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Flavonoids are compounds that result from the secondary metabolism of plants and play a crucial role in plant development and mitigating biotic and abiotic stresses. The highest levels of flavonoids are found in legumes such as soybean. Breeding programs aim to increase desirable traits, such as higher flavonoid contents and vigorous seeds. Soybeans are one of the richest sources of protein in the plant kingdom and the main source of flavonoid derivatives for human health. In view of this, the hypothesis of this study is based on the possibility that the concentration of isoflavones in soybean seeds contributes to the physiological quality of the seeds. The aim of this study was to analyze the content of flavonoids in soybean genotypes and their influence on the physiological quality of the seeds. Seeds from thirty-two soybean genotypes were obtained by carrying out a field experiment during the 2021/22 crop season. The experimental design was randomized blocks with four replications and thirty-two F3 soybean populations. The seeds obtained were subjected to germination, first germination counting, electrical conductivity and tetrazolium vigor and viability tests. After drying and milling the material from each genotype, liquid chromatography analysis was carried out to obtain flavonoids, performed at UPLC level. Data were submitted to analysis of variance and, when significant, the means were compared using the Scott-Knott test at 5% probability. The results found here show the occurrence of genotypes with higher amounts of flavonoids when compared to their peers. The flavonoid FLVD_G2 had the highest concentration and differed from the others. Thus, we can assume that the type and concentration of flavonoids does not influence the physiological quality of seeds from different soybean genotypes, but it does indirectly contribute to viability and vigor, since the genotypes with the highest FLVD_G2 levels had better FGC values. The findings indicate that there is a difference between the content of flavonoids in soybean genotypes, with a higher content of genistein. The content of flavonoids does not influence the physiological quality of seeds, but contributes to increasing viability and vigor. [ABSTRACT FROM AUTHOR]

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field. [ABSTRACT FROM AUTHOR]

A current challenge of genetic breeding programs is to increase grain yield and protein content and at least maintain oil content. However, evaluations of industrial traits are time and cost-consuming. Thus, achieving accurate models for classifying genotypes with better industrial technological performance based on easier and faster to measure traits, such as agronomic ones, is of paramount importance for soybean breeding programs. The objective was to classify groups of soybean genotypes to industrial technological variables based on agronomic traits measured in the field using machine learning (ML) techniques. Field experiments were carried out in two sites in a randomized block design with two replications and 206 F2 soybean populations. Agronomic traits evaluated were: days to maturation (DM), first pod height (FPH), plant height (PH), number of branches (NB), main stem diameter (SD), mass of one hundred grains (MHG), and grain yield (GY). Industrial technological variables evaluated were oil yield, crude protein, crude fiber, and ash contents, determined by high-optical accuracy near-infrared spectroscopy (NIRS). The models tested were: support vector machine (SVM), artificial neural network (ANN), decision tree models J48 and REPTree, random forest (RF), and logistic regression (LR, used as control). A genotype clustering was performed using PCA and k-means algorithm, and then the clusters formed were used as output variables of the ML models, while the agronomic traits were used as input variables. ML techniques provided accurate models to classify soybean genotypes for more complex variables (industrial technological) based on agronomic traits. RF outperformed the other models and can be used to contribute to soybean breeding programs by classifying genotypes for industrial technological traits. [ABSTRACT FROM AUTHOR]

Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses. [ABSTRACT FROM AUTHOR]

Obtaining soybean genotypes that combine better nutrient uptake, higher oil and protein levels in the grains, and high grain yield is one of the major challenges for current breeding programs. To avoid the development of unpromising populations, selecting parents for crossbreeding is a crucial step in the breeding pipeline. Therefore, our objective was to estimate the combining ability of soybean cultivars based on the F2 generation, aiming to identify superior segregating parents and populations for agronomic, nutritional and industrial traits. Field experiments were carried out in two locations in the 2020/2021 crop season. Leaf contents of the following nutrients were evaluated: phosphorus, potassium, calcium, magnesium, sulfur, copper, iron, manganese, and zinc. Agronomic traits assessed were days to maturity (DM) and grain yield (GY), while the industrial traits protein, oil, fiber and ash contents were also measured in the populations studied. There was a significant genotype × environment (G × A) interaction for all nutritional traits, except for P content, DM and all industrial traits. The parent G3 and the segregating populations P20 and P27 can be used aiming to obtain higher nutritional efficiency in new soybean cultivars. The segregating populations P11 and P26 show higher potential for selecting soybean genotypes that combine earliness and higher grain yield. The parent G5 and segregant population P6 are promising for selection seeking improvement of industrial traits in soybean. [ABSTRACT FROM AUTHOR]

