Highlights The developed downforce test stand simulated varying disc loads based on actual field data. The planter’s downforce control system was able to maintain the target gauge wheel load 94% of the time. The planter’s downforce control system managed disc load variations of up to 667 N within 1.3 s. Abstract . In recent years, precision planters have incorporated automatic control of the row unit downforce to reduce sidewall soil compaction, maintain proper seeding depth, and control row unit ride quality. By applying an appropriate row unit downforce, more uniform emergence and increased yield can be obtained. However, little research exists on evaluating the response and accuracy of downforce control systems during planting. Therefore, the objectives of this study were to (1) develop a laboratory-scale row unit downforce test stand and (2) use the test stand to evaluate the downforce control system response time and the load distribution between the gauge wheels, opening discs, and closing wheels using simulation scenarios based on real-world soil and terrain data. The downforce test stand was able to distribute the applied downforce to the row unit gauge wheels, opening discs, and closing wheels. It was also capable of varying the row unit ride height. The simulation scenarios using the test stand showed that the downforce control system maintained the target gauge wheel load (GWL) of 379 N within ±223 N for more than 94% of the time during all simulations. The downforce control system was also able to manage the GWL within 1.3 s for disc load variations up to 667 N. Keywords: Automatic downforce control, Downforce test stand, Gauge wheel load, Simulation.

Abstract. Seed lubricants play a crucial role in proper seed singulation by ensuring a smooth flow of the seeds through the metering unit. However, the harmful chemicals inadvertently expelled along with the air during the seed metering process have raised concerns regarding the negative effects of available lubricant to the environment. An alternative has been developed from soy protein, however, no knowledge exists regarding its suitability as a potential seed lubricant. Therefore, the objectives of this study was (1) to assess seed flowability by quantifying seed singulation for both corn () and soybean () crops, and (2) to perform a simple cost analysis to determine the cost of usage of each seed lubricant. To address these objectives, two Horsch planter row units were used to run simulated planting scenarios in the laboratory. One row unit was equipped with a seed tube sensor to record seed tube seed count and time interval while the other one was fitted with an encoder to record seed meter motor rpm. These data were used to quantify percentage seed singulation, skips/misses, and multiples. Treatment factors were seed size with three levels: small, medium, large and seed lubricant with four levels: talc, fluency agent; soy protein based, and fourth being no lubricant. Row unit was programmed to plant seeds at 7.2 kph (5 mph) simulated ground speed with a target population of 89,000 seeds/ha for corn and 370,000 seeds/ha for soybean. Each test was replicated three times in a completely randomized design. For corn, result suggests that large seeds of different shapes showed greater singulation irrespective of type of seed lubricant. For soybeans, results suggest that both seed lubricant and seed size could potentially affect seed flowability. Medium size soybeans exhibited greater singulation for all four levels of seed lubricants while Fluency Agent and soy protein resulted in greater singulation along with fewer skips and multiples. A cost analysis shows that usage cost of soy protein as seed lubricant is 63% less expensive as compared to Fluency Agent although slightly expensive by 44% compared to talc. In summary, soy protein could be an alternative seed lubricant for row crop planters in providing equivalent or better seed flowability, cost-effectiveness and environmental stewardship. Keywords: Seed flowability, Row crop planter, Singulation, Seed lubricant, Soy protein.

Highlights. Seed meter rpm error decreased with increasing planter speed during steady states.Point-row operations could result in up to 10-13 seeds being over- or under-planted.Seed meter rpm error varied from -7.2% to 7.9% during curve planting transient states. Abstract. Electric drive seed metering systems have become a common method for singulating row crop seed. These singulation systems have substantially fewer moving parts and can potentially respond more quickly than other drive mechanisms. However, the accuracy and response time of these systems has yet to be examined to quantify potential benefits of adoption. The objectives of this study were (1) to quantify accuracy and response time of electric meter drives to varying ground speeds and speed transitions during in-lab simulation planting operations on straight-line and curves, and (2) to compare actual seed meter motor speed to target meter speed during simulation field scenarios. To quantify metering system performance, test scenarios were developed to simulate planting on headlands, within field boundaries including traversing in-field obstacles, and planting on curves with different radii. Ground speeds during simulation scenarios were 7.2, 12.9, and 16.1 kph when operating on straight rows and 6.0, 8, 11, and 14.5 kph when planting along curvilinear paths. Test scenarios also included planter acceleration and deceleration at 0.4 and 0.6 m/s2 when traversing in-field obstacles and tighter radii curves. Tests were conducted with two different seeding rates, 44,460 and 88,920 seeds/ha. Eight high frequency encoders were mounted on the electric meters of selected row units to record real-time meter rpm and quantify seed meter accuracy and response time. A custom DAQ system was developed to read simulation test scenario data files in ASCII text file format and send prescribed ground speed commands to the Horsch Maestro 24.30 planter’s ECU at 10 Hz using a program written in LabVIEW. Results indicated that seed metering accuracy increased as ground speed increased resulting in a significantly lower seed meter rpm error at 16.1 kph under steady-state conditions. During transient states, seed meters needed 3 to 4 s to respond during deceleration and acceleration resulting to seed meter rpm error ranging from -3.7% to 3.6% at 44,460 seeds/ha seeding rate and from -3.8% to 3.2% at 88,920 seeds/ha seeding rate. During point-row operations, the response time of the meters was 0.4 s which could result in up to 10 seeds being under-planted and up to 13 seeds being over-planted per row unit. During curvilinear planting, seed meter rpm error for steady states ranged from -0.5% to 0.8% across varying turn radii resulting to seeding rate error ranging from -223 to 370 seeds/ha while during transient states seed meter rpm error varied from -7.2% to 7.9% resulting to seeding rate error ranging from -5,886 to 7,187 seeds/ha. Keywords: Seed meter rpm, Seeding rate error, Simulation, Variable rate planting, Planter meter.

