ABSTRACT Global consumption of oilseeds has been growing progressively in the last five growing seasons, in which soybean represents 60% of this sector. Thus, in order to maintain a high production in the region of Rio Verde, State of Goiás, against the phytopathological problems, this study aimed to define the best spectral range for the detection of H. glycines and P. brachyurus by linear regressions in soybean at R3 stage, as well as the elaboration of mathematical models through multiple linear regressions. For this, soil and root were sampled in the experimental area, as well as a flight was performed with the Sentera sensor. Data were used for the elaboration of regressions and for the validation of 2 mathematical models. Significant values were observed in simple linear regression only for cysts, in the visible range, with a good R² value for the Green, Red and 568 nm bands, to nonviable cysts. When working with the stepwise statistics, better results are found for H. glycines, which now has an R²(aj) of 0.7430 and P. brachyurus is then detected. From the mathematical model obtained with the multiple linear regression for non-viable cysts with an R²(aj) of 0.7430, it is possible to detect the spatial distribution of nematodes across the soybean field, in order to perform a localized management, optimizing the applications. Good results are also possible using the mathematical model obtained by simple linear regression.

ABSTRACT Images from unmanned aerial vehicles (UAV) can serve as a baseline for studies in weed science, complementing observations obtained in the ground. The objective of this work was to determine soybean (Glycine max (L.) Merr.) plant injury caused by pre-emergence herbicides in sandy and clayey soils, using a low-cost UAV. The experiment was conducted in a randomized complete block design, with four replicates and seven treatments consisted of herbicides (diclosulam, chlorimuron, sulfentrazone, flumioxazin, and S-metolachlor), hand weeded and untreated treatments. Ground-based evaluations were carried out to assess soybean crop injury, plant stand, leaf chlorophyll content, plant height, canopy distance and grain yield. Images were taken using a UAV equipped with an RGB (red green and blue) camera. Soybean plants sprayed with diclosulam had lower plant reflectance in the R (98.9), G (147.1) and B (74.3) range than the other treatments in sandy soil. In clayey soil, hand weeded treatment had higher plants (30.8 cm) and untreated favored smaller plants (24.9 cm) compared to herbicide treatments. In sandy soil, soybean yield of all treatments was similar, however in clayey soil, soybean yield treated with chlorimuron and flumioxazin was higher than 5000 kg ha-1 and better than the others treatments. The nutrient-poor soil (sandy) may have aggravated the plant injury caused by herbicides and explain the lower yield observed compared to clayey soil. It was determined soybean plant injury caused by pre-emergence herbicides with the use of unmanned aerial vehicles, providing complementary results to ground-based measurements, indicating the potential of this technology for low-cost evaluations in weed science.

RESUMO: Esse estudo avaliou a eficácia e as respostas espectrais de plantas de soja a quinze misturas de fungicidas utilizados no controle da ferrugem asiática da soja (FAS). A refletância do nível do dossel foi medida usando uma câmera multiespectral a bordo de um drone multirotor antes e duas horas após cada pulverização. A terceira aplicação de fungicidas melhorou o controle de FAS e aumentou a produtividade. Porém, três aplicações foliares consecutivas de fungicidas não afetaram a refletância de plantas de soja nos comprimentos de onda visível e infravermelho. Assim, drones podem ser uma estratégia viável para aquisição de dados independentemente da aplicação de fungicidas.

ABSTRACT: The study evaluated the efficacy and soybean spectral responses to fifteen foliar fungicide mixtures labeled to control Asian soybean rust. Canopy level reflectance was measured using a multispectral camera onboard a multirotor drone before and two hours after each spray. The third application of fungicides improved control of soybean rust and increased yield. Nevertheless, up to three consecutive foliar fungicides applications did not affect the reflectance of soybean plants at visible and infrared wavelengths. Thus, drones can be a viable strategy for data acquisition regardless of the application of the fungicides.

