METHODS AND SYSTEMS FOR USING THE DUTY CYCLE OF SENSORS TO DETERMINE THE FLOW RATE OF SEEDS OR PARTICLES

Abstract

An electronic system comprising a display device for displaying data and a logic processing unit coupled to the display device. The logic processing unit is configured to determine the duty cycle of at least one sensor for sensing the flow of a product or particles through a product or particle line of an agricultural implement and to determine the quantity of product or particles flowing through a line of the agricultural implement based on the duty cycle of the at least one sensor.

Description

METHODS AND SYSTEMS FOR USING THE WORK CYCLE OF SENSORS FOR DETERMINING THE FLOW RATE OF SEEDS OR PARTICLES TECHNICAL FIELD [1] Embodiments of the present disclosure relate to methods and systems for using the duty cycle of sensors to determine the flow rate of seeds or particles. BACKGROUND [2] Pneumatic seeders have a primary distribution system and a secondary distribution system. Seeds and, optionally, fertilizer, are fed from hoppers into the primary distribution system and are airlifted to the secondary distribution system. A manifold between the primary distribution system and the secondary distribution system divides the feed so that the secondary distribution system delivers seed / fertilizer to each row. The seeds / fertilizer are airlifted. [3] Typically, seed or fertilizer sensors on farm equipment have been optical sensors. When a particle (of fertilizer or a seed) passes through the optical sensor, it interrupts a light beam and a particle is detected. If the frequency is low enough, the frequency of these particle detections can be used to determine planting populations. However, for higher flow crops like wheat or for fertilizer, the typical 25 mm or 32 mm sizes of optical sensors do not have a large enough cross-sectional area to detect individual particles, thus making the 984410 of 37 particle counts from these sensors are unreliable and inaccurate. BRIEF DESCRIPTION OF THE FIGURES [4] This disclosure is illustrated by way of example, and not as a limitation, by the attached figures in which: [5] Figure 1 illustrates a prior art pneumatic seeder. [6] Figure 2 illustrates a tower of a pneumatic seeder having a vent valve and an actuator for the valve according to one embodiment. [7] Figure 3 illustrates a secondary product line having flow sensors according to one embodiment. [8] Figure 4A schematically illustrates an embodiment of an electrical control system for controlling an actuator. [9] Figure 4B schematically illustrates an embodiment of an electrical control system for controlling an actuator.

[10] Figure 5 illustrates a secondary product line having an ultrasonic sensor according to one embodiment.

[11] Figure 6 illustrates a secondary product line having an ultrasonic sensor according to another embodiment.

[12] Figure 7 illustrates a secondary product line 122 containing at least one valve (e.g., 750-1, 750-2) and at least one corresponding actuator (e.g., 724-1, 724-2) according to one embodiment.

[13] Figure 8 illustrates a flow diagram of one embodiment of a method 800 for using the duty cycle to determine particle and measurement parameters of a population. 984410 of 37

[14] Figure 9 illustrates a flow diagram of one embodiment of a method 900 for using duty cycle to estimate particle frequency measurement parameters.

[15] Figure 10 illustrates a flow diagram of one embodiment of a method 1000 for using duty cycle to estimate particle frequency measurement parameters.

[16] Figure 11 illustrates a monitor or display device having a user interface 1101 with customized agricultural use options including seed distribution according to one embodiment.

[17] Figure 12 illustrates a monitor or display device having a user interface 1201 with customized agricultural use options including information about the towers of an agricultural implement according to one embodiment.

[18] Figure 13 illustrates a monitor or display device having a user interface 1301 with customized agricultural use options including information about the turret 4 of an agricultural implement according to one embodiment.

[19] Figure 14 illustrates a monitor or display device having a user interface 1401 with customized agricultural use options including a Smart Connector and seed uniformity information for an agricultural implement according to one embodiment.

[20] Figure 15 illustrates a monitor or display device having a user interface 1501 with options for agricultural use. 984410 of 37 customized ones including seed uniformity, according to one embodiment.

[21] Figure 16 illustrates a graph of estimated frequency versus duty cycle, according to one embodiment.

[22] Figure 17 shows an example of a system 1200 including a machine 1202 (e.g., a tractor, combine, etc.) and an implement 1240 (e.g., a planter, sidebar for treatment, cultivator, plow, sprayer, spreader, irrigation implement, etc.) according to one embodiment. BRIEF SUMMARY

[23] In one embodiment, an electronic system comprises a display device for displaying data and a logic processing unit coupled to the display device. The logic processing unit is configured to determine a duty cycle of at least one sensor for detecting flow of a product or particulate through a product or particulate line of an agricultural implement and to determine an amount of product or particulate flowing through a line of the agricultural implement based on the duty cycle of the at least one sensor. DETAILED DESCRIPTION

[24] All references cited herein are incorporated herein by reference in their entirety. However, in the event of a conflict between a definition in this disclosure and one in a cited reference, the definition in this disclosure shall control.

[25] Figure 1 illustrates a typical pneumatic seeder 100. The 984410 of 37 pneumatic seeder 100 includes a cart 110 and a chassis 120. Cart 110 has a hopper 111 and a hopper 112 for storing seed and fertilizer, respectively. A main product line 116 is connected to an air blower (fan) 113 for conveying seed and fertilizer from meter 114 and meter 115, respectively. Main product line 116 supplies seed and fertilizer to collector tower 123. The seed and fertilizer are distributed by collector tower 123 to secondary product lines 122 leading to openers 121.

[26] Although the following description refers to the control of the collector tower 123 of a section of a pneumatic seeder 100, the same system can be applied to each section.