Farmers focus on reducing the cost of production and aim to increase profit. The objective of this study was to quantify the reduction of pesticides applied to soybean (Glycine max (L.) Merrill) and maize (Zea mays L.) crops in several stages of the production cycle using a site-specific spraying application based on real-time sensors in the Brazilian Cerrado region. The sprayers were equipped with a precision spraying control system based on a real-time sensor. The spraying operations were performed not only for herbicide, but also for fungicide and insecticides applications. The maps recorded the percentage of the spray boom when the application was turned on (on/off spray system) with nozzle-to-nozzle control. The precision spraying system based on real-time sensors reduced the volume of pesticides (including herbicides, insecticides, and fungicides) applied to soybean and maize crops. There was a more significant reduction in the volume of pesticides applied post-emergence of the crops in the initial stages of soybean and maize when the crops had less leaf area or less foliage coverage between the rows. The cost reduction achieved using this technology was 2.3 times lower than the cost associated with pesticide application over the entire area using a conventional sprayer. Under the experimental conditions, there were no differences in the average crop yield, compared to the historical productivity of soybean and maize crops by applying this technology because the recommended doses were not affected and the site of application was limited to points where the presence of plants was present was detected. [ABSTRACT FROM AUTHOR]

One of the main objectives of soybean breeding programs is the search for genotypes that are both high yielding and tolerant to abiotic stresses. Brazilian Cerrado, the main grain‐producing region in the country, is characterised by naturally acidic and low fertility soils that consequently have low base saturation. Therefore, identifying genotypes with good performance for favourable and low base saturation stress conditions is of fundamental importance for crop improvement. The objective of this study was to select F3 soybean populations for multiple traits under contrasting base saturation conditions. Field experiments were carried out in two crop seasons in randomised block design with three replicates and 10 F3 populations (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10). In the first experiment, F3 populations were evaluated without base saturation correction (V = 30%, low saturation), while in the second experiment, limestone was applied 3 months before the start to raise the base saturation to 60% (recommended saturation). We evaluated physiological, nutritional, agronomic and spectral variables. Multivariate analysis was performed to know the populations' performance and the interrelationship between variables and populations in the saturation conditions evaluated. The F3 populations showed differential performance for all traits across the conditions tested, enabling the identification of the best populations for each specific condition and both base saturation environments. Population P2 stood out under both low and recommended base saturation conditions and can be used in F4 plant selection to obtain superior lines for physiological and agronomic traits and vegetation indices. [ABSTRACT FROM AUTHOR]

Background: Precision agriculture techniques are widely used to optimize fertilizer and soil applications. Furthermore, these techniques could also be combined with new statistical tools to assist in phenotyping in breeding programs. In this study, the research hypothesis was that soybean cultivars show phenotypic differences concerning wavelength and vegetation index measurements. Results: In this research, we associate variables obtained via high-throughput phenotyping with the grain yield and cycle of soybean genotypes. The experiment was carried out during the 2018/2019 and 2019/2020 crop seasons, under a randomized block design with four replications. The evaluated soybean genotypes included 7067, 7110, 7739, 8372, Bonus, Desafio, Maracai, Foco, Pop, and Soyouro. The phenotypic traits evaluated were: first pod height (FPH), plant height (PH), number of branches (NB), stem diameter (SD), days to maturity (DM), and grain yield (YIE). The spectral variables evaluated were wavelengths and vegetation indices (NDVI, SAVI, GNDVI, NDRE, SCCCI, EVI, and MSAVI). The genotypes Maracai and Foco showed the highest grain yields throughout the crop seasons, in addition to belonging to the groups with the highest means for all VIs. YIE was positively correlated with the NDVI and certain wavelengths (735 and 790 nm), indicating that genotypes with higher values for these spectral variables are more productive. By path analyses, GNDVI and NDRE had the highest direct effects on the dependent variable DM, while NDVI had a higher direct effect on YIE. Conclusions: Our findings revealed that early and productive genotypes can be selected based on vegetation indices and wavelengths. Soybean genotypes with a high grain yield have higher means for NDVI and certain wavelengths (735 and 790 nm). Early genotypes have higher means for NDRE and GNDVI. These results reinforce the importance of high-throughput phenotyping as an essential tool in soybean breeding programs. [ABSTRACT FROM AUTHOR]

