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1. Determination Of Crop Injury From Aerial Application Of Glyphosate Using Vegetation Indices And GeostatisticsInjury to crops caused by off-target drift of glyphosate can seriously reduce growth and yield, and is of great concern to farmers and aerial applicators. Determining an indirect method for assessing the levels and extent of crop injury could support management decisions. The objectives of this study were to evaluate multiple vegetation indices (VIs) as surrogate variables for glyphosate injury identification and to evaluate the combined use of Geostatistical methods and the VIs to assess... B. Ortiz, S.J. Thomson, Y. Huang, K. Reddy |
2. First Results Of Development Of A Smart Farm In The NetherlandsGNSS technology has been introduced on about 20 % of the Dutch arable farms in The Netherlands today. Use of sensor technology is also slowly but gradually being adopted by farmers, providing them large amounts of digital data on soil, crop and climate conditions. Typical data are spatial variation in soil organic matter, crop biomass, crop yield, and presence of pests and diseases. We still have to make major steps to use all this data in a way that agriculture becomes more sustainable. We... T. Feher, C. Kocks, C. Kempenaar, K. Westerdijk |
3. Detection Of Fruit In Canopy Night-Time Images: Two Case Studies With Apple And MangoReliable estimation of the expected yield remains a major challenge in orchards. In a recent work we reported the development of an algorithm for estimating the number of fruits in images of apple trees acquired in natural daylight conditions. In the present work we tested this approach with night-time images of similar apple trees and further adapted this approach to night-time images of mango trees. Working with the apple images required only... R. Linker, A. Payne, K. Walsh, O. Cohen |
4. Optimizing Site-Specific Adaptive Management Using A Probabilistic Framework: Evaluating Model Performance Using Historic DataAgricultural producers are tasked with managing crop yield responses to nitrogen (N) within systems that have high levels of spatial (biophysical), climatic, and price uncertainty. To date, the outcome of most variable rate application (VRA) research has focused on the spatial dimension, proposing optimal fertilizer prescription maps that can be applied year after year. However, temporally static prescriptions can result in suboptimal outcomes, particularly if they do... L.J. Rew, B.D. Maxwell, P.G. Lawrence |
5. Assessing Soybean Injury from Dicamba Using RGB and CIR Images Acquired on Small UAVsDicamba is an herbicide used for postemegence control of several broadleaf weeds in corn, grain sorghum, small grains, and non-cropland. Currently, dicamba-tolerant (DT) soybean and cotton are under development, which provide new options to combat weeds resistant to glyphosate, the most widely used herbicide. With the use of DT-trait cotton and soybean, off-target dicamba drift onto susceptible crops will become a concern. To relate soybean injury to different rates of dicamba applications,... Y. Huang, H. Brand, D. Pennington, K. Reddy, S.J. Thomson |
6. North American Soil Test SummaryWith the assistance and cooperation of numerous private and public soil testing laboratories, the International Plant Nutrition Institute (IPNI) periodically summarizes soil test levels in North America (NA). Soil tests indicate the relative capacity of soil to provide nutrients to plants. Therefore, this summary can be viewed as an indicator of the nutrient supplying capacity or fertility of soils in NA. This is the eleventh summary completed by IPNI or its predecessor, the Potash &... Q. Rund, S. Murrell, A. Erbe, R. Williams, E. Williams |
7. Time Series Analysis of Vegetation Dynamics and Burn Scar Mapping at Smoky Hill Air National Guard Range, Kansas Using Moderate Resolution Satellite ImageryMilitary installments are import assets for the proper training of armed forces. To ensure the continued viability of the training grounds, management practices need to be implemented to sustain the necessary environmental conditions for safe and effective training. This analysis uses satellite imagery over time to gain insight into vegetation conditions over a large military installment. MODIS imagery was collected multiple times a year for 11 years at Smoky Hill Air National Guard Range (Smoky... E. Williams |