[27] Figure 2 illustrates collector tower 123. In collector tower 123 is a primary product line 116 that provides seed and optionally fertilizer in an air stream. Seed / fertilizer impact screen 125 has a mesh size that prevents the passage of seed and / or fertilizer. The seed / fertilizer falls through outlets 124 (or outlet ports) and is fed to secondary product lines 122. Above screen 125 is a tower 126 containing a valve 127. Valve 127 may be any type of actuatable valve. In one embodiment, valve 127 is a butterfly valve. The valve 127 is actuated by the actuator 128, which is arranged in the tower 126. The actuator 128 is in signal contact with the electrical control system 300. Optionally, there is a cover 130 attached to the tower 126 so that it can rotate, to cover the tower 126 when no air flows.When air flows, the lid 130 is lifted by the force of the air flowing through the tower 126, and. 984410 of 37 when air does not flow, the lid 130 closes the tower 126.

[28] In one embodiment, illustrated in Figure 2, the header tower 123 further includes a pressure sensor 140 disposed in the header tower 123. In another embodiment, the pressure sensor 140 is disposed in at least one secondary product line 122. The pressure sensor 140 is in signal communication with the electrical control system 300. This may provide closed loop feedback control of the valve 127. In another embodiment, the electrical control system 300 measures the pressure at the pressure sensor 140 located in the header tower 123 and the pressure sensor 140 in the secondary product line 122 and calculates a difference between the pressure sensors. The electrical control system 300 may perform control based on the pressure difference.

[29] In another embodiment, illustrated in Figure 3, there is a first particle sensor 150-1 and a second particle sensor 150-2 arranged in series within at least one secondary product line 122. The first particle sensor 150-1 and the second particle sensor 150-2 may be arranged individually or as parts within a unit. The first particle sensor 150-1 and the second particle sensor 150-2 are separated by a distance such that a waveform measured at the first particle sensor 150-1 will be duplicated at the second particle sensor 150-2. As seeds travel through a pneumatic seeder, they will not flow at an even distribution throughout. In a given cross section, there may be one, two, three, four, five, or more seeds together. As the seeds travel through a certain 984410 of 37 distance, the distribution of seeds in each group may expand or condense. Over a short distance, the grouping will still be uniform. Each grouping of seeds will generate a different waveform on a particle sensor. The waveforms from a plurality of groupings will create a pattern on the first particle sensor 150-1. When this pattern is detected on the second particle sensor 150-2, the time difference between each of these measurements is divided by the distance between the first particle sensor 150-1 and the second particle sensor 150-2 to determine the velocity of the seeds / fertilizer traveling through the secondary product line 122. Using the velocity, the electronic control system 300 can actuate the actuator 128 to change the amount of air exiting the tower 126 to change the velocity of the seeds / fertilizer traveling through the secondary product line 122.

[30] An example of a particle sensor is the Wavevision Sensor of Precision Planting LLC, and described in U.S. Patent No. 6,208,255. The first particle sensor 150-1 and the second particle sensor 150-2 are in signal communication with the electrical control system 300. This may provide closed loop feedback control of the valve 127.

[31] Although both the pressure sensor 140 and the particle sensor 150-1, 150-2 are illustrated, for closed loop feedback control only one need be used.

[32] In another embodiment illustrated in Figure 2, there may be at least one valve (e.g., valve 160) disposed at each outlet 124 (or outlet port) and actuated by actuator 161, which is in 984410 of 37 signal communication with the electrical control system 300. Each actuator 161 (or actuators) may be individually controlled to further regulate flow with at least one valve in each secondary product line 122. Each secondary product line 122 may contain at least one valve (e.g., 750-1, 750-2) and corresponding actuator (e.g., 724-1, 724-2) as illustrated in Figure 7. This may provide fine tuning control in each secondary product line 122 separate from other secondary product lines 122. In each secondary product line 122, the pressure sensor 140, an ultrasonic velocity sensor, or the particle sensor 150-1, 150-2 may perform the measurement necessary to control each actuator 122.In one embodiment, the particle sensor 150-1, 150-2 may be any sensor that has a signal output with a duration proportional to the time during which the sensor is blocked by particle(s) passing by the sensor.

[33] The electrical control system 300 is schematically illustrated in Figure 4A in accordance with one embodiment. In the electrical control system 300, the monitor 310 is in signal communication with the actuator 128, the actuator 161, the pressure sensor 140, the fluid velocity sensor 170, and the particle sensor 150-1, 150-2. It should be appreciated that the monitor 310 comprises an electrical controller. The monitor 310 includes a logic processing unit 316 (e.g., a central processing unit (CPU) 316), a memory 314, and optionally a graphical user interface (GUI) 312, which allows a user to view and enter data into the monitor 310. The monitor 310 may be of the type described in U.S. Patent No. 8,386,137. For example, monitor 310 may be a 984410 of 37 planter monitoring system that includes a visual display and a user interface, preferably a touchscreen graphical user interface (GUI). The touchscreen GUI is preferably housed within a housing that also houses a microprocessor, memory, and other hardware and software that can be used to receive, store, process, communicate, display, and perform various features and functions. The planter monitoring system preferably cooperates and / or interacts with various external devices and sensors.

[34] An alternative electrical control system 350 is illustrated in Figure 4B, including a module 320. The module 320 receives signals from the pressure sensor 140, the fluid velocity sensor 171, and the particle sensors 150-1, 150-2, which may be provided to the display 310 for display as output on the GUI 312. The module 320 may also provide control signals to the actuator 128 and the actuator 161, which may be based on operator input on the display 310.

