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Spectral signatures allow the characterization of a surface from the reflected or emitted energy along the electromagnetic spectrum. This type of measurement has several potential applications in precision agriculture. However, capturing the spectral signatures of plants requires specialized instruments, either in the field or the laboratory. The cost of these instruments is high, so their incorporation in crop monitoring tasks is not massive, given the low investment in agricultural technology. This paper presents a low-cost clamp to capture spectral leaf signatures in the laboratory and the field. The clamp can be 3D printed using PLA (polylactic acid); it allows the connection of 2 optical fibers: one for a spectrometer and one for a light source. It is designed for ease of use and holds a leave firmly without causing damage, allowing data to be collected with less disturbance. The article compares signatures captured directly using a fiber and the proposed clamp; noise reduction across the spectrum is achieved with the clamp.

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Jataí is a pollinator of some crops; therefore, its sustainable management guarantees quality in the ecosystem services provided and implementation in precision agriculture. We acquired videos of natural and artificial hives in urban and rural environments with a camera positioned at the hive entrance. In this way, we obtained videos of the entrance of several colonies for multiple bee tracking and removed images from the videos for bee detectors. This data, their respective labels, and metadata make up the dataset. The dataset displays potential for utilization in computer vision tasks such as comparative studies of deep learning models. They can also integrate intelligent monitoring systems for natural and artificial hives.

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Digital image datasets for Precision Agriculture (PA) still need to be available. Many problems in this field of science have been studied to find solutions, such as detecting weeds, counting fruits and trees, and detecting diseases and pests, among others. One of the main fields of research in PA is detecting different crop types with aerial images. Crop detection is vital in PA to establish crop inventories, planting areas, and crop yields and to have information available for food markets and public entities that provide technical help to small farmers. This work proposes public access to a digital image dataset for detecting green onion and foliage flower crops located in the rural area of Medellín City - Colombia. This dataset consists of 245 images with their respective labels: green onion (Allium fistulosum), foliage flowers (Solidago Canadensis and Aster divaricatus), and non-crop areas prepared for planting. A total of 4315 instances were obtained, which were divided into subsets for training, validation, and testing. The classes in the images were labeled with the polygon method, which allows training machine learning algorithms for detection using bounding boxes or segmentation in the COCO format.

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Drought and salinity stresses present significant challenges that exert a severe impact on crop productivity worldwide. Understanding the dynamics of salicylic acid (SA), a vital phytohormone involved in stress response, can provide valuable insights into the mechanisms of plant adaptation to cope with these challenging conditions. This paper describes and tests a sensor system that enables real-time and non-invasive monitoring of SA content in avocado plants exposed to drought and salinity. By using a reverse iontophoretic system in conjunction with a laser-induced graphene electrode, we demonstrated a sensor with high sensitivity (82.3 nA/[µmol L-1⋅cm-2]), low limit of detection (LOD, 8.2 µmol L-1), and fast sampling response (20 s). Significant differences were observed between the dynamics of SA accumulation in response to drought versus those of salt stress. SA response under drought stress conditions proved to be faster and more intense than under salt stress conditions. These different patterns shed light on the specific adaptive strategies that avocado plants employ to cope with different types of environmental stressors. A notable advantage of the proposed technology is the minimal interference with other plant metabolites, which allows for precise SA detection independent of any interfering factors. In addition, the system features a short extraction time that enables an efficient and rapid analysis of SA content.