The guidance on decision-making regarding deforestation in Amazonia has been efficient as a result of monitoring programs using remote sensing techniques. Thus, the objective of this study was to identify the expansion of soybean farming in disagreement with the Soy Moratorium (SoyM) in the Amazonia biome of Mato Grosso from 2008 to 2019. Deforestation data provided by two Amazonia monitoring programs were used: PRODES (Program for Calculating Deforestation in Amazonia) and ImazonGeo (Geoinformation Program on Amazonia). For the identification of soybean areas, the Perpendicular Crop Enhancement Index (PCEI) spectral model was calculated using a cloud platform. To verify areas (polygons) of largest converted forest-soybean occurrences, the Kernel Density (KD) estimator was applied. Mann–Kendall and Pettitt tests were used to identify trends over the time series. Our findings reveal that 1,387,288 ha were deforested from August 2008 to October 2019 according to PRODES data, of which 108,411 ha (7.81%) were converted into soybean. The ImazonGeo data showed 729,204 hectares deforested and 46,182 hectares (6.33%) converted into soybean areas. Based on the deforestation polygons of the two databases, the KD estimator indicated that the municipalities of Feliz Natal, Tabaporã, Nova Ubiratã, and União do Sul presented higher occurrences of soybean fields in disagreement with the SoyM. The results indicate that the PRODES system presents higher data variability and means statistically superior to ImazonGeo. [ABSTRACT FROM AUTHOR]

The search for high‐yielding genotypes and that are tolerant to abiotic stresses has been a major goal in plant breeding. Thus, the use of technologies such as precision agriculture associated with remote sensing tools for plant phenotyping has increased. The hypothesis of this research was that soya bean genotypes respond differently to low and adequate base saturation levels in the soil and that vegetation indexes can be efficient auxiliary tools in plant phenotyping for this purpose. The objective of this study was to evaluate the nutritional status and agronomic performance of soya bean genotypes grown in low and recommended base saturation conditions using high‐throughput phenotyping. The research was carried out in 2017/2018 and 2018/2019 crop seasons, in which two field experiments in each season were installed. In experiment I, genotypes (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10) were evaluated without soil correction (low saturation condition). In experiment II, liming was performed three months before sowing of the genotypes to raise the base saturation to 60% (recommended saturation). Canopy spectral behaviour at the following wavelengths was evaluated: green (550 nm), red (660 nm), red edge (735 nm) and near‐infrared (790 nm), and the vegetation indices (Vis) NDVI, SAVI, EVI and MSAVI were calculated. The variables evaluated were leaf calcium and magnesium contents and grain yield. The use of VI's was efficient in assessing the performance of genotypes soya bean at different base saturation levels. The EVI showed moderate correlation with the nutritional and agronomic variables measured in each base saturation level. The approach used enabled both to identify genotypes tolerant to low base saturation soils and the ones with better response to liming. [ABSTRACT FROM AUTHOR]

Drought stress, salinity and aluminium toxicity are considered the most critical abiotic factors limiting the growth of soya bean plants, accounting for the low yield of the crop in the Cerrado region. Our hypothesis is that soya bean cultivars have differential morphological responses to stressful environmental conditions. Thus, an experiment was carried out with the purpose of identifying and recommending soya bean genotypes that simultaneously combine adaptability and stability for morphological traits when subjected to stressful environmental conditions. Drought, saline and aluminium stresses were induced in seeds of 25 soya bean cultivars. Seed germination, and length and dry mass of the seedlings were measured. For each trait, adaptability and stability were evaluated by the Lin & Binns method and simultaneously considering all traits for the set of environments. There was significant GxE interaction for all traits evaluated. The genotypes 97R50 IPRO, BMX Bônus IPRO and 98R31 IPRO simultaneously combine adaptability and stability for all traits measured. These genotypes can be cultivated both under optimal and abiotic stress conditions, such as those investigated in this study. Additionally, they can be used as parents in crossing blocks aiming at obtaining genotypes resistant to drought, saline and aluminium toxicity stresses. [ABSTRACT FROM AUTHOR]

Gas exchange analysis is an important technique, as the reduction in yield may be related to the decreased photosynthetic activity, due to adverse climatic factors in the growing site. The hypothesis of this study was that contrasting soil water conditions result in different photosynthetic performance in soybean genotypes. Thus, our objective was to analyse the physiological capacity in soybean genotypes under field conditions with optimal soil moisture and under water deficit. The experimental design was random blocks with 10 genotypes (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10) and three replicates. Individual analysis of variance was performed in both environments (irrigated and rainfed), and a correlation network between the traits was generated. We measured the traits net photosynthesis, stomatal conductance, internal CO2 concentration, instant water‐use efficiency, instant carboxylation efficiency and grain yield. Stressed plants reduce stomatal conductance and transpiration, but increase the instant water‐use efficiency as a defence mechanism in sub‐optimal soil moisture conditions. The P6 genotype obtained better physiological capacity under irrigated conditions, while the P10 genotype showed the better performance under rainfed conditions, which makes it tolerant to water stress. Our findings can contribute to the genotype formation and parental choice steps in breeding programs aimed at obtaining both irrigation‐responsive and drought‐tolerant genotypes. [ABSTRACT FROM AUTHOR]