Chemical application is an integral part of crop care. Today, advanced sprayers automatically control individual boom sections and nozzles to accommodate increased machine sizes and travel speeds, yet automatic control of flow-based systems raises concerns regarding coverage accuracy and uniformity during changes in travel speed and spray swath width. New commercial systems apply product at a constant pressure using varied duty cycles of pulse width modulated (PWM) solenoids to maintain a constant application rate. However, concerns exist regarding the dynamic effect of solenoid on/off latency on spray fan pattern and spray coverage. The objectives of this study were to investigate the on/off latency in PWM nozzles, determine if active nozzles affect spray fan pattern latency, and develop flow characteristics to simulate dynamic spray coverage. A PWM system and flow rate controller were installed on a 6.6 m three-section boom sprayer with 13 nozzles. A Raven Viper 4 controller regulated the product flow rate and pressure, while a Capstan Pinpoint controller was used to set the system pressure, nozzle on/off configuration, and duty cycle. The results indicated that the PWM spray system maintained the pressure within +/- 5% of the target value and applied an accurate amount of flow per pulse regardless of the number of nozzles activated. There was a 20 ms delay in nozzle pressure development during each cycle, and the delay was constant regardless of the number of nozzles activated. After de-energizing the solenoid, the nozzle continued spraying at system pressure for 10 ms. Static spray droplet distribution proved that the system applied the correct volume per pulse. In addition, PWM duty cycles of 100%, 80%, 60%, and 40% provided spray coverage within. +/- 10% of the target rate for 100%, 94%, 77%, and 67% of the time, respectively. Greater signal overlap between odd and even nozzles increased the application coverage. Dynamic spray simulations showed that as-applied application error may vary beyond. +/- 10% of the target rate. As such, while the PWM system provided the desired amount of product per pulse, the spray coverage results indicated that the on-ground coverage could result in areas with under-or over-application.

In recent years, newer precision planters have seen integration of automatically controlled row unit downforce systems to reduce soil compaction, maintain proper seeding depth and control row unit ride quality. By keeping planter row unit downforce properly applied, a more uniform emergence and increased yield potential can be obtained. However, little knowledge exists to understand downforce system response and accuracy during scenarios typically occuring during field operation. Therefore, the study was conducted with two key objectives to study were to 1) develop a lab-based test stand to evaluate downforce system response time, accuracy, and downforce load distribution between the gage wheels, opening discs, and closing wheels; and 2) evaluate an automatic downforce system using test stand under simulated real-field scenarios. A downforce system test stand was designed with the capabilities of changing row unit vertical travel as well as load distributions between the planter row unit's gage wheels, opening discs, and closing wheels. The test stand was developed after assessing field operation of a planter with automatic downforce system operated on multiple fields. Simulation scenario were developed to 1) operate planter at varying speeds with uniform soil type and moisture; and 2) operate on soil with varying resistance due to texture and/or moisture changes. Real-time field data was used to develop simulation test files with target disc loading conditions. A custom LabVIEW program was developed to operate the downforce test stand's pneumatic valves and record data from the test stand and row unit sensors using a National Instrurrients (NI) CRio Chassis. The LabVIEW program could also read control commands from *.txt file, henceforth referred to as a simulation file, to actuate desired disc loads through the disc loading mechanism and platform height through the platform height control mechanism. The program would read the simulation file containing target values of disc load and platform height, parse the data fields and send it corresponding control loops. The control loops would used the simulation file data as a target load or distance height setting while reading pressure transducer and ultrasonic sensor data for current load of the disc cylinder and current height of the test stand, respectively. A National Instruments cRIO Chassis and C series modules were used to control the test stand and record data from the test stand at 10 Hz. Results from different scenarios exhibited that the test planter's automatic downforce control system maintained the target gauge wheel load setting of 38 kgf +/-- 22.7 kgf for more than 94% of the time. The downforce control system was able to manage gauge wheel load with disc load variations up to 68 kgf within 1.3 sec and load variation upto 22 kgf within 0.5 sec. Overall, the planter downforce control system ability to maintain target gauge wheel loads at varying speeds and soil texture variation within 0.5 to 1.3 s suggested appropriate control for precision planter.