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Climate instability directly affects agro-environments. Water scarcity, high air temperature, and changes in soil biota are some factors caused by environmental changes. Verified and precise phenotypic traits are required for assessing the impact of various stress factors on crop performance while keeping phenotyping costs at a reasonable level. Experiments which use a lysimeter method to measure transpiration efficiency are often expensive and require complex infrastructures. This study presents the development and testing process of an automated, reliable, small, and low-cost prototype system using IoT with high-frequency potential in near-real time. Because of its waterproofness, our device-LysipheN-assesses each plant individually and can be deployed for experiments in different environmental conditions (farm, field, greenhouse, etc.). LysipheN integrates multiple sensors, automatic irrigation according to desired drought scenarios, and a remote, wireless connection to monitor each plant and device performance via a data platform. During testing, LysipheN proved to be sensitive enough to detect and measure plant transpiration, from early to ultimate plant developmental stages. Even though the results were generated on common beans, the LysipheN can be scaled up/adapted to other crops. This tool serves to screen transpiration, transpiration efficiency, and transpiration-related physiological traits. Because of its price, endurance, and waterproof design, LysipheN will be useful in screening populations in a realistic ecological and breeding context. It operates by phenotyping the most suitable parental lines, characterizing genebank accessions, and allowing breeders to make a target-specific selection using functional traits (related to the place where LysipheN units are located) in line with a realistic agronomic background.

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BACKGROUND: Coffee farming constitutes a substantial economic resource, representing a source of income for several countries due to the high consumption of coffee worldwide. Precise management of coffee crops involves collecting crop attributes (characteristics of the soil and the plant), mapping, and applying inputs according to the plants' needs. This differentiated management is precision coffee growing and it stands out for its increased yield and sustainability. RESULTS: This research aimed to predict yield in coffee plantations by applying machine learning methodologies to soil and plant attributes. The data were obtained in a field of 54.6 ha during two consecutive seasons, applying varied fertilization rates in accordance with the recommendations of soil attribute maps. Leaf analysis maps also were monitored with the aim of establishing a correlation between input parameters and yield prediction. The machine-learning models obtained from these data predicted coffee yield efficiently. The best model demonstrated predictive fit results with a Pearson correlation of 0.86. Soil chemical attributes did not interfere with the prediction models, indicating that this analysis can be dispensed with when applying these models. CONCLUSION: These findings have important implications for optimizing coffee management and cultivation, providing valuable insights for producers and researchers interested in maximizing yield using precision agriculture. © 2024 Society of Chemical Industry.

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The uniformity of seed distribution and sowing speed directly impact crop quality and productivity. This experiment assessed how the position of the sowing monitoring sensor influences the distribution of cotton seeds using a pneumatic meter at different operating speeds. The experiment employed a completely randomized two-factor factorial design on a static simulation bench. The first factor involved the sensor installation sites (upper, middle, and lower portions of the conductor tube and conveyor belt), while the second factor encompassed simulated speeds of 3.0, 5.0, 7.0, 9.0, and 11.0 km/h. Parameters such as frequency of double, flawed, and acceptable spacing, coefficient of variation, and precision index were measured based on five replications of 250 consecutive spacing. The results indicated that the sensor's placement significantly influences reading accuracy. Optimal results were observed when the sensor was positioned at the final portion of the conductor tube, providing more accurate seed deposition, and facilitating decision-making.

A uniformidade de distribuição de sementes no solo e a velocidade de semeadura estão diretamente relacionados à qualidade e produtividade da lavoura. O objetivo do experimento foi avaliar a influência da posição de instalação do sensor de monitoramento de semeadura, em relação a leitura realizada em bancada de ensaio, durante a distribuição de sementes de algodão com dosador pneumático, submetido a diferentes velocidades operacionais. O experimento foi conduzido em bancada estática de simulação, com delineamento inteiramente casualizado, fatorial duplo, sendo o primeiro fator o local de instalação do sensor (porção superior, média e inferior do tubo condutor e esteira condutora) e o segundo as velocidades simuladas de 3,0; 5,0; 7,0; 9,0 e 11,0 km h−1. Os parâmetros mensurados para a avaliação da distribuição das sementes foram a frequência de espaçamentos duplos, falhos e aceitáveis, seu coeficiente de variação e índice de precisão, mensurados a partir de cinco repetições de 250 espaçamentos consecutivos. Os resultados obtidos foram que a posição de inserção do sensor de monitoramento interfere diretamente na eficiência da leitura, a qual tende a ser mais assertiva quando o sensor é posicionado na porção final do tubo condutor, demonstrando com maior acurácia a real deposição e assim facilitando a tomada de decisão.