[35] During closed loop feedback control operation, the monitor 310 receives a signal from the pressure sensor, fluid velocity sensor, and / or particle sensor 150-1, 150-2. The monitor 310 uses the pressure signal, fluid velocity signal, and / or particle signal to set a selected position of the actuator 128 which controls the valve 127 to regulate the amount of air exiting the tower 126. The monitor 310 sends a signal to the actuator 128 to effect this change. This in turn controls the amount of air flowing through the secondary product lines 122 to transport seed / fertilizer into the furrow at the appropriate force and / or velocity to place the seed / fertilizer in the furrow. 984410 of 37 furrow without the seeds / fertilizers bouncing out of the furrow.

[36] In one example, module 320 is located on an implement or a tractor. Module 320 receives sensor data from sensors located on an implement. The module processes the sensor data to perform operations of the methods set forth herein, or the module sends the sensor data to a logic processing unit to perform operations of the methods set forth herein.

[37] In addition to measuring pressure or particle velocity, the velocity of the fluid (air) can be measured. An ultrasonic velocity sensor can measure fluid velocity.

[38] Figure 5 illustrates an ultrasonic sensor for detecting flow through a product line or pipe according to one embodiment. The ultrasonic sensor 500 is positioned in a line or pipe 522 (e.g., a secondary product line) or very close to the line or pipe 522. The sensor (or ultrasonic flow meter) uses acoustic waves or vibrations of a certain frequency (e.g., greater than 20 kHz, about 0.5 MHz). The sensor 500 uses submerged or non-submerged transducers at the perimeter of the line or pipe to couple ultrasonic energy to the fluid flowing through the line or pipe. In one example, the sensor operates using the Doppler effect, where a transducer 504 having a transmitter transmits a beam 530.A transmitted frequency of beam 530 undergoes a linear shift upon reflection from particles and bubbles present in a fluid within line 522 to generate a Doppler shifted reflection 540 which is received by a receiver of a transducer 502. The frequency shift between the frequency of beam 530 and the frequency of the. 984410 of 37 reflection 540 may be directly related to the flow rate of a fluid (e.g., liquid, or air) having a flow direction 510. The frequency shift is linearly proportional to the flow rate of the materials through the line or pipe and may be used to generate an analog or digital signal that is proportional to the flow rate of the fluid.

[39] With a known inside diameter (D) of a 522 line or pipe, the volumetric flow rate (e.g., in gallons per minute) is equal to K * Vf * D2. In this example, Vf is the flow rate and K is a constant dependent on the units of Vf and D.

[40] Figure 6 illustrates an ultrasonic sensor (e.g., a transit time flow meter) for detecting flow through a product line or pipe according to one embodiment. Transit time flow meters (e.g., time of flight flow meters, travel time flow meter) measure a difference in travel time between pulses transmitted by a single path along and against a fluid flow (e.g., a liquid, or air). The sensor 600 has a housing 650 with transducers 602 and 604. The sensor 600 is positioned in a line or pipe 622 (e.g., a secondary product line) or in close proximity to the line 622 or pipe 622.

[41] In an example as illustrated in Figure 6, the sensor operates with transducers 602 and 604. Each transducer having a transmitter and a receiver alternately transmits and receives bursts of ultrasonic energy as beams 630 and 640 at an angle theta (e.g., about 45 degrees). The difference between the upstream transit time and the downstream transit time (Tu - Td) measured can be used. 984410 of 37 on the same path to calculate the flow through the line or pipe:

[42] V = K * D / sin 2theta * 1 / (T0 - tau)2delta T

[43] V is the mean velocity of flowing fluid, K is a constant, D is a diameter of the line or pipe, theta is an angle of incidence of ultrasonic boom waves, T0 is a zero flow transit time, delta T is T2 - T1, T1 is the transit time of boom waves (beam 630) from transducer 602 to transducer 604, T2 is the transit time of boom waves (beam 640) from transducer 604 to transducer 602, and tau is the transmission time of boom waves through the line 622 or pipe. Flow velocity is directly proportional to a different measure between upstream and downstream transit times. Volumetric flow rate is determined by multiplying the cross-sectional area of ​​the line or pipe by the flow velocity. Volumetric flow can be determined with an optional 690 microprocessor-based converter or the 300 or 350 electrical control system.The fluid having a 610 flow path must be a reasonable conductor of sonic energy.

[44] As discussed above, seed or fertilizer sensors on farm equipment have typically been optical sensors. When a particle (seed or fertilizer) passes through the optical sensor, a beam of light is broken and a particle is then detected. If low enough, the frequency of these particle detections can be used to determine planting populations. However, for higher flow crops like wheat or for fertilizers, optical sensors of typical sizes (25 mm or 32 mm) do not have a large enough cross-sectional area to detect individual particles, so 984410 of 37 The particle counts from these sensors are unreliable and inaccurate. For this reason, optical sensors used on implements that experience these higher frequencies (such as pneumatic seeders) are called blocking sensors because these sensors can only report whether they see particles or not.

[45] The blocking sensors used on pneumatic seeders do not report enough seed pulses to correctly report seeds / acre. Pneumatic seeders use a seed distribution value that displays unitless population. This can be a problem when the seeding rate is too high and the voltage drop across the sensor does not occur as frequently, resulting in lower population reporting.

[46] However, for a given sensor and particle type (e.g., corn, wheat, sorghum, barley, oats, canola, fertilizer, etc.), a relationship between the time a particle is detected by the optical sensor and the actual particle frequency can be measured (as described below). If this relationship can be derived for certain particles, the measured duty cycle of the optical sensor can be used to calculate an estimated particle frequency, which can then be used to calculate an estimated population based on other known variables, such as row unit speed, or row spacing.