8. Liquid Flow Control Requirements for Crop Canopy Sensor-Based N Management in Corn: A Project SENSE Case StudyWhile on-farm adoption of crop canopy sensors for directing in-season nitrogen (N) application has been slow, research focused on these systems has been significant for decades. Much emphasis has been placed on developing and testing algorithms based on sensor output to predict N needs, but little information has been published regarding liquid flow control requirements on equipment used in conjunction with these sensing systems. Addition of a sensor-based system to a standard spray rate controller... J. Luck, J. Parrish, L. Thompson, B. Krienke, K. Glewen, R.B. Ferguson |
9. Thermal Characterization and Spatial Analysis of Water Stress in Cotton (Gossypium Hirsutum L.) and Phytochemical Composition Related to Water Stress in Soybean (Glycine Max)Studies were designed to explore spatial relationships of water and/or heat stress in cotton and soybeans and to assess factors that may influence yield potential. Investigations focused on detecting the onset of water/heat stress in row crops using thermal and multispectral imagery with ancillary physicochemical data such as soil moisture status and photosynthetic pigment concentrations. One cotton field with gradations in soil texture showed distinct patterns in thermal imagery, matching patterns... S.J. Thomson, S.L. Defauw, P.J. English, J.E. Hanks, D.K. Fisher, P.N. Foster, P.V. Zimba |
10. Rapid Identification of Mulberry Leaf Pests Based on Near Infrared Hyperspectral ImagingAs one of the most common mulberry pests, Diaphania pyloalis Walker (Lepidoptera: Pyralididae) has occurred and damaged in the main sericulture areas of China. Naked eye observation, the most dominating method identifying the damage of Diaphania pyloalis, is time-wasting and labor consuming. In order to improve the identification and diagnosis efficiency and avoid the massive outbreak of Diaphania pyloalis, near infrared (NIR) hyperspectral imaging technology combined with partial least discriminant... L. Yang, L. Huang, L. Meng, J. Wang, D. Wu, X. Fu, S. Li |
11. Sensor Comparison for Yield Monitoring Systems of Small-Sized Potato HarvestersYield monitoring of potato in real time during harvesting would be useful for farmers, providing instant yield and income information. In the study, potentials of candidate sensors were evaluated with different yield measurement techniques for yield monitoring system of small-sized potato harvesters. Mass-based (i.e., load cell) and volume-based (i.e., CCD camera) sensors were selected and tested under laboratory conditions. For mass-based sensing, an impact plate instrumented with load cells... K.M. Swe, Y. Kim, D. Jeong, S. Lee, S. Chung, M.S. Kabir |
12. Effectiveness of UAV-Based Remote Sensing Techniques in Determining Lettuce Nitrogen and Water StressesThis paper presents the results of the investigation on the effectiveness of UAV-based remote sensing data in determining lettuce nitrogen and water stresses. Multispectral images of the experimental lettuce plot at Cal Poly Pomona’s Spadra farm were collected from a UAV. Different rows of the lettuce plot were subject to different level of water and nitrogen applications. The UAV data were used in the determination of various vegetation indices. Proximal sensors used for ground-truthing... S. Bhandari, A. Raheja, M.R. Chaichi, R.L. Green, D. Do, M. Ansari, J.G. Wolf, A. Espinas, F.H. Pham, T.M. Sherman |
13. Onboard Weed Identification and Application Test with Spraying Drone SystemsCommercial spraying drone systems nowadays have the ability to implement variable rate applications according to pre-loaded prescription maps. Efforts are needed to integrate sensing and computing technologies to realize on-the-go decision making such as those on the ground based spraying systems. Besides the understudied subject of drone spraying pattern and efficacy, challenges also exist in the decision making, control, and system integration with the limits on payload and flight endurance... Y. Shi, M. Islam, K. Steele, J.D. Luck, S. Pitla, Y. Ge, A. Jhala, S. Knezevic |
14. Obstacle-aware UAV Flight Planning for Agricultural ApplicationsThe use of unmanned aerial vehicles (UAVs) has emerged as one of the most important transformational tools in modern agriculture, offering unprecedented opportunities for crop monitoring, management, and optimization. To ensure effective and safe navigation in agricultural environments, robust obstacle avoidance capabilities are required to mitigate collision risks and to ensure efficient operations. Mission planners for UAVs are typically responsible for verifying that the vehicle is following... K. Joseph, S. Pitla, V. Muvva |