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Global navigation satellite systems (GNSSs) became an integral part of all aspects of our lives, whether for positioning, navigation, or timing services. These systems are central to a range of applications including road, aviation, maritime, and location-based services, agriculture, and surveying. The Global Positioning System (GPS) Standard Position Service (SPS) provides position accuracy up to 10 m. However, some modern-day applications, such as precision agriculture (PA), smart farms, and Agriculture 4.0, have demanded navigation technologies able to provide more accurate positioning at a low cost, especially for vehicle guidance and variable rate technology purposes. The Society of Automotive Engineers (SAE), for instance, through its standard J2945 defines a maximum of 1.5 m of horizontal positioning error at 68% probability (1σ), aiming at terrestrial vehicle-to-vehicle (V2V) applications. GPS position accuracy may be improved by addressing the common-mode errors contained in its observables, and relative GNSS (RGNSS) is a well-known technique for overcoming this issue. This paper builds upon previous research conducted by the authors and investigates the sensitivity of the position estimation accuracy of low-cost receiver-equipped agricultural rovers as a function of two degradation factors that RGNSS is susceptible to: communication failures and baseline distances between GPS receivers. The extended Kalman filter (EKF) approach is used for position estimation, based on which we show that it is possible to achieve 1.5 m horizontal accuracy at 68% probability (1σ) for communication failures up to 3000 s and baseline separation of around 1500 km. Experimental data from the Brazilian Network for Continuous Monitoring of GNSS (RBMC) and a moving agricultural rover equipped with a low-cost GPS receiver are used to validate the analysis.

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The synergy between eco-friendly biopolymeric films and printed devices leads to the production of plant-wearable sensors for decentralized analysis of pesticides in precision agriculture and food safety. Herein, a simple method for fabrication of flexible, and sustainable sensors printed on cellulose acetate (CA) substrates has been demonstrated to detect carbendazim and paraquat in agricultural, water and food samples. The biodegradable CA substrates were made by casting method while the full electrochemical system of three electrodes was deposited by screen-printing technique (SPE) to produce plant-wearable sensors. Analytical performance was assessed by differential pulse (DPV) and square wave voltammetry (SWV) in a linear concentration range between 0.1 and 1.0 µM with detection limits of 54.9 and 19.8 nM for carbendazim and paraquat, respectively. The flexible and sustainable non-enzymatic plant-wearable sensor can detect carbendazim and paraquat on lettuce and tomato skins, and also water samples with no interference from other pesticides. The plant-wearable sensors had reproducible response being robust and stable against multiple flexions. Due to high sensitivity and selectivity, easy operation and rapid agrochemical detection, the plant-wearable sensors can be used to detect biomarkers in human biofluids and be used in on-site analysis of other hazardous chemical substances.

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Currently, Brazil is one of the world's largest grain producers and exporters. Agriculture has already entered its 4.0 version (2017), also known as digital agriculture, when the industry has entered the 4.0 era (2011). This new paradigm uses Internet of Things (IoT) techniques, sensors installed in the field, network of interconnected sensors in the plot, drones for crop monitoring, multispectral cameras, storage and processing of data in Cloud Computing, and Big Data techniques to process the large volumes of generated data. One of the practical options for implementing precision agriculture is the segmentation of the plot into management zones, aiming at maximizing profits according to the productive potential of each zone, being economically viable even for small producers. Considering that climate factors directly influence yield, this study describes the development of a sensor network for climate monitoring of management zones (microclimates), allowing the identification of climate factors that influence yield at each of its stages.•Application of the internet of things to assist in decision making in the agricultural production system.•AgDataBox (ADB-IoT) web platform has an Application Programming Interface (API).•An agrometeorological station capable of monitoring all meteorological parameters was developed (Kate 3.0).