[47] The duty cycle of an optical flow sensor can be used to calculate an estimated product or particle frequency, which can then be used to calculate an estimated population based on other known variables such as the speed of the row unit or the spacing between the particles. 984410 of 37 rows.

[48] ​​By knowing the duty cycle of the sensor, other mathematical calculations can be made to generate useful measurement parameters such as Relative Frequency that a user (e.g., operator, farmer) can use to compare the number of particles going towards each row of the implement and identify mechanical problems that cause variation between rows.

[49] Figure 8 illustrates a flowchart of one embodiment of a method 800 for using duty cycle to determine particle and measurement parameters of a population. The method 800 is performed by a logic processing unit that may comprise hardware (circuitry, dedicated logic unit, etc.), software (e.g., executed on a general-purpose computer system or on a dedicated machine or device), or a combination of both. In one embodiment, the method 800 is performed by a logic processing unit (e.g., logic processing unit 316) of an electronic control system (e.g., electronic control system 300, electronic control system 350, a machine, apparatus, monitor 310 with CPU 316, a module 320, display device, user device, self-guided device, self-propelled device, etc.).The electronic control system or processing system (e.g., processing system 1220, 1262) executes instructions from a software application or program on a logic processing unit. The software application or program may be initiated by the electronic control system or processing system. In one example, a monitor or display device. 984410 of 37 receives user input and provides a customized display for Method 800 operations.

[50] In operation 802, a software application is initiated in an electronic control system or a processing system and displayed on a monitor or display device, for example, in a user interface. The electronic control system or processing system may be integrated or coupled to a machine that performs an application pass (e.g., planting, tilling, fertilizing). Alternatively, the processing system may be integrated with an apparatus (e.g., a drone, an image capture device) associated with the machine, which captures images during the application pass.

[51] In operation 804, the method determines a duty cycle of at least one sensor (e.g., optical sensors, sensors 140, 150-1, 150-2, 171, transducers 502, 504, 602, 604) to sense the flow of a product or particles through a product or particle line of an agricultural implement. This line supplies the product or particles to an agricultural field.

[52] In operation 806, the method measures an amount of product or particles flowing through a line of the agricultural implement based on the duty cycle of the at least one sensor. In operation 808, the method maps the duty cycle of the at least one sensor at specific positions determined by GPS to generate a spatial map of an agricultural field.

[53] In operation 810, a monitor or display device displays on a user interface a value showing the average duty cycle and the highest and lowest value for the at least one sensor of the 984410 of 37 implement. In operation 812, the monitor or display device displays on a user interface a value showing a range (e.g., maximum duty cycle - minimum duty cycle) of the duty cycle for the at least one sensor of the implement.

[54] Figure 9 illustrates a flowchart of one embodiment of a method 900 for using duty cycle to estimate particle frequency measurement parameters. The method 900 is performed by a logic processing unit that may comprise hardware (circuitry, dedicated logic unit, etc.), software (e.g., executed on a general-purpose computer system or a dedicated machine or device), or a combination of both. In one embodiment, the method 900 is performed by a logic processing unit (e.g., logic processing unit 316) of an electronic control system (e.g., electronic control system 300, electronic control system 350, machine, apparatus, monitor 310 with CPU 316, module 320, display device, user device, self-guided device, self-propelled device, etc.).The electronic control system or processing system (e.g., processing system 1220, 1262) executes instructions from a software application or program on a logic processing unit. The software application or program may be initiated by the electronic control system. In one example, a monitor or display device receives user input and provides a customized screen for the operations of method 900.

[55] In operation 902, a software application is started and displayed as a user interface on a monitor or display device. 984410 of 37 display. The electronic control system or processing system can be integrated or coupled to a machine that performs an application pass (e.g., seeding, tilling, fertilizing). Alternatively, the electronic control system or processing system can be integrated into a device (e.g., a drone or image capture device) associated with the machine, which captures images during the application pass.

[56] In operation 904, the method determines a duty cycle of at least one sensor (e.g., optical sensors, sensors 140, 150-1, 150-2, 171, transducers 502, 504, 602, 604) to sense the flow of seeds or particles through a seed or particle line of an agricultural implement. This line supplies the seeds or particles to an agricultural field.

[57] In operation 906, the method determines the relationship between the duty cycle and a given seed or particle type and the seed or particle size to estimate the number of seeds or particles passing through the sensor's optical path per second. This estimated value of seeds or particles per second is called the Estimated Frequency (Hz). In one example, for lower duty cycles (e.g., range 0-25%, 0-60%), a linear equation relates the Estimated Frequency to the duty cycle. The linear equation is as follows.

[58] Y = m * x + b, where Y = estimated frequency for the seeds or particles, m = constant between 1 and 10, x = duty cycle of at least one sensor, b = 0.

[59] In another example, for larger duty cycles (e.g., 25-100%, range 60-100%), an exponential equation relates the estimated frequency to the duty cycle. The exponential equation is as follows. 984410 of 37

[60] Y = a * eA(bx), where Y = estimated frequency for the seeds or particles, a = constant between 5 and 100, x = duty cycle of at least one sensor, b = constant between 0.01 and 10.

[61] Figure 16 illustrates a graph of estimated frequency versus duty cycle according to one embodiment. At lower duty cycles (0-50% or 0-25% duty cycle) a linear equation 1610 is used and at higher duty cycles (50-100% duty cycles) an exponential equation 1612 is used to determine the estimated frequency based on the duty cycle. The change from the linear equation 1610 to the exponential equation 1612 does not necessarily occur at a specific duty cycle. The transition may occur over a range between 0 and 100% duty cycle. For example, a lower duty cycle could be 0-25% with a higher duty cycle being greater than 25% to 100%, in one instance. In another embodiment, a non-linear equation may be used over the entire 0-100% duty cycle range.

