Classifications

• • A—HUMAN NECESSITIES
  • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
  • A01B—SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES OR IMPLEMENTS, IN GENERAL
  • A01B79/00—Methods for working soil
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
  • G06Q50/02—Agriculture; Fishing; Forestry; Mining

Definitions

• This invention patent application deals with a qualiquantitative measurer, particularly of a method and environment (web/application) where, through information/data collected in the field, filled in "georeferenced electronic notebook" (application) relates to Vegetation indices from satellites, whose field of application is focused on livestock based on grazing cattle. In short, it determines with greater precision (less error) the volume of grass available for consumption by cattle, allowing a more assertive adjustment of the number of animals (cattle) in the area, fostering improved efficiency in the use of the area.
• the direct sampling method depends on the cutting of samples, but it can still result in large errors, especially in places where there is great spatial variability, resulting in the need to increase the number of samples. This is a critical point, as it either increases the cost and time for measurement, or decreases accuracy.
• the double calibration (height x mass) is usually used, as there is a high correlation between the variables.
• the technical literature states that measuring height with the rising plate has advantages over the ruler, as it is faster, less laborious and more reliable.
• Figure 01 illustrates the cut of the grass mass within a frame of known area and measurement of mass on a scale.
• Table 2 shows the estimate of average green mass per square meter, multiplying the height of the grass, from table 1, by the value found in rule 3 (0.040454545 kg), item [008].
• Remote Sensing allows the identification, qualification, quantification and monitoring of areas, enabling the generation of maps, analysis of information effectively, quickly, globally and accurately, optimizing the use of natural resources and inputs.
• Silva (2016) highlights that remote sensing linked to precision agriculture is an agricultural management tool, as it considers the spatial and temporal variability of production factors at the local level. It is important to reiterate that the tool promotes sustainable management practices, as it analyzes the heterogeneity within production units that, by traditional methods, would be considered homogeneous, thus promoting data for differentiated and refined management.
• the "raw material” of remote sensing is the interaction of Electromagnetic Radiation (REM) with objects on the earth's surface, and of these, reflection is the main phenomenon.
• Artificial satellite sensors capture the emitted or reflected REM, separating them into different wavelength intervals, represented, for example, by green, red, blue, etc.
• Plants like any other object, have inherent spectral responses (eg, reflection), related to the stage of plant development, water availability, nutritional deficiencies, etc. Any change in ecological factors (animal consumption and precipitation, for example) impacts the amount of biomass and the spectral response. There are wavelengths that are absorbed by photosynthetic pigments (red and blue) and others reflected (green), and others, such as mid-infrared, correlated with the presence of water inside the leaf tissues.
• NDVI Normalized Difference Vegetation Index
• Fig. 1 illustrates cutting grass mass within a frame of known area and measuring mass on a scale
• Fig. 2 Shows Details of Ascending Plate counters
• Fig. 3 Shows area of interest with Google Earth image
• Fig. 4 Shows area of interest with RGB composition of 06/06/2020;
• Fig. 5 Shows the area of interest with RGB Compositing from 06/06/2020;
• Fig. 6 Show pixel detail and numeric value
• Fig. 7 Shows the determination of a center where the operator performs the taking of heights in an imaginary circle, distant from 2 to 4 meters from the center;
• Fig. 8 Shows orientation map for sample collection in the object area
• Fig. 9 Shows NDVI separated in Classes
• Fig. 10 Example of Regression Equation to transform height into mass
• Fig. 1 1 Shows the georeferenced map and samples
• Fig. 12 Shows example of regression equation relating NDVI to predict biomass
• Fig. 13 Shows NDVI of 12/16/2019
• Fig. 14 Shows details of the property registration
• Fig. 15 Exemplifies the choice of property using data from the Rural Environmental Registry (CAR);
• Fig. 16 Shows the operation of delimiting/drawing/naming the areas of interest, informing the type of forage, using the pickets tab;
• Fig. 17 Shows RGB Image for cloud and cloud shadow analysis;
• Fig. 18 Shows NDVI Image sent to application, which will guide sample collection;
• Fig. 19 Shows the screen of the mobile application referring to the first calibration
• Fig. 20 Shows, according to the application, the volume of grass per paddock
• Fig. 21 Shows the platform where the user subsequently enters the dry matter content and leaf percentage, reiterating that it is with the dry green leaf that the cattle are stocked;
• Fig. 22 Shows the diagram of the method of the invention.
• the pixel ( Figure 06) has a size of 10 by 10 meters. This fact is of paramount importance in the method of calibration/correlation of NDVI in Biomass. Thus invented the method that relates the biomass to the NDVI with these pixel characteristics, using the rising plate to calibrate or ruler, as discussed in item [023].