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El monitoreo del contenido de humedad en el suelo es especialmente importante, ya que proporciona información relevante para tomar decisiones acertadas, en cuanto a riego, fertirriego y manejo del estrés hídrico. Este trabajo tiene como objetivo validar un modelo de estimación del contenido de agua en el suelo, mediante espectroscopía de reflectancia difusa en el rango del infrarrojo cercano. Los suelos evaluados provienen de los municipios de Puerto Gaitán (Meta), Espinal (Tolima) y Mosquera (Cundinamarca). En los dos primeros se establecieron redes rígidas, para seleccionar los puntos de muestreo y empleando dos profundidades en cada caso (0-10 y 10-30; 0-10 y 10-25 cm, respectivamente). Para el tercero, se describieron 77 calicatas y se tomaron muestras a 0-10 y 10-35 cm de profundidad. Posteriormente, se evaluó el contenido de humedad considerando 0, 15 y 30 % de humedad. Los datos obtenidos se analizaron con estadística descriptiva. Se empleó la validación cruzada y externa para cada modelo y se obtuvo un modelo general, a partir de los datos de los tres sitios. Los modelos obtenidos para cada sitio de muestreo y el modelo general tienen buena capacidad predictiva. Según los resultados, se afirma que la espectroscopía de reflectancia difusa NIR es una excelente opción para determinar el contenido de agua en el suelo. De igual manera, a partir del análisis de componentes principales, se identificó una diferenciación entre contenidos de agua de los suelos estudiados.

Monitoring soil moisture content is especially important as it provides relevant information for making informed decisions regarding irrigation, fertigation, and water stress management. This study aims to validate a model for estimating soil water content using diffuse reflectance spectroscopy in the near-infrared range. The evaluated soils come from the municipalities of Puerto Gaitán (Meta), Espinal (Tolima), and Mosquera (Cundinamarca). In the first two municipalities, rigid networks were established to select sampling points, with two depths considered for each case (0-10 and 10-30 cm; 0-10 and 10-25 cm, respectively). For the third municipality, 77 soil pits were described, and samples were taken at depths of 0-10 and 10-35 cm. Subsequently, moisture content was evaluated at 0, 15, and 30 % moisture levels. The obtained data were analyzed using descriptive statistics. Cross-validation and external validation were applied to each model, and a general model was developed based on the data from all three sites. The obtained models for each sampling site and the general model demonstrated good predictive capacity. Based on the results, it is affirmed that near-infrared diffuse reflectance spectroscopy is an excellent option for determining soil water content. Similarly, principal component analysis identified differentiation between water contents of the studied soils.

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Monitoring the climatic conditions of crops is essential for smart agriculture development and adaptation of agricultural systems in the era of global change. Thereby, it is possibly better to understand the stages of development of the crop, thus adopting management practices more efficiently and planning the harvest with greater accuracy. This study was developed to analyze the growing degree-hours and degree-days in two management zones (MZs) for each phenological stage of wheat (Triticum aestivum L.) and the application of low-cost agroclimatological stations to monitor the climatic conditions of the field production. The study was developed in a Ferralsol in Céu-Azul/Brazil. Ten low-cost agrometeorological stations were installed in two MZs delineated based on elevation data using the web platform AgDataBox. Data on solar radiation, atmospheric pressure, wind speed, precipitation, relative humidity, air, and soil temperature were evaluated over two wheat crop seasons. Our results showed different climatic conditions, especially humidity and temperature, between MZs and crop seasons, which could probably cause yield variability. By the low-cost agroclimatological stations, it is possible to collect data on the thermal accumulation by the culture in growing degree-hours, which is a more accurate parameter than the growing degree-days (commonly used in similar studies). With the growing degree-hours data, it was possible to follow the development of the phenological stages of wheat. In conclusion, the results obtained suggest the importance of evaluating agroclimatological parameters in monitoring wheat crops. However, more studies are needed in regions with greater slopes, which may have microclimates that intensely influence the crop.

