Soybean is the main commodity of Brazilian agribusiness, and the country stands out for the largest world production of this oilseed. The culture is carried out under two main forms of cultivation, conventional and in the form of no tillage. The possibility of two to three agricultural crops per year contributes to the emergence of various plant protection problems, including soybean rust, the stinkbug complex, defoliating caterpillars, nematodes, in addition to competition with weeds. Thus, the purpose of this chapter is to describe the main application techniques of chemical or biological products in the control of agents that are harmful to the soybean crop, as well as to bring technological innovations involving remote sensing, unmanned aerial vehicle, and other techniques of application in the control of these harmful agents to the crop. Also comment on the benefits of spray adjuvants and the limitations of tank-mixes with plant protection products intended for soybean cultivation.

The detection and monitoring of soybean rust (SBR) through remote sensing is promising because of the importance of the crop and the aspects of the disease.We evaluated the effects of different levels of SBR severity on soybean [Glycine max (L.) Merr.] leaflets reflectance aiming for the construction of a disease classification model. Leaflet reflectance was evaluated on two cultivars (susceptible and partially resistant) at four disease severity levels: healthy, low, moderate, and high.Leaflets were collected in the field and taken to the laboratory for spectral evaluation through the spectrophotometer UV 2700 coupled with Integrating Sphere Attachment ISR-603, in the range of 270-1000 nm. The feasibility of using a collection of vegetation indices (VIs) and data dimensionality reduction through multiple factor analysis (MFA) was evaluated, and a classification model was constructed. Ten algorithms were assessed based on precision, sensibility, and accuracy parameters, using 80% of the dataset as training data and 20% as testing dataset. The visible range and red edge region contributed more significantly to the disease prediction and classification model. The MFA performed satisfactorily in the dimensionality reduction and unveiled the effect of specific wavelengths on the classification of each class. Most of the VIs studied had high correlation performance across the severity classes. Classification accuracy and precision were >70% for all models. Linear support vector machine with the collection of VIs achieved the best results. This study provides a practical path for developing a detection model to be integrated into SBR management programs.

Adjuvants mayimprove control efficiency of volunteer RR ® corn with ACCase inhibiting herbicides. Thus, the aim of this study was to evaluate the effects of adjuvant addition to the herbicides clethodim and quizalofop, on surface tension, spray deposition and efficiency of volunteer RR ® corn control. The surface tension of clethodim (96 g a.i. ha -1 ) and quizalofop (60 g a.i. ha -1 ) herbicides with and without a mineral oil, a vegetable oil and an organosilicon adjuvant was evaluated at concentrations of 0.01; 0.05; 0.1; 0.5; 1.0 and 2.0% v v -1 . To evaluate deposition and visual control efficiency, the same herbicides associated or not with the mineral oil (0.5% v v -1 ), the vegetable oil (0.5% v v -1 ) and the organosilicon adjuvant (0, 05% v v -1 ) were used. For this, volunteer RR ® corn plants were grown in a greenhouse until stage V2-V3. The adjuvants reduced the surface tension of the herbicides clethodim and quizalofop, organosilicon was the most efficient.Adjuvants does not alter spray deposition of herbicides on corn plants. Mineral oil increases potential control of clethodim herbicide and anticipates control with quizalofop herbicide.

The application of remote sensing techniques and prediction models for soybean rust (SBR) monitoring may result in different fungicide application timings, control efficacy, and spraying performance. This study aimed to evaluate the applicability of a prediction model as a threshold for disease control decision-making and to identify the effect of different application timings on SBR control as well as on the spraying technology. There were two experimental trials that were conducted in a 2 × 4 factorial scheme: 2 cultivars (susceptible and partially resistant to SBR); and four application timings (conventional chemical control at a calendarized system basis; based on the prediction model; at the appearance of the first visible symptoms; and control without fungicide application). Spray deposit and coverage at each application timing were evaluated in the lower and upper region of the soybean canopy through quantitative analysis of a tracer and water-sensitive papers. The prediction model was calculated based on leaf reflectance data that were collected by remote sensing. Application timings impacted the application technology as well as control efficacy. Calendarized system applications were conducted earlier, promoting different spray performances. Spraying at moments when the leaf area index was higher obtained poorer distribution. None of the treatments were capable of achieving high spray penetration into the canopy. The partially resistant cultivar was effective in holding disease progress during the crop season, whereas all treatments with chemical control resulted in less disease impact. The use of the prediction model was effective and promising to be integrated into disease management programs.

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