Most of the Brazilian soils, especially in Cerrado biome, have high acidity and toxic aluminium, making the application of limestone an indispensable technique to obtain high yield in soybean crop. The hypothesis of this study was that contrasting base saturation provides changes in water‐use efficiency (WUE) and agronomic traits of soybean populations. Thus, we evaluated the WUE and agronomic performance of F3 soybean populations under different base saturation levels. Each experiment was conducted in a randomized complete blocks design with three replicates and 10 F3 populations (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10) for two crop seasons. Segregating populations were obtained by bulk method. In the first experiment, F3 populations were evaluated without correction of base saturation (V = 30%), while in the second experiment, limestone was applied three months before sowing to increase base saturation to 60%. The traits evaluated were as follows: calcium, magnesium and potassium contents, water‐use efficiency (WUE), plant height, first pod height, main stem diameter and grain yield. Our findings indicate that WUE and agronomic performance of F3 soybean populations depends on the saturation condition. We identified the P4 and P9 populations as the most promising for use in breeding programmes aiming at higher tolerance to abiotic stress caused by low base saturation in soil. [ABSTRACT FROM AUTHOR]

In soybean, there is a lack of research aiming to compare the performance of machine learning (ML) and deep learning (DL) methods to predict more than one agronomic variable, such as days to maturity (DM), plant height (PH), and grain yield (GY). As these variables are important to developing an overall precision farming model, we propose a machine learning approach to predict DM, PH, and GY for soybean cultivars based on multispectral bands. The field experiment considered 524 genotypes of soybeans in the 2017/2018 and 2018/2019 growing seasons and a multitemporal–multispectral dataset collected by embedded sensor in an unmanned aerial vehicle (UAV). We proposed a multilayer deep learning regression network, trained during 2000 epochs using an adaptive subgradient method, a random Gaussian initialization, and a 50% dropout in the first hidden layer for regularization. Three different scenarios, including only spectral bands, only vegetation indices, and spectral bands plus vegetation indices, were adopted to infer each variable (PH, DM, and GY). The DL model performance was compared against shallow learning methods such as random forest (RF), support vector machine (SVM), and linear regression (LR). The results indicate that our approach has the potential to predict soybean-related variables using multispectral bands only. Both DL and RF models presented a strong (r surpassing 0.77) prediction capacity for the PH variable, regardless of the adopted input variables group. Our results demonstrated that the DL model (r = 0.66) was superior to predict DM when the input variable was the spectral bands. For GY, all machine learning models evaluated presented similar performance (r ranging from 0.42 to 0.44) for each tested scenario. In conclusion, this study demonstrated an efficient approach to a computational solution capable of predicting multiple important soybean crop variables based on remote sensing data. Future research could benefit from the information presented here and be implemented in subsequent processes related to soybean cultivars or other types of agronomic crops. [ABSTRACT FROM AUTHOR]