Electric drive seed metering systems have become common for planting row-crop seed to accommodate increased machine size and planting speeds and to allow individual row unit-control that enable site-specific planting for spatially sensitive areas and contour farming. Seed singulation (a measurement of singulated seeds, misses, and multiples) is critical requirement when adopting high speed planting. However, current planting controllers fail to indicate whether singulation errors occurred due to operator-based behaviors such as speed changes, headland operation, point rows and contour farming at varying speed transitions (accelerations/decelerations). Therefore this study was conducted to understand a seed metering system’s ability to singulate seed under typical scenarios with specific objectives to (1) quantify electric seed metering accuracy using high-speed imaging and (2) identify machine operating states that impact seeding accuracy. A Horsch Maestro 24.30 planter was sent commands to plant at constant speeds of 7.2, 9.7, 12.0 kph while accelerating/decelerating at 2.4 and 4.8 kph/s from/to a stop and between speeds. The planter was sent commands to plant around contours at varying radii (20, 40, 80, 150 m) at varying speeds (i.e., 0, 2.4, 4.8, 6.4, 7.2, 9.7, 12.1, 12.9, 14.5, 16.1 kph). Simulations were conducted at two rates (44,550 and 89,110 seeds ha−1). A high-speed imaging system was developed using LabVIEW to record real-time seed meter singulation at 300 frames/s by combining planting machine states with seed tube sensor data and vision based seed measurements to quantify single count seeds, misses, and multiples. When planting from 2.4 kph to 16.1 kph, results showed an average singulation of 98.45% where errors nearly doubled with fast accelerations and decelerations and abrupt changes such as a shift during headland turns. Overall, planting above 1250 seeds per minute resulted in an increased number of singulation errors. The vision based measurements were within 0.8 ± 0.2% of the commercial seed tube sensors. The seed per minute value which provided optimal seed singulation can be used as a control parameter by technology users and manufactures to select optimal operating parameters to achieve target singulation rates. The methodology provided optimal machine conditions and operator behaviors to achieve a target percent singulation by identifying scenarios which increase singulation by minimizing misses and multiples.

Chemical application is an integral part of crop care replacing mechanical operations. Applications usually occur multiple times to address crop stress from weed competition, fungus, nutrient deficiency, and neighboring crop. Today, advanced sprayers automatically control individual sections and nozzles to accommodate increased machine size and travel speeds. Yet, automatic control raise concerns for flow based systems regarding coverage accuracy and uniformity during speed changes. New commercial systems apply product at constant pressure using varied duty cycles of Pulse Width Modulated (PWM) solenoids to maintain a constant application rate. However, concerns exist in terms of dynamic spray coverage. This study combined a full-system audit and high speed imagery to study the dynamic effect of solenoid On/Off latency on spray fan pattern and product distribution. Study objectives were to investigate the On/Off time latency in PWM nozzles, determine if active nozzles affect spray fan pattern latency, and develop flow characteristics to simulate dynamic spray coverage. . A PWM system along with rate controller was installed on a 21.5 ft boom-section sprayer with 13 nozzles. A Raven Viper 4 system regulates sprayer system pressure and flow while the Capstan Pinpoint™ controller controls the system pressure and active nozzle configuration while adjusting the duty cycle. Results indicate the pressure latency remained constant regardless of active nozzles. On/off-time latency was found to be 20 ms to reach system pressure after energizing and de-energizing a nozzle solenoid. However, after de-energizing a nozzle solenoid, the nozzle continued spraying at system pressure for 10 ms. Static spray fan pattern deposit prove the system applies the correct rate per pulse; however, on/off-time latency create varying coverage as quantified in the dynamic simulation. As found with high-speed images, deposit time was consistently found to be 65 ms from when the nozzle solenoid is energized with an average droplet speed of 467 in/s. Fast travel speed result in a higher duty cycle and longer on-time with increased coverage efficiency. To conclude, PWM spray systems apply an accurate amount of flow per pulse regardless of active nozzles with uniform coverage occurring from higher duty cycles.