[62] Returning to Figure 9, in operation 908, the method maps the estimated frequency (Hz) for the seeds or particles of a given sensor or row at specific positions determined by GPS to generate a spatial map of an agricultural field.

[63] In operation 910, the method determines the relative frequency based on a calculation of the estimated frequency for a given row divided by the average frequency for all sensors of a farm implement and then compares the relative frequency between rows (in a group of sensors or for all sensors at once) on a farm implement to determine which rows have higher or lower velocities to estimate the relative frequency. 984410 of 37 number of seeds or particles that pass through the sensor's optical path per second.

[64] In operation 912, the method maps the relative frequency of a specific sensor or row at specific positions determined by GPS to generate a spatial map for a given field.

[65] In step 914, the method uses a standard deviation of relative frequency as a performance measure to quantify the uniformity of particles from an implement.

[66] Figure 10 illustrates a flowchart of one embodiment of a method 1000 for using duty cycle to estimate particle frequency measurement parameters. The method 1000 is performed by a logic processing unit that may comprise hardware (circuitry, dedicated logic unit, etc.), software (e.g., executed on a general-purpose computer system or a dedicated machine or device), or a combination of both. In one embodiment, the method 800 is performed by a logic processing unit (e.g., logic processing unit 316) of an electronic control system (e.g., electronic control system 300, electronic control system 350, a machine, apparatus, monitor 310 with CPU 316, module 320, display device, user device, self-guided device, self-propelled device, etc.).The electronic control system or processing system (e.g., processing system 1220, 1262) executes instructions from a software application or program in the logic processing unit. The software application or program may be initiated by the electronic control system or processing system. 984410 of 37 In one example, a monitor or display device receives user input and provides a customized display for the operations of method 1000.

[67] In operation 1002, a software application is initiated on an electronic control system or a processing system and displayed on a monitor or display device as a user interface. The processing system or electronic control system may be integrated or coupled to a machine performing an application pass (e.g., planting, tilling, fertilizing). Alternatively, the processing system or electronic control system may be integrated with an apparatus (e.g., a drone, or an image capture device) associated with the machine, which captures images during the application pass.

[68] In step 1004, the method determines a ratio between the estimated frequency and the actual frequency in a laboratory, and then uses this ratio to calibrate the estimated frequency (Hz) value to an estimated flow rate (e.g., as seeds / sec). In step 1006, the method converts the estimated flow rate to an estimated population (e.g., seeds / acre or mass / acre (mass of each seed multiplied by the number of seeds)) or other unit area by knowing the row spacing and the speed of the row unit of a farm implement. In step 1008, the method maps the flow modification of a given sensor to a row at specific positions determined by GPS to generate a spatial map of a farm field. In step 1010, the method displays the spatial map of the flow estimate for the farm field on a monitor or display device. In step 1012, the method 984410 of 37 displays on a monitor or display device one or more of: seed distribution, seed uniformity, sensor output, flow estimate, seed total, and seed average in combination with one or more implement data including: down force data, soil analysis implement data (e.g., soil moisture data, organic matter data, soil temperature data), and furrow closure data.

[69] Figure 11 illustrates a monitor or display device having a user interface 1101 with customized agricultural use options including seed distribution according to one embodiment. An initiated software application (e.g., a field application) of an electronic control system or processing system generates the user interface 1101 displayed on the monitor or display device.

[70] The software application may provide different display regions that the user may select. In one example, the display regions include a standard option 1102, a measurement parameters option 1104, and a Large Map option 1106 for controlling the size of a map of a region of the field that is displayed. Additionally, in one example, the display regions include a seed uniformity region having selectable option 1110, a low row region 1111, and a high row region 1112. The seed uniformity region displays a figure calculated from the Dashboard MiniChart (DMC) standard deviation values ​​of the DMC region 1150. The low row region 1111 displays the lowest estimated seed or particle frequency for 984410 of 37 a row unit divided by (estimated average seed or particle frequency * 100 for all row units) of an agricultural implement. The high row region 1112 shows the highest estimated seed or particle frequency for a row unit divided by (estimated average seed or particle frequency * 100 for all row units) of an agricultural implement.

[71] The DMC region 1150 includes normalized values ​​(e.g., 120, 100, 80) for the estimated seed or particle frequency for a row unit / (average of the estimated frequency * 100 for all row units). In one example, a mean value of 100 is set.

[72] Figure 12 illustrates a monitor or display device having a user interface 1201 with customized agricultural use options including information about the turrets of an agricultural implement according to one embodiment. An initiated software application (e.g., a field application) of a processing system generates the user interface 1201 displayed on the monitor or display device.

[73] The software application may provide different display regions that the user can select. In one example, selecting the seed distribution option 1110 from the user interface 1101 generates the user interface 1201 having tower information. The tower information includes for each tower selectable tower options having region DMC values ​​1250 and the estimated average seed or particle frequency of the rows in a tower / (average of the estimated frequency of all rows * 100 for a given tower). 984410 of 37 tower).

[74] The DMC region 1250 includes normalized values ​​(e.g., 120, 100, 80) for the estimated seed or particle frequency for a row unit / (average of the estimated frequency * 100 for all row units). In one example, a mean value of 100 is set.