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This article presents the capture protocol to acquire hyperspectral images, which can be used to quantify the concentration of total phosphorus in soil samples. 152 soil samples were prepared, and a hyperspectral cube made up of 145 images in the VIS-NIR bands, between 420 and 1000 nm, was obtained from each of them. The images obtained were taken with the Bayspec OCIF Series hyperspectral camera, in push-broom function, using a platform that includes an illumination system that offers a continuous spectrum in the range of interest. The samples were prepared with a soil from the Santander de Quilichao region, Cauca, Colombia, and mixed with known concentrations of P2O5 fertilizer, so that a total mass of 50 g was obtained. Each sample was deposited in a round black plastic container, 6 cm in diameter and a depth of 1 cm. The soil samples were analyzed in the laboratory to establish the concentration of total phosphorus. Therefore, the database is made up of the images associated with the hyperspectral cube of each sample, and four tables: the first describes the properties of the soil used to obtain the mixtures, the second the composition of the fertilizer used, the third describes the soil-fertilizer ratio to make up the samples, and the fourth was the laboratory analysis of the total phosphorus content of the analyzed samples.

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The usage of spatial tools might be helpful in the optimization of decision-making regarding soil management, with technologies that assist in the interpretation of information related to soil fertility. Therefore, the present study evaluated the spatial variability of chemical attributes of the soil under an agroforestry system compared to a native forest in the municipality of Tomé-açu, Eastern Amazon, Brazil. Soil samples were performed at 36 points arranged in a 55 x 55 m grid. The soils were prepared and submitted to analysis in order to determine pH in H2O, exchangeable calcium, magnesium, potassium and aluminium, available phosphorus, potential acidity, organic matter, bases saturation and aluminium saturation. For each soil attribute, the spherical, gaussian and exponential models were adjusted. After the semivariograms fitting, data interpolation for assessment of spatial variability of the variables was performed through ordinary kriging. The spherical and gaussian models were the most efficient models in estimation of soil attributes spatial variability, in most cases. Most of variables presented a regular spatial variability in their respective kriging maps, with some exceptions. In general, the kriging maps can be used, and we can take them as logistical maps for management and intervention practices in order to improve the soil fertility in the study areas. The results principal components indicate the need for integrated management of soil chemical attributes, with localized application of acidity correctors, fertilizers and other types of incomes, using the spatial variability of these fertility variables.

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In recent decades, research on precision irrigation driven by climate change has developed a multitude of strategies, methods and technologies to reduce water consumption in irrigation projects and to adapt to the increasing occurrence of water scarcity, agricultural droughts and competition between agricultural and industrial sectors for the use of water. In this context, the adoption of water-saving and application practices implies a multidisciplinary approach to accurately quantify the water needs of crops under different water availability and management practices. Thus, this review article presented a review of technologies and new trends in the context of precision irrigation, future perspectives and critically analyze notions and means to maintain high levels of land and water productivity, which minimize irrational water consumption at the field level.

Nas últimas décadas pesquisas voltadas à irrigação de precisão, impulsionadas pelas mudanças climáticas, desenvolveram uma infinidade de estratégias, métodos e tecnologias para reduzir o consumo de água em projetos de irrigação, para adaptação à crescente ocorrência de escassez de água, secas agrícolas e competição entre os setores agrícolas e industriais pelo uso da água. Nesta conjuntura, a adoção de práticas de economia e aplicação de água, implica em uma abordagem multidisciplinar para a quantificação precisa das necessidades de água das culturas, sob diversas práticas de disponibilidade e manejo da água. Dessa forma, este artigo de revisão tem como objetivo apresentar uma revisão sobre as tecnologias e novas tendências no contexto da irrigação de precisão, as perspectivas futuras e analisar criticamente noções e meios para manter altos índices de produtividade da terra e da água, que minimizem o consumo de água irracional a nível de campo.