15. Remote and Proximal Sensing for Sustainable Water Use in Almond Orchards in Southeast Spain in a Digital Farming ContextThe increasing expansion of irrigated almond orchards in regions of southeast Spain, facing water scarcity, underscores the need for a more effective and precise monitoring of the crop water status to optimize irrigation scheduling and improve crop water use efficiency. Remote and proximal sensing, combining visible, multispectral and thermal capabilities at different scales allows to estimate water needs, detect and quantify crop water stress, or identify different productivity zones within an... |
16. AI Enabled Targeted Robotic Weed ManagementIn contemporary agriculture, effective weed management presents a considerable challenge necessitating innovative solutions. Traditional weed control methods often rely on the indiscriminate application of broad-spectrum herbicides, giving rise to environmental concerns and unintended crop damage. Our research addresses this challenge by introducing an innovative AI-enabled robotic system designed to identify and selectively target weeds in real-time. Utilizing the advanced Machine Learning technique... A. Balabantaray, S. Pitla |
17. Advancements in Agrivoltaics: Autonomous Robotic Mowing for Enhanced Management in Solar FarmsAgrivoltaics – the co-location of solar energy installations and agriculture beneath or between rows of photovoltaic panels – has gained prominence as a sustainable and efficient approach to land use. The US has over 2.8 GW in Agrivoltaics, integrating crop cultivation with solar energy. However, effective vegetation management is critical for solar panel efficiency. Flat, sunny agricultural land accommodates solar panels and crops efficiently. The challenge lies in managing grass... S. Behera, S. Pitla |
18. Implementation of Autonomous Material Re-filling Using Customized UAV for Autonomous Planting OperationsThis project introduces a groundbreaking use case for customized Unmanned Aerial Vehicles (UAVs) in precision agriculture, focused on achieving holistic autonomy in agricultural operations through multi-robot collaboration. Currently, commercially available drones for agriculture are restrictive in achieving collaborative autonomy with the growing number of unmanned ground robots, limiting their use to narrow and specific tasks. The advanced payload capacities of multi-rotor UAVs,... V. Muvva, H. Mwunguzi, S. Pitla, K. Joseph |
19. Wheat Spikes Counting Using Density Prediction Convolution Neural NetworkVision-based wheat spikes counting can be valuable for pre-harvest yield estimation for growers and researchers. In this study, wheat spike counting convolutions neural networks were implemented to solve the problem of vision-based wheat yield prediction problem. Encoder-decoder style convolutional neural networks (CNN) were developed with a Global Sum Pooling (GSP) layer as its output layer and trained to produce a density map which predicts the pixelwise wheat spikes density. This... C. Liew, S. Pitla |
20. AIR-N: AI-Enabled Robotic Precision Nitrogen Management PlatformThe AI-Enabled Robotic Nitrogen Management (AIR-N) system is a versatile, cloud-based platform designed for precision nitrogen management in agriculture, targeting the reduction of nitrous oxide emissions as emphasized by the EPA. This end-to-end integrated system is adaptable to various cloud services, enhancing its applicability across different farming environments. AIR-N's framework consists of three primary components: a sensing layer for gathering data, a cloud layer where AI and machine... A. Kalra, S. Pitla, J.D. Luck |
21. Proximal, Drone, and Satellite Sensors for In-season Variable Nitrogen Rate Application in Corn: a Comparative Study of Fixed-rate and Sensor-based ApproachesEffective nitrogen (N) management is essential for optimizing corn yield and enhancing agricultural sustainability. Traditional N application methods, typically uniform split pre-plant and in-season applications, often neglect the spatial and temporal variability of N requirements across different fields and years, potentially leading to N overuse. With the rise of precision agriculture technologies, it is crucial to reassess these conventional practices. This study had two main objectives: first,... A. Jakhar, A. Bhattarai, L. Bastos, G. Scarpin |