[75] Upon selecting a tower (e.g., tower 4), a user interface 1301 is generated as illustrated in Figure 13. The user interface 1301 includes the same information about tower 4 as is illustrated in user interface 1201. The DMC region 1350 includes normalized values ​​(e.g., 120, 100, 80) for the estimated seed or particle frequency for a row unit / (average estimated frequency * 100) for row units of tower 4. In one example, a mean value of 100 is set.

[76] Figure 14 illustrates a monitor or display device having a user interface 1401 with customized agricultural use options including a seed data processing module, such as a Precision Planting LLC SmartConnector, and seed uniformity information for an agricultural implement according to one embodiment. An initiated software application (e.g., a field application) of an electronic control system or processing system generates the user interface 1401 displayed on the monitor or display device.

[77] The software application may provide different display regions that the user may select. In one example, the user interface display region includes sensor information for 984410 of 37 sensing seeds or particles passing through a seed or particle line of an agricultural implement, seed uniformity 1410, and seed total 1420. Seed uniformity is calculated based on the estimated seed or particle frequency of a row unit (e.g., 1-11) / (average of the estimated seed or particle frequencies for all row units). Seed total indicates the total number of seeds detected by a sensor in unit time for a row unit.

[78] Figure 15 illustrates a monitor or display device having a user interface 1501 with customized agricultural use options, which, according to one embodiment, include seed uniformity. An initiated software application (e.g., a field application) of an electronic control system or a processing system generates the user interface 1501 displayed on the monitor or display device.

[79] The software application may provide different display regions that the user may select. In one example, the display regions include a standard option 1502, a measurement parameters option 1504, and a Large Map option 1506 for controlling the size of a map of a field region that is displayed. Additionally, in one example, the display regions include a seed uniformity region having a selectable option 1510, a low row region 1511, and a high row region 1512. The seed uniformity region 1510 displays a standard deviation of the seed population for all rows (e.g., estimated seed or particle frequency for a 984410 of 37 row unit divided by (estimated average seed or particle frequency for all rows * Constant). The Dashboard MiniChart (DMC) region 1550 displays a flow estimate of 180,000, where half of the values ​​are greater than 180,000 and half of the values ​​are less than 180,000. The low row region 1511 displays the lowest seed uniformity of a row unit among all row units (e.g., estimated seed or particle frequency of a row unit divided by (estimated average seed or particle frequency * 100 for all row units)) of a farm implement.The high row region 1512 shows the highest seed uniformity among all row units (e.g., estimated seed or particle frequency of a row unit divided by (estimated average seed or particle frequency * 100 for all row units)) of an agricultural implement. A sensor output region 1520 shows an average duty cycle for the row unit sensors, the low sensor output 1521 having the lowest sensor duty cycle, and the high sensor output 1522 having the highest sensor duty cycle. In one example, a sensor has a first voltage level and a second voltage level. The duty cycle is calculated based on the percentage of time the sensor operates at the first voltage level (e.g., less than 1 volt).The sensor switches from a first voltage level to a second voltage level or vice versa, based on the detection of a seed or particle passing through the sensor's optical path.

[80] The flow estimate region 1530 shows an estimated seed population with the lowest flow estimate 1531 and the population 984410 of 37 highest estimated 1532. The duty cycles of the seed or particle sensors are used to calculate an estimated frequency and then the estimated frequency is used to calculate a flow number.

[81] In one embodiment, an estimated particle frequency may be calculated for the sensor based on one or more properties selected from the measured duty cycle, particle type, particle size, and particle shape. In another embodiment, the estimated particle frequency may be estimated based on an empirically determined lookup table or a tuned equation for the relationship between frequency and duty cycle. In another embodiment, the estimated particle frequency may be estimated based on a single calibration constant determined by a calibration procedure or a calibrated flow setpoint. In another embodiment, the control system may automatically learn over time a calibration curve of the estimated particle frequency as a function of duty cycle.As more data is collected, the calibration curve can be adjusted accordingly. In another embodiment, the relationship between duty cycle and estimated particle frequency can initially be a fixed relationship based on nominal empirical data and particle properties, and then changed to a corrected relationship based on measured (or automatically learned) data.

[82] The monitor or display device may also display any of the parameters or measurements set forth herein (e.g., seed distribution, seed uniformity, sensor output, flow estimate, total seeds, average seeds, population) in 984410 of 37 combination with one or more implement data including: downforce data, soil analysis implement data (e.g. soil moisture data, organic matter data, soil temperature data), and furrow closure data.

[83] Examples of measurement parameters include High Row, Low Row, and average measurement parameters (for any value), population (including ordered rate of population increase and actual rate of population increase), singulation, skips, multiples, smooth ride (good handling), good separation, downforce, ground contact, speed, and vacuum. Figures 5 and 6 of U.S. Patent No. 8,078,367, U.S. Patent No. 9,955,625, and U.S. Patent 6,070,539, which are incorporated herein by reference, provide examples of some of these same measurement parameters. U.S. Patent No. 8,078,367 and U.S. Patent No. 9,955,625 are incorporated herein by reference.

[84] Examples of soil testing implement data can be found in WO2019070617A1, which is incorporated herein by reference. Figures 20, 22, 45, 48, 50, 51, 52, 71, and 72 provide examples of soil testing implement data (e.g., soil testing apparatus data) including organic matter, soil moisture, temperature, depth, soil components, goodness of separation, seed germination moisture, voids, moisture uniformity, moisture variability, emergence environment score, seed environment score, and seed environment score properties. An example of an implement is Precision's SmartFirmer sensor. 984410 of 37 Planting LLC.

[85] Examples of closure information can be found in international patent WO2017197274, PCT / US2018 / 061388, filed on November 15, 2018, and international patent PCT / US2019 / 020452, filed on March 2, 2019, which are incorporated herein by reference.

