while still keeping a separate training set. This strategy has been widely adopted in ML analyses to avoiding overfitting or biased learning (Granholm et al, 2012;André et al, 2022;Ramos et al, 2020;Gava et al, 2022;Baio et al, 2023;Santana et al, 2023a;Santana et al, 2023b). Additionally, the cross-validation strategy provides the prediction results of one variable for each fold (k), making it possible to have repetitions for the model's accuracy values both in classification and regression studies, such as correlation between observed versus predicted (André et al, 2022;Baio et al, 2023;Santana et al, 2023b).…”

Section: Machine Learning Analysismentioning

confidence: 99%

“…This strategy has been widely adopted in ML analyses to avoiding overfitting or biased learning (Granholm et al, 2012;André et al, 2022;Ramos et al, 2020;Gava et al, 2022;Baio et al, 2023;Santana et al, 2023a;Santana et al, 2023b). Additionally, the cross-validation strategy provides the prediction results of one variable for each fold (k), making it possible to have repetitions for the model's accuracy values both in classification and regression studies, such as correlation between observed versus predicted (André et al, 2022;Baio et al, 2023;Santana et al, 2023b). This makes it possible to carry out statistical tests to compare or group means for the different techniques, to be described in detail in the next subtopic, which provides an accurate recommendation about the best ML models.…”

Section: Machine Learning Analysismentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Models for Predicting Mechanical Damage, Vigor and Viability of Soybean Seeds During Storage

Cirqueira,

Coradi,

Teodoro

et al. 2023

Eng. Agríc.

View full text Add to dashboard Cite

Artificial Intelligence has been widely applied in data prediction for better decision making and process optimization. In the post-harvest, the control of biotic and abiotic factors is fundamental for the conservation of seed quality. Meanwhile, the tetrazolium test has been used to evaluate seed quality, however, with several limitations that can lead to evaluation errors. Thus, machine learning models can be an alternative to predict the quality of soybean seeds, with gains in the speed of obtaining results in relation to laboratory analysis methods, making the processes more robust and with low operational cost. With this, the aim of this study was to identify the best machine learning model for predicting mechanical damage, vigor and viability of soybean seeds during storage, depending on different conditions (10, 15 and 25 ºC), packaging (with coating and uncoated) and storage times (0, 3, 6, 9 and 12 months). M5P decision tree (M5P) and Random Forest (RF) models showed the best performance for predicting seed vigor (r = 0.75 and MAE = 10.0), and viability (r = 0.85 and MAE = 5.1), and mechanical damage to seeds (r = 0.64 and MAE = 11.2). It was concluded that the Random Forest (RF) model was the one that best predicted the results of soybean seed quality, with a more simplified and agile analysis for the development of vigor and viability of soybean seeds in storage.

show abstract

Section: Machine Learning Analysismentioning

confidence: 99%

Section: Machine Learning Analysismentioning

confidence: 99%

Machine Learning Models for Predicting Mechanical Damage, Vigor and Viability of Soybean Seeds During Storage

Cirqueira,

Coradi,

Teodoro

et al. 2023

Eng. Agríc.

View full text Add to dashboard Cite

show abstract

Site index curves construction for uneven-aged forest stands. A machine learning simulation approach

Diamantopoulou,

Progios

2024

E3S Web Conf.

View full text Add to dashboard Cite

In research related to forest disturbances, forest structure, ecological diversity, and forest sustainability, the concept of site quality holds significant importance. Site quality can be described as the combination of physical and biological factors that determine a site’s capacity to sustain tree growth. Thus, it becomes crucial to have a comprehensiv e understanding of site quality curves, known as site index curves. This paper endeavors to present a methodology for creating precise and reliable site index curves tailored to uneven- aged stands. To reach this goal, initially standard non-linear regression modeling was applied. Furthermore, in the field of forestry and environmental studies, especially in Greek forests, there are high demands for accurate predictions about forest health, potential and productivity. The rapidly developed field of machine learning can provide solutions to these requirements. To this direction, to directly predict the site index for each tree, the effectiveness of the eXtreme Gradient Boosting (XGBr) ensemble machine learning technique for regression modeling was investigated, aiming to effectively capture the non- linear characteristics of site index curves. In the realm of environmental and forest modeling, the studied simulation approach showed its potential to serve as a crucial foundation for advancing sustainable forest management.

show abstract

Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning

Santana,

Ratke,

Zanatta

et al. 2024

AgriEngineering

View full text Add to dashboard Cite

The application of hyperspectral data in machine learning models can contribute to the rapid and accurate determination of caffeine content in coffee beans. This study aimed to identify the machine learning algorithm with the best performance for predicting caffeine content and to find input data for these models that can improve the accuracy of these algorithms. The coffee beans were harvested one year after the seedlings were planted. The fresh beans were taken to the spectroscopy laboratory (Laspec) at the Federal University of Mato Grosso do Sul, Chapadão do Sul campus, for spectral evaluation using a spectroradiometer. For the analysis, the dried coffee beans were ground and sieved for the quantification of caffeine, which was carried out using a liquid chromatograph on the Waters Acquity 1100 series UPLC system, with an automatic sample injector. The spectral data of the beans, as well as the spectral data of the roasted and ground coffee, were analyzed using machine learning (ML) algorithms to predict caffeine content. Four databases were used as input: the spectral information of the bean (CG), the spectral information of the bean with additional clone information (CG+C), the spectral information of the bean after roasting and grinding (CGRG) and the spectral information of the bean after roasting and grinding with additional clone information (CGRG+C). The caffeine content was used as an output to be predicted. Each database was subjected to different machine learning models: artificial neural networks (ANNs), decision tree (DT), linear regression (LR), M5P, and random forest (RF) algorithms. Pearson’s correlation coefficient, mean absolute error, and root mean square error were tested as model accuracy metrics. The support vector machine algorithm showed the best accuracy in predicting caffeine content when using hyperspectral data from roasted and ground coffee beans. This performance was significantly improved when clone information was included, allowing for an even more accurate analysis.

show abstract

Machine Learning Methods for Woody Volume Prediction in Eucalyptus

Cited by 4 publications

References 29 publications

Machine Learning Models for Predicting Mechanical Damage, Vigor and Viability of Soybean Seeds During Storage

Machine Learning Models for Predicting Mechanical Damage, Vigor and Viability of Soybean Seeds During Storage

Site index curves construction for uneven-aged forest stands. A machine learning simulation approach

Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning

Contact Info

Product

Resources

About