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Automation continues to play a greater role in agricultural production with commercial systems now available for machine vision identification of weeds and other pests, autonomous weed control, and robotic harvesters for fruits and vegetables. The growing availability of autonomous machines in agriculture indicates that there are opportunities to increase automation in cotton production. This article considers how current and future advances in automation has, could, or will impact cotton production practices. The results are organized to follow the cotton production process from land preparation to planting to within season management through harvesting and ginning. For each step, current and potential opportunities to automate processes are discussed. Specific examples include advances in automated weed control and progress made in the use of robotic systems for cotton harvesting.

Uniform plant stand, herein understood as an outcome of plant spacing and seeding-depth uniformity, requires proper selection of downforce control across varying field conditions, especially when planting at faster ground speeds. Thus, objectives of this study were (1) to assess the effect of ground speed and downforce settings on plant spacing and seeding depth, and (2) evaluate the relationship of planting speed and row-unit vibration. A 12-row planter was used to plant corn on no-till and strip-tilled fields. Treatment factors were downforce setting with two levels: 620 and 980 N, and ground speed with four levels: 7.2, 9.7, 12.1, and 16.1 km h−1. The planter was programmed to plant corn at 51 mm seeding depth at a seeding rate of 84,000 seeds ha−1, equivalent to a theoretical plant spacing of 178 mm. No significant downforce effect was observed at either field on plant spacing, although the low downforce setting resulted in lower plant spacing variability. Higher variability in spacing was observed with increasing ground speed. Target seeding depth on the no-till field was achieved with high downforce and slower ground speed, while deeper than the target seeding depth on the strip-tilled field was observed with a high downforce setting. Finally, both downforce settings revealed increasing row-unit acceleration as ground speed increased.

HighlightsNozzle pressure drop varies between PWM systems at different application rates and application pressures.Change in flow rate with respect to the expected flow differs between PWM systems at different rates and pressures.There was a latency before the system reached the target application pressure.PWM systems operate for less time than the specified duty cycle, which may cause application errors.Abstract. Three PWM nozzle control systems, Capstan PinPoint II, John Deere ExactApply, and Raven Hawkeye, referred to as systems S1, S2, and S3, respectively, were used in this study. Data on nozzle pressure, boom pressure, flow rate, and response time were recorded with different duty cycles (25%, 50%, 75%, and 100%) and operating frequencies (10, 15, and 30 Hz) for two application rates (112.2 and 187.1 L ha-1) and two application pressures (275.8 and 448.2 kPa) at 1 kHz using a LabVIEW program and a cRIO data acquisition system. Results indicated that the PWM systems perform differently when operating at different application rates, pressures, duty cycles, and frequencies. Each PWM system provided a different pressure drop at the nozzle during operation. The increase in application rate and pressure increased the pressure drop. The percent change in flow rate with respect to the expected flow was also significantly different between the PWM systems, which could be due to the differences in pressure provided at the nozzle during operation. The PWM systems also showed latency before reaching the target application pressure during operation and operated for less time than the specified duty cycle at stable target pressure while also continuing to spray even after the solenoid valves had closed. The application pressure during peak and fall times and the time of stable application pressure within a cycle should be given careful consideration when selecting a PWM system, as they can contribute to application errors. Producers should also consider the pressure drop with the selected PWM system and target application rate to set up the system to apply at the desired pressure. Manufacturers mostly recommend operating PWM systems at 10 Hz. For the purpose of this study, the operating frequency of the PWM systems was set to 10 and 15 Hz for S1, to 15 and 30 Hz for S2, and to 10, 15, and 30 Hz for S3. Producers should expect differences in pressure drop, stabilized pressure application time, and flow rate if they choose to operate at a higher frequency. The results of this study are only applicable to the types of nozzle bodies and nozzle tips used. The data will differ based on the dual-orifice valve coefficient equation: the larger the second orifice, the greater the pressure drop. This will affect the final orifice pressure, as well as the flow rate. This study did not address the impact of flow resistance caused by differences in the design of nozzle bodies and nozzle types. Keywords: Nozzle flow rate, Pressure drop, Pulse width modulation control modules, Response time.

HighlightsThe 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.