[86] Figure 17 shows an example of a system 1200 including a machine 1202 (e.g., a tractor, combine, etc.) and an implement 1240 (e.g., a planter, sidebar for treatment, cultivator, plow, sprayer, spreader, irrigation implement, etc.) according to one embodiment. The machine 1202 includes a processing system 1220, a memory 1205, the machine network 1210 (e.g., a serial bus network with controller area network (CAN) protocol, an ISOBUS network, etc.), and a network interface 1215 for communicating with other systems or devices, including the implement 1240. The machine network 1210 includes controllers 1212 (e.g., speed sensors, optical sensors), controllers 1211 (e.g., GPS receiver, radar unit) for controlling and monitoring operations of the machine or implement.The network interface 1215 may include at least one of a GPS transceiver, a WLAN (e.g., WiFi) transceiver, an infrared transceiver, a Bluetooth transceiver, Ethernet, or other communication interfaces with other devices and systems, including the implement 1240. The network interface 1215 may be integrated into the machine network 1210 or may be separate from the machine network 1210 as illustrated in Figure 12. The I / O ports 1229 (e.g., the diagnostic / on-board diagnostics (OBD) port) allow communication with another data processing system or device (e.g., a computer or a computer system). 984410 of 37 (for example, display devices, sensors, etc.).

[87] In one example, the machine operates a tractor that is coupled to an implement for planting applications and seed or particle detection during an application. Planting data and seed / particle data for each row unit of the implement may be associated with location data at the time of application to gain a better understanding of planting and seed / particle characteristics for each row and region of a field. Data associated with planting applications and seed / particle characteristics may be displayed on at least one of display devices 1225 and 1230. The display device may be integrated with other components (e.g., with processing system 1220, memory 1205, etc.) to form display 300.

[88] The processing system 1220 may include one or more microprocessors, processors, a system on a chip (integrated circuit), or one or more microcontrollers. The processing system includes a logic processing unit 1226 for executing software instructions of one or more programs and a communications unit 1228 (e.g., a transmitter, a transceiver) for transmitting and receiving communications from the machine via the machine network 1210 or a network interface 1215 or from the implement via the implement network 1250 or network interface 1260. The communications unit 1228 may be integrated into the processing system or may be separate from the processing system. In one embodiment, the communications unit 1228 is in data communication with the machine network 1210 and the implement network 1250 via 984410 of 37 of a diagnostic / OBD port of I / O Ports 1229.

[89] Logic processing unit 1226 which includes one or more processors or processing units, may process communications received from communications unit 1228 and include agricultural data (e.g., GPS data, planting application data, soil characteristics, any data sensed by sensors of implement 1240 and machine 1202, etc.). System 1200 includes memory 1205 for storing data and programs (software 1206) for execution by the processing system. Memory 1205 may store, for example, software components such as planting application software or seed / particle software for analyzing seed / particle and planting applications for performing the operations of the present disclosure, or any other software applications or modules, images (e.g., captured images of crops, seeds, soil, furrow, soil clumps, row units, etc.), alerts, maps, etc. The memory 1205 may be any known form of non-transitory storage medium that is machine-readable, for example, semiconductor memories (e.g., flash; SRAM; DRAM; etc.) or non-volatile memories, such as hard drives or solid-state drives. The system may also include an audio input / output subsystem (not shown) that may include a microphone and speaker, for example, for receiving and sending voice commands or for user authentication or authorization (e.g., by biometrics).

[90] The processing system 1220 communicates bi-directionally with the memory 1205, the machine network 1210, the interface 984410 of 37 network 1215, header 1280, display device 1230, display device 1225 and I / O Ports 1229 via communication links 1231 -1236, respectively. The processing system 1220 may be integrated with memory 1205 or may be separate from memory 1205.

[91] Display devices 1225 and 1230 may provide a visual user interface for a user or operator. The display devices may include display controllers. In one embodiment, display device 1225 is a handheld tablet device or a computing device with a touch screen that displays data (e.g., planting application data, captured images, a local view map layer, high definition field maps with various measured seed / particle data, as-performed planting or harvest data or other agricultural variables or parameters, yield maps, alerts, etc.) and data generated by an agricultural data analysis software application and receives input from the user or operator to display an exploded view of a region of a field, monitoring and controlling field operations.Operations may include machine or implement configuration, data reporting, machine or implement control, including sensors and controllers, and storage of generated data. The display device 1230 may be a display (e.g., a display provided by an original equipment manufacturer (OEM)) that displays images and data for a local view map layer, measured seed / particle data, fluid application data as applied, or seeding data. 984410 of 37 as performed or data of the harvest as performed, yield data, seed germination data, seed environment data, control of a machine (e.g. a seeder, tractor, harvester, sprayer, etc.), machine handling and monitoring of the machine or of an implement (e.g. a seeder, harvester, sprayer, etc.) that is connected to the machine by sensors and controllers located on the machine or implement.

[92] A cab control module 1270 may include an additional control module for enabling or disabling certain components or devices of the machine or implement. For example, if the user or operator cannot control the machine or implement using one or more of the display devices, then the cab control module may include switches for turning off or disconnecting components or devices of the machine or implement.