ABSTRACT The objective of this research was to evaluate weed control in a successional soybean-sorghum system by using preemergent herbicides. Two trials were conducted in soybean and two in sorghum, in different soil types (sandy in Rio Verde city and clayey in Montividiu city). All trials were established in a completely randomized block design with five preemergent herbicides in soybean (rates in): diclosulam 35.3 g a.i.·ha–1, chlorimuron 20 g a.i.·ha–1, sulfentrazone 200 g a.i.·ha–1, flumioxazin 50 g a.i.·ha–1, S-metolachlor 1728 g a.i.·ha–1, and two controls (hand weeded and untreated). Treatments in sorghum trials were the same to the soybean plus atrazine 1250 g a.i.·ha–1 and atrazine 1250 g a.i.·ha–1 + S-metolachlor 1728 g a.i.·ha–1. All treatments had four replicates. Weed control was assessed at 7, 14, 21 and 28 days after planting (DAP) in both crops. In addition, yield was measured when grains reached physiological maturity. All preemergent herbicide treatments successfully controlled weeds, specially Commelina benghalensis, Cenchrus echinatus and Eleusine indica, in both soybean trials until 28 DAP. In some weeds of sorghum, sulfentrazone, diclosulam and chlorimuron sprayed at soybean preemergence performed better than atrazine sprayed at sorghum preemergence. All preemergent herbicides sprayed at soybean preemergence did not affect soybean and sorghum yield, showing similarity with the hand weeded treatment. The results of this research provide evidence that the mix of crop succession and preemergent herbicide applications can be a strong strategy for integrated weed management.

RESUMO: A soja é uma das principais espécies de planta cultivadas no mundo. Todavia, perdas de produtividade são ocasionadas por vários tipos de estresses, incluindo os nematoides H. glycines e P. brachyurus. Como objetivo, buscou-se determinar a melhor banda espectral para a detecção do H. glycines e P. brachyurus com o uso de modelos de regressões lineares simples e definir um modelo matemático de regressão linear múltiplo para sua detecção, no início do florescimento (R1). Para isto, foram definidos nove pontos de coleta em cinco reboleiras, totalizando 45 pontos. As coletas foram feitas em um padrão específico de distâncias, de forma a ter amostras com tipos variados de populações de nematoides. Foram realizados voos com o Phantom 4 Advanced, Sequoia e Sentera sobre cada uma das reboleiras. O comprimento de onda do vermelho melhor explicou a variabilidade dos dados para H. glycines no solo e na raiz, bem como dos juvenis de segundo estádio no solo. Para P. brachyurus, a RedEdge da Sentera foi a que explicou melhor a variabilidade dos dados para nematoide na raiz e a NIR da Sequoia a que melhor explicou para juvenis no solo. Quando se utilizou um modelo matemático para a detecção do P. brachyurus e H. glycines, percebe-se uma grande melhora no R² e p-valor com relação às regressões lineares simples. No início da floração (R1), a refletância espectral da soja foi associada ao número de H. glycines e P. brachyurus no solo e nas raízes, usando sensores de baixo custo e multiespectrais.

ABSTRACT: Soybean is one of the main crop species grown in the world. However, there is a decline in productivity due to the various types of stress, including the nematodes Heterodera glycines and Pratylenchus brachyurus. The objectives were to determine the best spectral band for detecting H. glycines and P. brachyurus at the beginning of flowering (R1). Soil and root sampling was conducted at nine sampling sites in each of the five nematode-infested regions, totaling 45 sampling points. Flights were made at all regions using Phantom 4 Advanced, Sequoia and 14-band customized Sentera. For H. glycines, the red spectral band best explained the variability on soil and root nematode counts as well as the second stage of juveniles in soil. For P. brachyurus, Sentera RedEdge best explained the variability in root nematode counts and Sequoia NIR best explained soil juveniles. A multiple linear regression model using spectral data for detecting P. brachyurus and H. glycines improved R² compared to simple linear regressions. At flowering growth stage (R1), soybean spectral reflectance was associated with the number of H. glycines and P. brachyurus on soil and roots using low-cost and multispectral sensors.