In the search for greater precision and agility in the assessment of diseases and the application of fungicides in At the most opportune moment, the use of technology has been increasing in the field. It is the case of techniques used based on vegetation indices, used for the improvement of research into the control of ramularia spot in cotton crops.
Leaf diseases can cause major changes to the plant canopy, either through defoliation or due to different pigmentation in the leaves. An example is the ramularia stain (Ramularia areola), disease considered main part of the cotton crop. Its symptoms manifest in both sides of the sheet, particularly the lower one. Initially, they are characterized by lesions bluish on the underside of the leaf evolving to white color throughout leaf, angular in shape and cottony in appearance, causing the leaf to fall. When the fungal attack begins early, as has occurred in recent years, intense defoliation occurs, reducing the healthy leaf area and thus, the bolls that are in the last positions do not receive the necessary nutrients and end up being aborted by the plant or resulting in less weight, which causes the reduction of production.
A Cotton cultivation is extremely technical due to the high cost of production and also its sensitivity to attack by pests and diseases. Same having one of the highest profitability among major crops, the planted area recorded a reduction in the cerrado region in recent years, showing how complex it is the management of this crop. Because of this, research in this culture proves to be more important every day. A work based on the partnership between Chapadão Agricultural Research Support Foundation (Fundação Chapadão) and the Federal University of Mato Grosso do Sul (campus of Chapadão do Sul) was carried out to compare different vegetation indices and treatments with fungicide applications to control leaf spot ramularia in cotton cultivation, with the aim of creating new techniques for evaluating diseases in crops and improving research.
Since beginning of the research, the results were very satisfactory, for example of the images obtained from the culture at phenological stage C3, when they are open the third position hoods, from the Sequoia sensor coupled to a Vehicle Unmanned Aerial (UAV). Figure 1 shows the Foundation's experimental field Chapadão, where cotton was cultivated, focusing on research into different management of ramularia spot.
The coloring of the image (Figure 1), shows in green the highest values of the vegetation index (in this case the NDVI). These higher values reflect areas with greater leaf coverage. In Yellow are the average values and red are the lowest values, which show the places with the least amount of vegetation.
The longitudinal marks on the image correspond to the tracks of the tractor used to carry out the application of maintenance of the area. Furthermore, a stripe can be seen cutting through the area close to half in a horizontal direction, a little inclined where a level curve is found.
From within this plot, it was an experiment was removed in which the effectiveness of fungicides used was compared in the control of ramularia spot with the value of the vegetation index and a large correlation was noted, as shown in Figure 2.
On the left of figure 2 is the scale of effectiveness values, which are obtained through the evaluation method of diseases known as the Area Under the Disease Progress Curve (AACPD), represented by the blue columns. On the right side are the values of one of the vegetation indices studied, with the highest values corresponding to greater amount of vegetation. At the bottom of the graph are the numbers referring to to each of the phytosanitary treatments used to control leaf spot ramularia, with treatment 1 being considered control (without any application of fungicides). It can be clearly seen that the vegetation index precisely follows the efficiency curve of each fungicide treatment.
The results were surprising and some vegetation indices showed greater correlation with the efficiency of the disease control than crop productivity itself. This means that if deals with a new way of evaluating and quantifying ramularia spot in cotton, thus proving the viability of this technique.
In few years it is expected that this technique can be used both in research agricultural and rural property, as another assessment tool of foliar diseases, improving assessment techniques, so that they can different treatments for crop foliar diseases can also be improved.
Intensify the Crop monitoring can be considered a future perspective for cotton production. Such monitoring is the safest, most practical and cheap that allows the farmer to apply fungicides to the disease control at the most opportune time, with maximum benefit from the entire available technical knowledge, and even adopt other control methods (physical, cultural and biological).
In global agriculture, many techniques have been used to based on vegetation indices, a relatively new tool that provides of various uses. To understand the range of possibilities that are covered by this tool, it is necessary to understand what is really treats.
In short, vegetation indices use two or more measurements reflectance, which is the amount of light reflected by the object divided into spectrum bands. These bands, also called bands, when located in the region of the visible spectrum (Figure 3), represent the colors that are reflected.
Within each band there is also a variation in the amount of light reflected by the object, individually, and due to this it is possible to generate a number inserted into specific known mathematical models as Vegetation Indices.
These measurements can be obtained from numerous types of sensors, which range from manual equipment, operated in a simpler way than transmit information in real time. Also sensors attached to aerial vehicles, such as Drones and Unmanned Aerial Vehicles (UAVs), and orbital sensors sent to the space next to satellites. All of these sensors can still be active, which capture the artificial light emitted by the equipment itself or passives, which capture sunlight reflected by the object.
In addition to the versatility provided by vegetation indices, the Access to information has also made great progress. Satellites like CEBERS and LANDSAT offers its information in a free and accessible way. Furthermore There are companies specialized in crop imaging.
In the field, vegetation indices have already been used, for example, for the preparation of soil maps, assessment of soil uniformity farming, detection of pest attacks in reboleira and even for estimating production of some crops.
In recent years, this tool has proven to be very useful to agriculture. Research indicates that these indexes aim to understand canopy variables and serve as a basis in many sensing applications remote for crop management, as they are correlated to several important biophysical properties (Ahamed et al., 2011). Vegetation indices, according to JENSEN (2009), can be correlated to the leaf area index (LAI); to the percentage of green roof; the chlorophyll content; to green biomass and (v) radiation photosynthetically absorbed (RFAA).
Also each of the parameters agronomic variables may be more correlated with a specific band or with a vegetation index. For example, the Red Edge (RE) spectral band is highly correlated with leaf nitrogen content and amount of chlorophyll. The Near Infra Red (NIR) spectral band is more associated with coverage and green biomass.
Theoretically, vegetation indices can capture variations in plant canopy. And this variation depends, mainly, the quantity of leaves and the architecture of the canopy (Ponzoni et al., 2012). Thus, all types of Modifications to the number of sheets or architecture will have an effect on the reflectance and, consequently, vegetation indices.
Article published in issue 222 of Cultivar Grandes Culturas, November, 2017.
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