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Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

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Proximal soil sensing technologies, such as visible and near infrared diffuse reflectance spectroscopy (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS), are dry-chemistry techniques that enable rapid and environmentally friendly soil fertility analyses. The application of XRF and LIBS sensors in an individual or combined manner for soil fertility prediction is quite recent, especially in tropical soils. The shared dataset presents spectral data of VNIR, XRF, and LIBS sensors, even as the characterization of key soil fertility attributes (clay, organic matter, cation exchange capacity, pH, base saturation, and exchangeable P, K, Ca, and Mg) of 102 soil samples. The samples were obtained from two Brazilian agricultural areas and have a wide variation of chemical and textural attributes. This is a pioneer dataset of tropical soils, with potential to be reused for comparative studies with other datasets, e.g., comparing the performance of sensors, instrumental conditions, and/or predictive models on different soil types, soil origin, concentration range, and agricultural practices. Moreover, it can also be applied to compose soil spectral libraries that use spectral data collected under similar instrumental conditions.

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Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, aerial, satellite, ancillary data), this has been geographically unequal, causing certain areas to be virtually devoid of useful data to help face their specific challenges. However, even in areas with available resources and good infrastructure, data and knowledge gaps are still prevalent, because agricultural environments are mostly uncontrolled and there are vast numbers of factors that need to be taken into account and properly measured for a full characterization of a given area. As a result, data from a single sensor type are frequently unable to provide unambiguous answers, even with very effective algorithms, and even if the problem at hand is well defined and limited in scope. Fusing the information contained in different sensors and in data from different types is one possible solution that has been explored for some decades. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. While some success has been achieved, there are still many challenges that prevent a more widespread adoption of this type of approach. This is particularly true for the highly complex environments found in agricultural areas. In this article, we provide a comprehensive overview on the data fusion applied to agricultural problems; we present the main successes, highlight the main challenges that remain, and suggest possible directions for future research.

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Impedimetric wearable sensors are a promising strategy for determining the loss of water content (LWC) from leaves because they can afford on-site and nondestructive quantification of cellular water from a single measurement. Because the water content is a key marker of leaf health, monitoring of the LWC can lend key insights into daily practice in precision agriculture, toxicity studies, and the development of agricultural inputs. Ongoing challenges with this monitoring are the on-leaf adhesion, compatibility, scalability, and reproducibility of the electrodes, especially when subjected to long-term measurements. This paper introduces a set of sensing material, technological, and data processing solutions that overwhelm such obstacles. Mass-production-suitable electrodes consisting of stand-alone Ni films obtained by well-established microfabrication methods or ecofriendly pyrolyzed paper enabled reproducible determination of the LWC from soy leaves with optimized sensibilities of 27.0 (Ni) and 17.5 kΩ %-1 (paper). The freestanding design of the Ni electrodes was further key to delivering high on-leaf adhesion and long-term compatibility. Their impedances remained unchanged under the action of wind at velocities of up to 2.00 m s-1, whereas X-ray nanoprobe fluorescence assays allowed us to confirm the Ni sensor compatibility by the monitoring of the soy leaf health in an electrode-exposed area. Both electrodes operated through direct transfer of the conductive materials on hairy soy leaves using an ordinary adhesive tape. We used a hand-held and low-power potentiostat with wireless connection to a smartphone to determine the LWC over 24 h. Impressively, a machine-learning model was able to convert the sensing responses into a simple mathematical equation that gauged the impairments on the water content at two temperatures (30 and 20 °C) with reduced root-mean-square errors (0.1% up to 0.3%). These data suggest broad applicability of the platform by enabling direct determination of the LWC from leaves even at variable temperatures. Overall, our findings may help to pave the way for translating "sense-act" technologies into practice toward the on-site and remote investigation of plant drought stress. These platforms can provide key information for aiding efficient data-driven management and guiding decision-making steps.

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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.