Soybean (Glycine max L. Merril) seed costs have steadily increased over the past decades and triggered a growing interest in reducing seeding rates, while improving profitability via seed savings and better yields. However, little is known on the implications of early-season spatial plant-to-plant uniformity on soybean yield under the current reduced seeding rates and modern varieties. Therefore, the objectives of this study were to i) explore traditionally used and new metrics to characterize early-season spatial plant-to-plant uniformity, ii) select and identify the best metrics relating the spatial plant-to-plant uniformity and soybean yield, and iii) evaluate the effect of stand uniformity under varying yielding environments and seeding rates. Soybean trials evaluating different planters (Max Emerge® and Exact Emerge®) and seeding rates (160, 215, 270 and 321 thousand seeds ha-1) were conducted in rainfed and irrigated conditions during 2019 and 2020 in the US and Argentina. Early-season plant-to-plant spacing was recorded in all trials and multiple stand uniformity metrics were calculated. Spacing standard deviation (sd) and the sd of the achieved versus target evenness index (ATEI = observed spacing/theoretical plant spacing) showed the greatest correlation with soybean yield (R2 = 0.26 and 0.32, respectively). However, only ATEI sd exhibited a more consistent relationship with soybean yield across environments. Non-uniform stands, described by ATEI sd, negatively impacted soybean yields under low and medium yield environments (mean of 2.7 and 3.0 Mg ha-1, respectively), while high yielding environments (mean of 4.3 Mg ha-1) were not sensitive to stand uniformity. The effect of unevenness spacing was not affected by the tested seeding rates. Our study provides new insights on the ability of soybeans to compensate yield at low yield environments and suggests uniformity of spacing between plants as a target to maximize yields regardless of the seeding rate.

Current operator feedback from in-field pesticide application operations conveys limited information and often does not allow the operator to visualize a true representation of their performance. Farm management information systems (FMIS) typically do not account for overlap, varying application rates across the width of the spray boom during turns, or off-rate errors due to controller response. The pesticide application coverage training (PACT) tool was developed to deploy data analytics methodologies to sprayer operational data collected during field applications. The goal was to compare enhanced feedback via the PACT tool versus data generated from commercially available FMIS software today. Data were collected for multiple Nebraska fields and processed by the PACT program which consisted of a novel MATLAB program developed for this project. The PACT program successfully generated high-resolution as-applied maps and the automated application report further quantified the contributions of these errors to the total error to illustrate how overlap, turning errors or controller response issues may have individually affected application accuracy. PACT program output metrics were compared with current data provided by FMIS software. Field-average metrics were not found to be significantly different when comparing the PACT program to the FMIS output; however, when examining how in-field errors were distributed amongst various application rate ranges, significant differences were noted in comparison to the FMIS output. Thus, the PACT program was able to quantify and illustrate application rate variation due to boom section overlap and turning movements unaccounted for in traditional FMIS software.

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.

Proper seed placement during planting is critical for achieving uniform emergence, which optimizes the crop for maximum yield potential. While uniform plant spacing and seeding depth are often used by corn growers to determine the performance of precision planters, these parameters can be influenced by factors other than machinery, such as seed germination, insects, diseases, and soil properties (e.g., temperature and moisture). Currently, the ideal way to determine planter performance is to manually measure plant spacing and seeding depth. However, this process is both cost- and labor-intensive and prone to human errors. Therefore, this study aimed to develop and test a proof-of-concept sensing and measurement (SAM) system to measure seed spacing and seeding depth and provide the geo-location of each planted seed. The system consisted of a high-speed camera, light section sensor, potentiometer, and survey-grade real-time kinematic (RTK) global positioning system (GPS) unit. Results demonstrated the potential of the proof-of-concept SAM system for measuring seed spacing, seeding depth, and geo-location of corn seeds. Results showed that seed spacing can be calculated using the generated stitched images, achieving a root mean squared error (RMSE) of 0.63 cm and a coefficient of determination (R2) of 0.87 when compared to actual seed spacing measurements. Likewise, 98% of the recorded seeding depths were within the acceptable tolerance of ±10% error. Finally, GPS coordinates were recorded for individual images, which can be used to locate individual seeds and provide detailed information on missing plants (no seeds).HighlightsCalculated seed spacing showed an RMSE of 0.63 cm and an R2 of 0.87.98% of the recorded seeding depth was within the acceptable error of ±10%.GPS coordinates were recorded for individual images. Keywords: High-speed camera, Image mosaic, Light section sensor, Proof of concept, Seeding depth, Seed spacing.

