Authors
| Filter results21 paper(s) found. |
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1. Sensing The Inter-row For Real-time Weed Spot Spraying In Conventionally Tilled Corn FieldsThe spatial distribution of weeds is aggregated most of the time in crop fields. Site-specific management of weeds could result in economical and environmental benefits due to herbicide... L. Longchamps, B. Panneton, M. Simard, R. Theriault, T. Roger |
2. Partial Weed Scouting For Exhaustive Real-time Spot Spraying Of Herbicides In CornReal-time spot spraying of weeds implies the use of plant detectors ahead of a sprayer. The range of weed spatial autocorrelation perpendicularly to crop rows is often greater than the space between the corn rows. To assess the possibility of using less than one plant detector scouting each inter-row, a one hectare field was entirely sampled with ground pictures at the appropriate timing for weed spraying. Different ways of disposing the detectors ahead of the sprayer were virtually tested. Scouting... L. Longchamps, B. Panneton, G.D. Leroux, M. Simard, R. Theriault |
3. Can Active Sensor Based NDVI Consistently Classify Wheat Genotypes?ABSTRACT ... M.A. Naser, R. khosla, S. Haley, R. Reich, L. Longchamps, M. Moragues, G.W. buchleiter, G.S. Mcmaster |
4. Early Detection of Corn N-Deficiency by Active Fluorescence Sensing in MaizeGlobally, the agricultural nitrogen use efficiency (NUE) is no more than 40 %. This low efficiency comes with an agronomic, economic and environmental cost. By better management of spatial and temporal variability of crop nitrogen need, NUE can be improved. Currently available crop canopy sensors based on reflectance are capable... R. Khosla, D.G. Westfall, L. Longchamps |
5. Comparing Sensing Platforms for Crop Remote SensingRemote sensing offers the possibility to obtain a rapid and non-destructive diagnosis of crop health status. This gives the opportunity to apply variable rates of fertilizers to meet the actual crop needs at every locations of the field. However, the commonly used normalized difference vegetation index (NDVI)... R. Khosla, L. Longchamps |
6. Development of a Quick Diagnosis Method to Target Fields with Better Potential for Site-Specific Weed ManagementSite-specific weed management appears as an innovative way of saving herbicides in crop while maintaining yield. This can potentially lead economic and ecological benefits. However, it was reported in the literature that savings range from 1 % to 94 % from one field to the other. This implies that certain fields... B. Panneton, M. Simard, G.D. Leroux, L. Longchamps |
7. Testing The Author Sequence - FinalizeThis is just a test to verify the bug with the authors sequence. ... L. Longchamps, B. Panneton, D.G. Westfall, R. Khosla |
8. Spectral Vegetation Indices to Quantify In-field Soil Moisture VariabilityAgriculture is the largest consumer of water globally. As pressure on available water resources increases, the need to exploit technology in order to produce more food with less water becomes crucial. The technological hardware requisite for precise water delivery methods such as variable rate irrigation is commercially available. Despite that, techniques to formulate a timely, accurate prescription for those systems are inadequate. Spectral vegetation indices, especially Normalized Difference... J. Siegfried, R. Khosla, L. Longchamps |
9. Detecting Nitrogen Variability at Early Growth Stages of Wheat by Active Fluorescence and NDVILow efficiency in the use of nitrogen fertilizer, has been reported around the world which often times result in high production costs and environmental damage. Today, unmanned aerial vehicles (UAV) cameras are being used to obtain conditions of crops, and can cover large areas in a short time. The objectives of this study were (i) to investigate N-variability in wheat at early growth stages using induced fluorescence indices, NDVI measured by active sensor and NDVI obtained by digital imagery;... E. Patto pacheco, J. Liu, L. Longchamps, R. Khosla |
10. Climate Smart Precision Nitrogen ManagementClimate Smart Agriculture (CSA) aims at improving farm productivity and profitability in a sustainable way while building resilience to climate change and mitigating the impacts of agriculture on greenhouse gas emissions. The idea behind this concept is that informed management decision can help achieve these goals. In that matter, Precision Agriculture goes hand-in-hand with CSA. The Colorado State University Laboratory of Precision Agriculture (CSU-PA) is conducting research on CSA practices... L. Longchamps, R. Khosla, R. Reich |
11. Pest Detection on UAV Imagery Using a Deep Convolutional Neural NetworkPresently, precision agriculture uses remote sensing for the mapping of crop biophysical parameters with vegetation indices in order to detect problematic areas, and then send a human specialist for a targeted field investigation. The same principle is applied for the use of UAVs in precision agriculture, but with finer spatial resolutions. Vegetation mapping with UAVs requires the mosaicking of several images, which results in significant geometric and radiometric problems. Furthermore, even... Y. Bouroubi, P. Bugnet, T. Nguyen-xuan, C. Bélec, L. Longchamps, P. Vigneault, C. Gosselin |