• the operator in the field should not collect samples close to trees, roads and boundaries between areas, avoiding pixel contamination by objects that have high or no biomass.
• the operator, in loco uses the georeferenced map, “navigating” over the areas of interest.
• the flow automatically transforms the measured height into grass biomass, using the regression equation of the height to mass ratio.
• the mass of the 1st calibration and the value of the vegetation index, the NDVI.
• the person responsible for the field analysis measures only heights with the ascending plate, which are automatically transformed into forage grass biomass.
• the georeferenced map it collects several samples ( Figure 1 1 - Georeferenced map and samples), as described in the method (30 sub samples to generate a sample, circle of 2 to 4 meters inside the pixel, etc).
• TMS essential multiplier value for determining the biomass available for animal consumption
• the invention already transformed into a platform and application (to be explained later), used 10 (ten) samples to develop a regression equation that transforms height into mass. After this step, 30 georeferenced height samples were collected, which were automatically transformed into a mass, aiming to correlate with the NDVI. After the statistical processes (already described in the previous topics), he obtained an average mass of 0.690 +/- 0.18 kg/m 2 , with 95% certainty that the average mass will oscillate between 0.690 and 0.708 kg/m 2 .
• Another advantage of the method of the invention is that the calibration of a date can be used in subsequent dates/images, as long as the interpolation interval is respected.
• the NDVI range was from 0.771 to 0.913 (Table 6), while for 12/31/2019 (Table 8), the calibration formula also comprises the mass of the area.
• the picket 1 , 2 and 6 probably due to the consumption of forage for livestock.
• the average grass growth per unit area can also be calculated. It should be noted that these statements are determined without field effort.
• the user first registers (1 ) and must register the rural property (2) and the areas of interest (pasture area or paddocks), also informing the municipality and state. This stage is critical and essential, as the cattle rancher or manager will know the limits.
• the first insertion is from the boundary of the property and is done through a polygon created in Google Earth, drawn on the platform itself or according to public data from the Rural Environmental Registry (CAR).
• CAR Rural Environmental Registry
• Figure 14 shows details of the property register (2), where identification fields (10) are presented.
• Figure 15 exemplifies the choice of property using data from the Rural Environmental Registry (CAR).
• CAR Rural Environmental Registry
• Figure 16 shows, according to the platform, an example of delimitation of areas of interest.
• these data are stored in the system. Now it can be used in the field, together with the mobile application, but before this step, the user will analyze if the recent image, up to 5 (five) days, does not have contamination by cloud or cloud shadow ( Figure 17). In this way, it requests the RGB image to analyze whether the image is usable. If yes, fieldwork is recommended and the NDVI is sent to the system (application) ( Figure 18).
• Figure 19 shows the screen of the mobile application referring to the first calibration (4). This relates the mass with the height, and the application screen has input and output number fields of the ascending plate, number of repetitions. After measuring the height, the grass is cut inside the frame, as explained and the value of the grass mass is inserted. It is suggested that this step be performed more than 3 (three) times, and that the sampled locations represent low, medium and high grass.
• the height collection is performed, noting that the user must sample the locations with representative colors, from red to green, as shown in Figure 18. It is reiterated that the sample must be collected per point with 30 subsamples on the imaginary circle, method already explained.
• the application screen for this step is similar to the previous one, but the height is automatically transformed into mass (kg/m 2 ). The user informs the number of entry, exit and repetitions of the ascending plate, the average height is transformed into biomass that is related to the NDVI value by georeferencing (latitude x longitude), using the cell phone's GPS.
• Figure 20 shows, according to the application, the volume of grass per paddock. It should be clarified that, eventually, the method of the invention will bring a map and informative table to the most refined final production, with a qualitative color map, field information, and comparison with the status of the last field. The same data is sent to the platform (figure 21), where the user subsequently enters the dry matter content and leaf percentage, reiterating that it is with the dry green leaf that the cattle are stocked.

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Citations (4)

• 2020
  • 2020-10-27 BR BR102020022030-6A patent/BR102020022030B1/pt not_active IP Right Cessation
• 2021
  • 2021-10-27 WO PCT/BR2021/050470 patent/WO2022087702A1/pt active Application Filing

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