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.

Permanent or solid set canopy delivery (SSCD) system with certain types of emitters placed along the tree rows have shown promise in the past for foliar application in tree fruit orchards. However, limited knowledge exists in terms of designing a SSCD system for uniform canopy spray coverage in tree fruit orchards. This study examined the spray coverage in high-density, super spindle apple orchards created by different nozzles and their configuration within a canopy on a SSCD system. Three different types of emitters (sprinkler, full cone, and hollow cone) and two different design configurations were evaluated in the orchard using three consecutive tree fruit rows that were 9 m long. Application pressures were 241, 379, and 517 kPa. Coverage analysis on the upper- and under-side of leaves was determined using water sensitive cards (WSC) on three randomly selected trees (23= cards/tree). The results indicated that the design configuration with two 80° hollow-cone nozzles, installed at three different canopy heights, and spray application pressure of 379 kPa provided greater coverage on upper-side (87%) and on the under-side (50%) of tree leaves. Additional studies would have to be conducted for other fruit crops and training systems to optimize system design for intended spray coverage. Keywords: Application pressure, Emitter, Solid set canopy delivery, Spray nozzle, Spray coverage.

Pulse width modulation (PWM) technology manages the flowrate by varying the duty cycle. However, application errors might still occur while the system manages to maintain the target pressure resulting in pesticide resistance and product loss. Field tests were performed to assess the pressure and droplet size uniformity of the PWM technology. Boom pressure and duty cycle data from selected nozzles across the sprayer boom were recorded using a custom data acquisition system when applying product at a rate of 140.3 l L ha-1 at 275.8 kPa application pressure (field 1) and 112.2 L ha-1 application rate at 413.7 kPa application pressure ( field 2). Results showed that the pressure was within ±10% of the target for 84.4% on field 1 and 89.8% on field 2 when using a PWM system. Pressure coefficient of variations were less than 10.0% for 98.0% of the time in both fields. In a flow-based system, the pressure was below ±10.0% of the target for the majority of the time in both fields. The droplet size varies from coarse (73.0%) to medium (27.0%) for field 1 while medium (65.9%) to fine (34.1%) on field 2 when using a PWM system. In a flow-based system, the droplet size was at the coarse category for field 1, while it varies from coarse (24.0%), medium (60.8%) to fine (1.6%) for field 2. The PWM technology may provide an application pressure within the acceptable range thereby providing a better droplet size and application uniformity than a flow-based system.

The use of drones has become widespread in agriculture, and it is associated with unique opportunities and challenges. The most common role of drones in agriculture is as a remote sensing platform to assess and monitor crops, but emerging agricultural applications include precision distribution of agricultural chemicals and biological control agents, livestock health monitoring, and remote sampling. Several different drone designs and sensor types are used, each with its associated advantages and limitations. The use of sensors with wide angles of view at relatively low altitudes above ground level presents challenges that require unique data capture and data processing procedures to overcome. This chapter provides an overview of the development of agricultural drone use, approaches and methods used to ensure that data quality is adequate for agricultural applications, and a selection of traditional and emerging agricultural drone applications.

On-farm adoption of individual and groups of precision agriculture technologies has grown in the past 15 years. Based on a sample of 545 farm observations collected by the Kansas Farm Management Association, farm adoption of bundles of embodied knowledge and information intensive technologies was analyzed using a Markov transition approach. Three separate analyses estimated transition probabilities to show the adoption of bundles of embodied knowledge technologies, the adoption of bundles of information intensive technologies, and the adoption of variable rate technologies contingent on prior adoption of embodied knowledge and/or information intensive technologies. Each analysis was estimated for two separate time periods (2009–2012) and (2013–2016). The probability that farms retain the same bundle or transition to a different bundle by the next time period are reported. The results indicate that persistence with the same technology bundle is the predominant behavior and that this behavior has strengthened in the study’s most recent time period.