12. Precision Nitrogen and Water Management for Enhancing Efficiency and Productivity in Irrigated MaizeNitrogen and water continue to be the most limiting factors for profitable maize production in the western Great Plains. The objective of this research was to determine the most productive and efficient nitrogen and water management strategies for irrigated maize. This study was conducted in 2016 at Colorado State University’s Agricultural Research Development and Educational Center, in Fort Collins, Colorado. The experiment included a completely randomized block design with five... E. Phillippi, R. Khosla, L. Longchamps, P. Turk |
13. Observational Studies in Agriculture: Paradigm Shift RequiredThere is a knowledge gap in agriculture. For instance, there is no way to tell with precision what is the outcome of cutting N fertilizer by a quarter on important outcomes such as yield, net return, greenhouse gas emissions or groundwater pollution. Traditionally, the way to generate knowledge in agriculture has been to conduct research with the experimental method where experiments are conducted in a controlled environment with trials replicated in space and... L. Longchamps, B. Panneton, N. Tremblay |
14. Machine Learning Techniques for Early Identification of Nitrogen Variability in MaizeCharacterizing and managing nutrient variability has been the focus of precision agriculture research for decades. Previous research has indicated that in-situ fluorescence sensor measurements can be used as a proxy for nitrogen (N) status in plants in greenhouse conditions employing static sensor measurements. Indeed, practitioners of precision N management require determination of in-season plant N status in real-time at field scale to enable the most efficient N fertilizer... D. Mandal, R.D. Siqueira, L. Longchamps, R. Khosla |
15. Enhancing NY State On-farm Experimentation with Digital AgronomyAgriculture is putting pressure on the ecosystems and practices need to evolve towards a more sustainable way of producing food. Industrial agriculture has imposed a unique production model on the ecosystems while it is now understood that it is more sustainable to adapt the production model to the ecosystem. This involves adapting existing solutions to the local agricultural context and developing new solutions that are best suited to the local ecosystem. Farmers are doing this by conducting... L. Longchamps |
16. In-season Nitrogen Prediction Evaluation Using Airborne Imagery with AI Techniques in Commercial Potato ProductionIn modern agriculture, timely and precise nitrogen (N) monitoring is essential to optimize resource management and improve trade benefits. Potato (Solanum tuberosum L.) is a staple food in many regions of the world, and improving its production is inevitable to ensure food security and promote related industries. Traditional methods of assessing nitrogen are labour-intensive, time-consuming, and require subjective observations. To address these limitations, a combination of multispectral... B. Javed, A. Cambouris, M. Duchemin, L. Longchamps, P.S. Basran, S. Arnold, A. Fenech, A. Karam |
17. A Data Retrieval System to Support Observational Research of On-Farm ExperimentationObservational research is a powerful methodology, capable of rapidly identifying trends and patterns present in complex systems. New work seeks to apply these techniques to agronomic production systems. While data generated from on-farm experimentation are often considered anecdotal, these data hold significant importance for farmers because they originate from their distinctive agricultural systems. Combining the large volumes of farmer-collected data with remote sensing, environmental, and biophysical... P. Lanza, A. Yore, L. Longchamps |
18. Soil Microbial Biomass and Bacterial Diversity Enhanced Through Winter Cover Cropping in Paddy FieldsRice production is typically based on input-intensive and often environmentally unsustainable monoculture system. Alternatives are increasing, such as fallow cover cropping and rice–fish coculture (RFC). However, options of fallow cover cropping in RFC are scarcely explored, and the soil microbial response strategies to cover cropping remain unclear. Here, we evaluated soil-plant-microbe interactions under three cover cropping systems: Chinese milk vetch single cropping (CM), rapeseed single... S. Cai, S. Xu, D. Zhang, H. Zhu, L. Longchamps |
19. Optimal Placement of Soil Moisture Sensors in an Irrigated Corn FieldPrecision agricultural practices rely on characterization of spatially and temporally variable soil and crop properties to precisely synchronize inputs (water, fertilizer, etc.) to crop needs; thereby enhancing input use efficiency and farm profitability. Generally, the spatial dependency range for soil water content is shorter near the soil surface compared to deeper depths, suggesting a need for more sampling locations to accurately characterize near-surface soil water content. However, determining... D. Mandal, L. Longchamps, R. Khosla |
20. Using Dynamic Crop Growth Data to Assess Early Season N Status in MaizeNitrogen (N) is perhaps the most important mineral nutrient determining crop growth and yield. Fertilizer sources can vary, but it is used in practically all cropping systems, and accounts for one of the highest input costs. Farmers often overapply N to their fields as a simple "insurance policy" to guarantee maximum yields. This can be problematic due to the volatile nature of N in the environment, as well reducing potential profits by not optimizing the rates. There... A. Yore, P. Lanza, L. Longchamps |
21. On-Farm Experimentation Community MeetingMeeting Agenda: Updates for the OFE-C Newsletters Increased membership Conference Global OFE Network (GOFEN) Scientists AND Farmers Global Directory Discussion points OFE-C Outreach Country reps for the OFE-C / Entry point Newsletter... L. Longchamps |