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.

O cultivo da cultura do milho (Zea mays L.) tem uma extrema importância econômica e social, pois ele é cultivado em praticamente todas as partes do planeta. O objetivo desse trabalho foi avaliar os atributos físicos da espiga, que melhor descrevem a massa da espiga do milho. O experimento teve sua condução realizada nas comodidades da área experimental do centro tecnológico da Agricultura Familiar (CETAF), localizado no município de Parauapebas- PA, pertencente a mesorregião Sudeste do Pára. Análise de trilha foi realizada usando o software GENES, responsável por detalhar a relação entre uma variável e outra. De forma direta, que os atributos massa total da espiga e massa de grãos da espiga, foram os atributos que melhor representam a massa da espiga. De forma indireta, observamos que a massa total da espiga, influencia significativamente a massa dos grãos da espiga.

O milho encontra-se entre os principais cereais cultivados em todo o mundo, presente na alimentação humana, animal e na indústria. A cadeia é muito produtiva e apresenta diversas vantagens, para o produtor rural, empreendedores, agroindústria e uma grande capacidade de produção. Em decorrência da grande influência do milho na economia, o investimento em novas tecnologias é constante na busca de melhorar o desempenho dessa cultura, tanto para aumentar sua resistência a diversos tipos de pragas e insetos, quanto para o aumento da produção. As diferentes cultivares do milho, são responsáveis por 50% da produtividade e a escolha da cultivar correta é crucial para o melhor desempenho possível da cultura. Os híbridos são divididos em simples, duplo e triplo e são recomendados para diferentes sistemas de produção e desempenham grande impacto na produtividade, uniformidade dos grãos, resistência a pragas e doenças e na adaptação. O milho apresenta um impacto diferente no que diz respeito a produção mundial, nacional e regional, mas sua importância econômica é consistente. O presente estudo objetivou revisar as principais características, a produção, espécies e cultivares da cultura do milho.

Using multispectral sensors attached to unmanned aerial vehicles (UAVs) can assist in the collection of morphological and physiological information from several crops. This approach, also known as high-throughput phenotyping, combined with data processing by machine learning (ML) algorithms, can provide fast, accurate, and large-scale discrimination of genotypes in the field, which is crucial for improving the efficiency of breeding programs. Despite their importance, studies aimed at accurately classifying sorghum hybrids using spectral variables as input sets in ML models are still scarce in the literature. Against this backdrop, this study aimed: (I) to discriminate sorghum hybrids based on canopy reflectance in different spectral bands (SB) and vegetation indices (VIs); (II) to evaluate the performance of ML algorithms in classifying sorghum hybrids; (III) to evaluate the best dataset input for the algorithms. A field experiment was carried out in the 2022 crop season in a randomized block design with three replications and six sorghum hybrids. At 60 days after crop emergence, a flight was carried out over the experimental area using the Sensefly eBee real time kinematic. The spectral bands (SB) acquired by the sensor were: blue (475 nm, B_475), green (550 nm, G_550), red (660 nm, R_660), Rededge (735 nm, RE_735) e NIR (790 nm, NIR_790). From the SB acquired, vegetation indices (VIs) were calculated. Data were submitted to ML classification analysis, in which three input settings (using only SB, using only VIs, and using SB + VIs) and six algorithms were tested: artificial neural networks (ANN), support vector machine (SVM), J48 decision trees (J48), random forest (RF), REPTree (DT) and logistic regression (LR, conventional technique used as a control). There were differences in the spectral signature of each sorghum hybrid, which made it possible to differentiate them using SBs and VIs. The ANN algorithm performed best for the three accuracy metrics tested, regardless of the input used. In this case, the use of SB is feasible due to the speed and practicality of analyzing the data, as it does not require calculations to perform the VIs. RF showed better accuracy when VIs were used as an input. The use of VIs provided the best performance for all the algorithms, as did the use of SB + VIs which provided good performance for all the algorithms except RF. Using ML algorithms provides accurate identification of the hybrids, in which ANNs using only SB and RF using VIs as inputs stand out (above 55 for CC, above 0.4 for kappa and around 0.6 for F-score). There were differences in the spectral signature of each sorghum hybrid, which makes it possible to differentiate them using wavelengths and vegetation indices. Processing the multispectral data using machine learning techniques made it possible to accurately differentiate the hybrids, with emphasis on artificial neural networks using spectral bands as inputs and random forest using vegetation indices as inputs.

The use of unmanned aerial spraying systems is currently being explored and applied worldwide. The objective of this study was to characterize the droplet population generated by hydraulic nozzles and centrifugal atomization nozzles used in sprayers mounted on remotely piloted aircraft (RPA). Two spray nozzle technologies were tested using a Malvern SprayTech laser particle size meter. The hydraulic nozzle evaluated was model 11001, which generates a wide-use fan spray. The centrifugal atomization nozzle, used in RPA sprayers, was manufactured by Yuenhoang, model DC12V. The experimental design was implemented in a completely randomized scheme, containing variations in the nozzles (hydraulic nozzle and centrifugal atomization nozzle) and application rate (AR) (5, 10, and 15 L ha⁻¹ in the test with the hydraulic nozzle; and 9.2, 12.8, and 15.6 L ha⁻¹ in the test with the centrifugal nozzle), with five replicates per treatment. The hydraulic nozzle test data showed a coefficient of variation of 6.8% VMD for all treatments, with droplet sizes within the fine classification ranging from 132.8 to 163.2 µm. It is noteworthy that the average relative span (span) of the droplet population generated by the hydraulic nozzle was 1.2, i.e., 20% higher than the desired reference value of 1. This value exceeds the general average reported for the centrifugal atomization nozzle, which has a span of 1.1. The relative span of the droplet size distribution for the hydraulic nozzles is greater than that observed with the centrifugal atomization nozzles. Excluding the extreme rotational speeds of the centrifugal atomization nozzle, the percentage of droplets generated with a volume smaller than 100 µm is lower compared to those produced by the hydraulic nozzle.

This work aimed to evaluate the effect of calcium on the development of corn plants grown with the omission and excess of calcium in a nutrient solution. The experiment was conducted in a greenhouse from March to May 2012. Three concentrations of calcium (0, 200, and 600 mg Ca L⁻¹) were added to the nutrient solution, which was renewed weekly, for a total of 40 days. The following variables were measured weekly: the number of leaves, average stem diameter, dry weight of the plant shoots and roots, and visual leaf diagnosis. The results showed that when the plants were deprived of calcium, their root systems were significantly reduced, as determined by the Tukey test (p ≤ 0.05). The plants with calcium deprivation had shorter roots and a dark brown color and displayed initial symptoms of chlorosis in their young leaves, which eventually led to necrosis and tipping. Hydroponics is promising and has shown satisfactory production results, contributing to the improvement of the environment, job creation, and increased profit for rural producers.