Planter downforce control allows row units to maintain a target gauge wheel load (GWL) across a range of soil resistance within a field. Downforce control is typically set for a target seed depth and can be implemented either as fixed or by automatic or active control to attain the desired GWL. Recent advances allow for the control of individual row units into sections for improved GWL application. However, little knowledge exists on the spatial variability of GWL, row-to-row GWL variability, and on the recommended GWL control requirements on planters operating in actual field conditions. Therefore, the objectives of this study were to (1) quantify real-time GWL variability across individual row units within a 12-row crop planter programmed to implement a constant downforce control during field operations; (2) evaluate gauge wheel load range (GWLR) across individual row units and within 2-row, 3-row, or 4-row control sections to determine the optimal downforce control section size; and (3) assess the impact of soil texture and soil compaction due to tire tracks on GWL variability. To address these objectives, a 12-row crop planter equipped with hydraulic downforce control was used to plant three fields. The planter was set to plant corn at 5.2 and 5.7 cm depths with fixed target GWL set at 334 ±223 N (111 to 557 N) and GWLR set at 0 to 883 N. A data acquisition system collected real-time GPS, planting speed, GWL, hydraulic pressure, and planter toolbar height data at 10 Hz. Real-time GWL data of individual row units were analyzed to determine the GWL distribution within or outside the set target GWL. Moreover, GWLR was measured in individual row units and across varying control section sizes. Soil electrical conductivity (EC) was measured using a Veris mobile sensor platform. Soil EC was used in defining zones of low, medium, and high textured soil. Results show that GWL was within the target range of 111 to 557 N at 33% of the total planting time across the three fields, and GWLR was within 0 to 441 N at 9% of the total planting time. Results also indicate that a 2-row, 3-row, and 4-row control section could provide GWLR within 0 to 441 N at 76%, 46%, and 28% of the total planting time, respectively. These findings suggest the need for automatic downforce systems with fewer row units per control section to maintain target GWL within an acceptable range for all row units. Regression analyses indicate that soil texture is a significant variable that can influence real-time GWL. Furthermore, compacted soil due to tractor tires contributed to significantly lower GWL. Our data suggest the need for active downforce control to achieve improved GWL uniformity under varying field-operating conditions. Keywords: Gauge wheel load, Planter downforce, Precision planters, Seeding depth, Variability.

Pulse width modulation (PWM) is currently implemented in agricultural sprayers to deliver the expected flowrate by managing the duty cycle and maintaining the target pressure and provide the desired droplet size. However, there are still issues about the application errors caused by the pressure variations when using this system as it may result in pesticide resistance and product loss. Field tests were conducted in a 30.0 ha (ha) and 54.0 ha fields to assess the pressure uniformity and the expected droplet size of the PWM technology. These parameters were also translated to an equivalent value if a flow-based system will be used during operation. Results showed that pressure mostly remained within the acceptable range in both fields. Pressure CV were within 10.0%, indicating the system's ability to provide acceptable pressure at varying conditions. However, nozzle pressure still varies, especially at the outer boom section where frequent duty cycle variation occurs. Pressure also deviates during acceleration and deceleration, indicating the system's latency to respond rapidly on changing conditions. In a flow-based system, the pressure varies due to varying speed as dictated by field terrain, curvilinear paths, and headland maneuvers. The droplet size also deviates from the expected droplet spectra based on the nozzle manufacturer’s specification when using a PWM system. However, the PWM system can still provide the target droplet size if a nozzle selected provides the desired droplet spectra in a wide range of pressure . In a flow-based system, the droplet size was expected to vary because of the fluctuation in application pressure due to speed changes during operation.

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. 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.