[Display omitted] • It is possible to distinguish soybean genotypes to grain industrial traits by hyperspectral reflectance of the leaves. • Logistic regression was superior in the classification of soybean genotypes for industrial traits. • The use of wavelengths provided better performance of the algorithms in the classification. This research aimed to evaluate the accuracy of machine learning techniques in distinguishing groups soybean genotypes according to grain industrial traits using hyperspectral reflectance of the leaves. A total of 32 soybean genotypes were evaluated and allocated in randomized blocks with four replications. At 60 days after emergence, spectral analysis was carried out on three leaf samples from each plot. The spectral analysis of the leaves was carried out with a hyperspectral sensor providing ranges from 350 to 2500 nm. Once the wavelengths were obtained, they were grouped into averages of representative intervals into bands. At the end of the crop cycle, grain yield was obtained, and subsequently the determination of carbohydrate, oil, and protein content. Initially, the genotypes were subjected to cluster analysis using the k-means algorithm and subsequently, the data was subjected to machine learning analysis, using six models: J48 Decision Trees (J48) and REPTree (DT), Random Forest (RF), Artificial Neural Networks (ANW), Logistic Regression (LR) and Support Vector Machine (SVM). Logistic regression (LR) was used as a reference point as it is a traditional regression algorithm. The clusters formed acted as the output of the models, while for the input of the models, two groups of data were used: the spectral variables (SV) obtained by the sensor (350–2500 nm) and the spectral averages of the bands selected (BS) (350–2200 nm). The use of machine learning techniques presented lower responses than the standard technique used in the work, that is, LR, which presented superiority in the classification of soybean genotypes in terms of industrial traits. The use of wavelengths provided better performance of the algorithms in the classification in relation to selected bands. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hyperspectral reflectance for the levels of Asian soybean rust severity showed differences across the entire electromagnetic spectrum. • Plants with 50% disease severity have a curve close to healthy plants with lower reflectance. • More severely attacked plants have higher reflectance. Traditional monitoring of asian soybean rust severity is a time- and labor-intensive task, as it requires visual assessments by skilled professionals in the field. Thus, the use of remote sensing and machine learning (ML) techniques in data processing has emerged as an approach that can increase efficiency in disease monitoring, enabling faster, more accurate and time- and labor-saving evaluations. The aims of the study were: (i) to identify the spectral signature of different levels of Asian soybean rust severity; (ii) to identify the most accurate machine learning algorithm for classifying disease severity levels; (iii) which spectral input provides the highest classification accuracy for the algorithms; (iv) to determine a sample size of leaves that guarantees the best accuracy for the algorithms. A field experiment was carried out in the 2022/2023 harvest in a randomized block design with a 6x3 factorial scheme (ML algorithms x severity levels) and four replications. Disease severity levels assessed were: healthy leaves, 25 % severity, and 50 % severity. Leaf hyperspectral analysis was carried out over a wide range from 350 to 2500 nm. From this analysis, 28 spectral bands were extracted, seeking to distinguish the spectral signature for each severity level with the least input dataset. Data was subjected to machine learning analysis using Artificial Neural Network (ANN), REPTree (DT) and J48 decision trees, Random Forest (RF), and Support Vector Machine (SVM) algorithms, as well as a traditional classification method (Logistic Regression - LR). Two different input datasets were tested for each algorithm: the full spectrum (ALL) provided by the sensor and the 28 spectral bands (SB). Tests with different sample sizes were also conducted to investigate the algorithms' ability to detect severity levels with a reduced sample size. Our findings indicate differences between the spectral curves for the severity levels assessed, which makes it possible to differentiate between healthy plants with low and high severity using hyperspectral sensing. SVM was the most accurate algorithm for classifying severity levels by using all the spectral information as input. This algorithm also provided high classification accuracy when using smaller leaf samples. This study reveals that hyperspectral sensing and the use of ML algorithms provide an accurate classification of different levels of Asian rust severity, and can be powerful tools for a more efficient disease monitoring process. [ABSTRACT FROM AUTHOR]

[Display omitted] • Soybean genotypes were grouped according to their grain yield and industrial traits. • Support Vector Machine and J48 algorithms performed better at classifying them. • Using the spectral bands selected in the study improved the classification accuracy of the algorithms. Employing visible and near infrared sensors in high-throughput phenotyping provides insight into the relationship between the spectral characteristics of the leaf and the content of grain properties, helping soybean breeders to direct their program towards improving grain traits according to researchers' interests. Our research hypothesis is that the leaf reflectance of soybean genotypes can be directly related to industrial grain traits such as protein and fiber contents. Thus, the objectives of the study were: (i) to classify soybean genotypes according to the grain yield and industrial traits; (ii) to identify the algorithm(s) with the highest accuracy for classifying genotypes using leaf reflectance as model input; (iii) to identify the best input data for the algorithms to improve their performance. A field experiment was carried out in randomized block design with three replications and 32 soybean genotypes. At 60 days after emergence, spectral analysis was carried out on three leaf samples from each plot. A hyperspectral sensor was used to capture reflectance between the wavelengths from 450 to 824 nm. Representative spectral bands were selected and grouped into means. After harvest, grain yield was assessed and laboratory analyses of industrial traits were carried out. Spectral, industrial traits and yield data were subjected to statistical analysis. Data were analyzed by the following machine learning algorithms: J48 (J48) and REPTree (DT) decision trees, Random Forest (RF), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and conventional Logistic Regression (LR) analysis. The clusters formed were used as the output of the models, while two groups of input data were used for the input of the models: the spectral variables (WL) noise-free obtained by the sensor (450–828 nm) and the spectral means of the selected bands (SB) (450.0–720.6 nm). Soybean genotypes were grouped according to their grain yield and industrial traits, in which the SVM and J48 algorithms performed better at classifying them. Using the spectral bands selected in the study improved the classification accuracy of the algorithms. [ABSTRACT FROM AUTHOR]