ABSTRACT Image analysis combined with machine learning models can be an excellent tool for classification of fava (Phaseolus lunatus L.) genotypes and is a low-cost system. Fava is grown by family farmers, mainly, in the Northeast and South regions of Brazil, presenting economic and social importance. Evaluations to gather information on qualitative and quantitative characters of seeds enable the description and distinction of genotypes, allowing the evaluation of variability of plant species, which is essential in breeding programs. The use of image analysis is a fast and economic tool for obtaining large quantity of information. Machine learning techniques have been developed and implemented in the agricultural sector due to technological advances and increasing use of artificial intelligence, which enables the automatization of several processes. In this context, the objective of this work was to evaluate different machine learning models to classify fava genotypes, using data obtained through image analysis. Images of fava seeds were captured using a table scanner (HP Scanjet 2004), set to true color mode, arranged upside down inside of an aluminum box fully closed during the capture of the images for an adequate illumination and prevention of environmental noises. The K-Nearest Neighbor, Naive Bayes, Linear Discriminant Analysis, Support Vector Machine, Gradient Boosting, Bootstrap Aggregating, Classification and Regression Trees, Random Forest, and C50 models were used for the study. Linear Discriminant Analysis was the model that presented the highest efficiency for classifying the genotypes, with an accuracy of 90%.

ABSTRACT Quantifying soil gas emissions is costly, since it requires specific methodologies and equipment. The objective of this study was to evaluate modeling by nonlinear regression and artificial neural networks (ANN) to estimate CO2 emissions caused by soil managements. CO2 emissions were evaluated in two different soil management systems: no-tillage and minimum tillage. Readings of CO2 flow were carried out by an automated closed system chamber; soil temperature, water content, density, and total organic carbon were also determined. The regression model and the ANN models were adjusted based on the correlation of the variables measured in the areas where the soil was managed with no-tillage and minimum tillage with data of CO2 emission. Artificial neural networks are more accurate to determine correlations between CO2 emissions and soil temperature, water content, density, and organic carbon content than linear regression.