Precision agriculture (PA) has been commercially available for decades, however only specific technologies have been readily adopted. The overall goal of this study was to provide information of the historical changes (from 2000 to 2016), current status of PA utilization, and sales expectations in the next time period. Within this overarching objective, specific goals included 1) determining the specific technologies that farmers adopt and 2) estimating the probability of transitioning from one bundle of PA technologies to another. The three information-intensive technologies included: 1) yield monitor (YM) with or without GNSS 2) variable rate (VR) application of inputs, and 3) precision soil sampling (PSS). Combinations of these three technologies in addition to a possible “no technology adopted” response resulted in eight categories of PA technology bundles. Each year, farms were classified as having one of these eight possible bundles of PA technology. Adoption of PA technologies has increased over time, with the use of only YMs and the bundle of all three PA technologies (YM, PSS, and VR) as the two primary bundles being adopted. When only VR was adopted, there was a 47% probability that the farm would add a YM by next year. When a farm used YM, VR, and PSS, there was a 99% probability that a farm would continue using the bundle in the following year. The results are useful for farmers, extension professionals, and policymakers to understand prior adoption paths for bundles of PA technology. Future steps can connect this database on adoption of PA technology with farm meta-descriptors such as acreage, type of crop, rotation, other relevant management practices, and financial variables so to better understand how farmers are integrating technologies into their farming operations. Keywords: Adoption, Information-intensive, Markov chain, Precision agriculture, Sequential, Site specific, Soil sampling, Transition probability, Variable rate, Yield monitor.

Precision agriculture technologies have been adopted individually and in bundles. A sample of 348 Kansas Farm Management Association farm-level observations provides insight into technology adoption patterns of precision agriculture technologies. Estimated transition probabilities shed light on how adoption paths lead to bundling of technologies. Three information intensive technologies were assigned to one of eight possible bundles, and the sequence of adoption was examined using Markov transition processes. The probability that farms remain with the same bundle or transition to a different bundle by the next time period are reported. Farms with the complete bundle of all three technologies were likely to persist with their current technology.

Thermal image quality is critical to accurately quantify spatial and temporal growth and stress patterns of field crops. Image quality can be impacted by several factor including environment, flying altitude, and camera focal length. Often times the thermal sensor selection is based upon price or already owned sensor. Metrics are available to select the flight altitude based on thermal sensor for desired ground resolution, however no study have been conducted to provide relative difference in image quality and efficiency of generating a thermal orthomosaic. Therefore, this study was conducted with goal to compare accuracy of canopy temperature quantification and assess the quality of thermal orthomosaic when using thermal sensor of different focal length and image acquisition at varying flying altitudes of a sUAS. Three thermal infrared cameras were selected with focal lengths of 9mm, 13mm, and 19mm. All three cameras were flown at altitudes of 20m, 50m, and 80m, to collect aerial imagery of 7,000 m2 soybeans field. The cameras were mounted on a rotary quadcopter. All flights were conducted at 3 m/s flying speed, and 1 second shutter trigger interval. A ground reference system provided ground truth data for thermometric transformations. Imagery data was compared to assess differences in number of images collected, percentage overlap required for 1 second shutter trigger interval, quality of orthomosaic and accuracy of canopy temperatures. Results show that 13 mm focal length and 50 m altitude result in a finer resolution orthomosaic. The canopy temperatures were quantified accurately regardless of altitude and focal length.

Variable-rate application continues to gain interest among precision agriculture practitioners including the use of crop sensor technology for application of nitrogen in grain crops. For liquid fertilizers, variable-orifice nozzles are being implemented since they provide a much larger nozzle flow range compared to traditional fixed orifice nozzles. However, understanding the performance of variable-orifice nozzles under different field operating conditions has been limited. Therefore, the objective of this study was to evaluate the performance of variable orifice nozzles in support of variable-rate application. Two common variable-orifice nozzles offered by different companies were selected for this study. They were tested over three flow ranges (0.76 to 1.89 L/min) with all tests replicated three times. A commercially available 18.6-m, wet boom sprayer equipped with 37 nozzle bodies was used. Nozzles were numbered but then randomly assigned a position along the boom. To evaluate the performance of an individual nozzle, three random nozzle locations along the spray boom were established for both sets of nozzles. Therefore, 18 tests per replication were required to include the 3 flow rates, 3 different locations, and 2 nozzle types. Once the desired flow rate test was established, tip flow was measured using SpotOn Sprayer Calibrator technology. Tip flows were recorded and statistical analyses performed to evaluate flow uniformity (CV) across the boom but also detect off-rate errors by individual nozzles and locations across the boom. With the exception of a few nozzles, the uniformity across the spray boom, as defined by the CV, was acceptable for both nozzle types over approximately a 2:1 flow range. Both nozzle types were less uniform at the low flow rate. There were three nozzles of each type that resulted in unacceptable flow errors in multiple tests.