[93] The implement 1240 (e.g., a planter, cultivator, plow, sprayer, spreader, irrigation implement, etc.) includes an implement network 1250, a processing system 1262, a network interface 1260, and optional input / output ports 1266 for communicating with other systems or devices, including the machine 1202. The implement network 1250 (e.g., a serial bus network with Controller Area Network (CAN) protocol, an ISOBUS network, etc.) includes a pump 1256 for pumping fluid from one or more storage tanks 1290 to the application units 1280, 1281... N of the implement, controllers 1252 (e.g., radar, electrical conductivity, electromagnetic, a force probe, speed sensors, seed / particle sensor for detecting the 984410 of 37 seed / particle passage, sensors for detecting characteristics of soil or a furrow including a plurality of soil layers differing in density, a depth of a transition from a first soil layer to a second soil layer based on the density of each layer, a magnitude of the difference between the densities of the soil layers, an index of variation in soil density with depth, variability in soil density, roughness of a soil surface, thickness of a residue layer, density of a soil layer, soil temperature, presence of seeds, spacing between seeds, percentage of tamped seeds, and presence of residue in the soil, at least one optical sensor for detecting at least one of: soil organic matter, soil moisture, soil texture, and cation exchange capacity (CEC) of the soil,downforce sensors, valve actuators, moisture sensors, or flow sensors for a combine harvester, speed sensors for the machine, seed applied force sensors for a planter, fluid application sensors for a sprayer, or vacuum, lift force, or downforce sensors for an implement, flow sensors, etc.), controllers 1254 (e.g., a GPS receiver), and processing system 1262 for controlling and monitoring the operations of the implement. The pump controls and monitors the application of the fluid to the crops or soil as applied by the implement. The fluid application may be performed at any stage of crop development, including within a planting furrow at seed planting, adjacent to a planting furrow in a separate furrow, or in a region near the planting region (e.g., between rows of corn or 984410 of 37 soybeans) where there are seeds or where a crop grows.

[94] For example, the controllers may include processors in communication with a plurality of seed sensors. The processors are configured to process data (e.g., fluid application data, seed sensor data, soil data, furrow or trench data) and transmit the processed data to processing system 1262 or 1220. The controllers and sensors may be used to monitor motors and drive units in a planter that includes a variable speed drive system to change plant population density. The controllers and sensors may also provide strip control to disconnect individual rows or sections of the planter. The sensors and controllers may detect changes in an electric motor that controls each row of a planter individually.These sensors and controllers can detect the speed at which seeds are delivered into a seed tube for each row of a planter.

[95] The network interface 1260 may be a GPS transceiver, a WLAN (e.g., WiFi) transceiver, an infrared transceiver, a Bluetooth transceiver, Ethernet, or other communications interfaces to other devices and systems, including the machine 1202. The network interface 1260 may be integrated into the implement network 1250 or may be separate from the implement network 1250 as illustrated in Figure 24.

[96] The processing system 1262 communicates bi-directionally with the implement network 1250, the network interface 1260, and the I / O Ports 1266 via the communication links 1241-1243, respectively. 984410 of 37

[97] The implement communicates with the machine via two-way wired and possibly also wireless communications 1204. The implement network 1250 may communicate directly with the machine network 1210 or via network interfaces 1215 and 1260. The implement may also be physically coupled to the machine to perform agricultural operations (e.g., seed / particle detection, planting, harvesting, spraying, etc.).

[98] Memory 1205 may be a non-transitory, machine-accessible medium upon which one or more sets of instructions (e.g., software 1206) are stored that incorporate one or more of the methodologies or functions described herein. Software 1206 may also reside, completely or at least partially, within memory 1205 and / or within processing system 1220 during execution thereof by system 1200, the memory and processing system also constituting machine-accessible storage media. Additionally, software 1206 may be transmitted or received over a network, via network interface 1215.

[99] In one embodiment, a non-transitory machine-accessible medium (e.g., memory 1205) contains executable computer program instructions that when executed by a data processing system cause the system to perform the operations or methods of the present disclosure. Although an exemplary embodiment is shown where the non-transitory machine-accessible medium (e.g., memory 1205) is a single medium, it should be understood that the term non-transitory machine-accessible medium is intended to include a 984410 of 37 single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store one or more sets of instructions. The term "non-transitory machine-accessible medium" should also be considered to include any medium that is capable of storing, encoding, or transporting a set of instructions to be executed by the machine and that causes the machine to perform one or more of the methodologies of the present disclosure. Therefore, the term "non-transitory machine-accessible medium" will be considered to include, but is not limited to, solid-state memories, optical and magnetic media, and carrier wave signals. 984410 of 37 20215863485 PALAZZI- 20215863485 Digitally signed by PORTALTRAM ITES - INPI Date: 2020.06.17 13:46:13 -03:00 Reason: Digitally signed by the INPI Location: Buenos Aires, Argentina 984410

Claims

1. An electronic system for estimating the flow of seeds or particles in an agricultural implement comprising: a display device for visualizing data; at least one sensor configured to switch from a first voltage level to a second voltage level based on the detection of a seed or particle passing through an optical path of the at least one sensor; and processing logic coupled or integrated with the display device, characterized in that the processing logic, which includes one or more processors, is configured to determine a duty cycle of the at least one sensor for detecting the flow of a seed or particle through each seed or particle line of the agricultural implement, and to determine an estimated population quantity of seeds or particles flowing through each seed or particle line of the agricultural implement based on the duty cycle measured from the at least one sensor;and wherein the display device is configured to display data including the duty cycle measured by each sensor or the estimated population of seeds or particles flowing through each seed or particle line of the agricultural implement, and to identify mechanical problems or generate one or more control signals to adjust the flow of one or more seed or particle lines of the agricultural implement based on a degree of variation in the duty cycle or variation in the estimated population of seeds or particles between each seed or particle line of the agricultural implement, the processing logic being configured to calculate the duty cycle of at least one sensor based on a percentage of time during which the at least one sensor operates at the first voltage level. 26 Claims follow;