Citation: Frink, E., Flippo, D., & Sharda, A. (2016). Invisible leash: Object-following robot. Journal of Automation, Mobile Robotics and Intelligent Systems, 10(1), 3-7. doi:10.14313/JAMRIS_1-2016/1

Citation: Luck, J. D., Schaardt, W. A., Forney, S. H., & Sharda, A. (2016). Development and evaluation of an automated spray patternator using digital liquid level sensors. Applied Engineering in Agriculture, 32(1), 47-52. doi:10.13031/aea.32.11381

The greenhouse thermal imaging system had a ?0.62?C (α=0.05) measurement accuracy.Unstressed canopy temperatures followed closely to characteristic crop water use.Canopy temperature is a viable water stress indicator throughout growth stages.Spatial and temporal canopy temperature provide accurate crop health assessment.82% of soil moisture variation was explained by CWSI values above 0.6. Inadequate water application often decreases yield and grain quality. Existing methods using single, localized soil moisture or canopy temperature measurements do not account for crop water stress on both a high spatial and temporal resolution for precision irrigation water management decisions and scheduling. Therefore, this study was conducted to understand the feasibility of thermal cameras in order to quantify high resolution spatial canopy temperatures in relation to soil moisture. The objectives of this study were to deploy a thermal infrared imaging system (TIRIS) for high spatial and temporal monitoring of corn canopy temperature in greenhouse, test camera durability and measurement accuracy during full-season crop development, remove background temperatures with image segmentation, and sample individual plants to investigate full-season crop water stress versus soil moisture content. A TIRIS was developed using a lightweight uncooled thermal camera. Corn plants were divided into well-watered and water-stressed irrigation zones to observe stress from water deficits. Canopy temperatures were used to develop empirical canopy and air temperature deficit versus vapor pressure deficit linear regressions. Results showed that the TIRIS system maintained measurement accuracy of ?0.62?C (α=0.05) while compensating for changing ambient greenhouse conditions. Canopy and air temperature deficit versus vapor pressure deficit regression equations revealed that the predicted canopy temperature was closely related to characteristic water use. Results of the 80-day study demonstrated that 82% of soil moisture variation was explained by the crop water stress index (CWSI) values between 0.6 and 1.0. Results indicated that the CWSI derived by remotely measuring canopy temperature using TIRIS can be used as an alternate irrigation scheduling method in order to quantify spatial and temporal soil moisture variability.

Tillage practices have a significant effect on soil properties. Understanding the specific effects due to geometric and speed parameters of the tillage implement is the key to selecting a tillage practice that is best for a field. This is the first step of many towards optimizing an efficient tillage implement given initial field conditions and desired final conditions. Simple, small-scale tests were performed on idealized implement geometries as a proof-of-concept for future large-scale tests. The results of these tests are presented here and in-depth analysis will be presented in future work.

Sudden death syndrome (SDS), a fungal infection in soybean caused by Fusarium virguliforme, greatly affects the plant health and in some cases, can cause yield losses of more than 70%. Infected plants are scored by visual assessment based on severity and extent of infection. This manual process is time intensive and not practical for large acreages. Diseased and stress in plants show elevated canopy temperatures that can potentially lead to identification of unhealthy plants without manual scoring. The infection decreases nutrient distribution causing stress that results in internal plant temperature to increase. Thermal infrared (TIR) sensors have the ability to measure the emitted radiation of an object in the infrared region of the electromagnetic spectrum to estimate canopy temperatures. However, TIR sensors have not yet been utilized to capture changes in canopy temperatures to detect SDS in soybean. Therefore, the goal of this study was to (1) use a TIR sensor to assess plant health and vitality, and (2) evaluate canopy temperatures over the growing season to quantify disease development. A thermal infrared camera was mounted on a small unmanned aerial system to capture aerial imagery over the growing season. The first flight was achieved once SDS foliar symptoms began initial development. The remaining three flights occurred before, during, and after full pod fill when symptoms had reached their apex. Results show increasing correlations over the four days. Elevated canopy temperature changes were observed on canopies at early SDS symptom development. Symptoms at the end of the growing season displayed strong correlations to the canopy temperature with ρ = 0.7404. Disease severity showed the strongest correlation throughout the four flights with the last at ρ = 0.7245. The four flights exhibit a decreasing trend with Spearman's rho (R2 = 0.86 for disease severity). Therefore, thermal imaging can be utilized to detect diseased plots. Future studies will be conducted to understand how to mitigate for SDS using thermal detection.

Thermal image quality is critical to accurately quantify spatial and temporal growth and stress patterns of field crops. Image data quality from a thermal sensor can be impacted by several factors including environment, flying altitude, and camera focal length. Often times the thermal sensor selection is based upon price or ease of availability for research. Metrics are available to select the flight altitude based on thermal sensor focal length for desired ground resolution, however, no study has been conducted to provide the relative difference in image data, quality and efficiency of generating a thermal orthomosaic. Therefore, this study was conducted with the goal to compare the accuracy of canopy temperature quantification and assess the quality of thermal orthomosaic when using a thermal sensor of different focal lengths and image acquisition at varying flying altitudes of an sUAS. Three thermal infrared cameras were selected with focal lengths of 9 mm, 13 mm, and 19 mm. All three cameras were flown at altitudes of 20 m, 50 m, and 80 m, to collect aerial imagery of a 7,000 m2 soybean field. The cameras were mounted on a rotary quadcopter. All flights were conducted at 3 m/s flying speed and 1 s shutter trigger interval. A ground reference system comprising of a panel and water bath system with measured actual temperature provided ground truth data for thermometric transformations. Imagery data were compared to assess differences in the number of images collected, percentage overlap required for 1 s shutter trigger interval, quality of orthomosaic and accuracy of canopy temperatures. Results showed that 13 mm focal length and 50 m altitude resulted in a finer resolution orthomosaic which can provide robust and accurate information on canopy temperature. The selection of such a system of camera lens angle and altitude can provide accurate, reliable and rapid canopy temperature quantification.

Sudden Death Syndrome (SDS) has spread from the US to other countries which is causing yield loss of 10–15% and 70% in extreme cases from infected plants. Currently, SDS impacts are scored by visual assessment of infection severity and percent of crop diseased. The quality of manually collected row based coarse data collected over several hours can be impacted by assessment errors and changes in diurnal environmental conditions. Small unmanned aerial systems (sUAS) offer an alternative method to provide a more accurate and reliable measurement of crop disease. A platform designed to collect high throughput aerial imagery data to quantify SDS is proposed. A comparative evaluation of ground-based and aerial remote sensing methods for scoring of SDS is proposed to evaluate efficacy. The purpose of this research was to (1) compare accuracy and benefits of ground-based and aerial remote sensing methods for scoring of SDS, (2) determine if pigment index (PI) can be used for the assessment and quantification of SDS, and (3) assess if PI can be utilized for determination of maturity. A seven-acre field was selected as test plots to collect reflectance using both a ground-based spectrometer and sUAS aerial imagery using broadband modified color infrared sensor over a two-year period. Aerial imagery was collected once in 2016 (FD1) and twice in 2017 (FD2 and FD3) using a sUAS late in the growing season and at maturity each year. PI values were compared to manual collected ground-based data. Results from check plots indicated that the PI derived using aerial imagery and the ground-based spectrometer data explained 80 and 78% of the variation in SDS scores, respectively. When analyzing only high instances of SDS (SDS score >25) in the check plots aerial data and ground-based data explained 84 and 71% of the variation in SDS scores, respectively. Correlations between SDS scores and different indices analyzed showed that only PI and Blue Normalized Difference Vegetation Index (BNDVI) were significantly correlated with SDS score, with PI showing significantly greater correlations (−0.79 (FD2) and −0.72 (FD3)) to SDS on field scale than BNDVI (−0.36 (FD2) and −0.35 (FD3)). The PI derived from aerial imagery data showed strong correlations with SDS score, SDS severity and plant maturity, indicating that PI can be used in field studies to quantify critical growth indicators in soybean plots.