Proceedings
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| Filter results10 paper(s) found. |
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1. Hyperspectral Imagery for the Detection of Nitrogen Stress in Potato for In-season Management... T.J. Nigon, C. Rosen, D. Mulla, Y. Cohen, V. Alchanatis, R. Rud |
2. Evaluating Water Status in Potato Fields Using Combined Information from RGB and Thermal Aerial ImagesPotato yield and quality are highly dependent on an adequate supply of water. In this study the combined information from RGB and thermal aerial images to evaluate... Y. Cohen, V. Alchanatis, B. Heuer, H. Lemcoff, M. Sprintsin, C. Rosen, D. Mulla, T. Nigon, Z. Dar, A. Cohen, A. Levi, R. Brikman, T. Markovits, R. Rud |
3. Evaluating Remote Sensing Based Adaptive Nitrogen Management for Potato ProductionConventional nitrogen (N) management for potato production in the Upper Midwest, USA relies on using split-applications of N fertilizer or a controlled release N product. Using remote sensing to adaptively manage N applications has the potential to improve N use efficiency and reduce losses of nitrate to groundwater, which are important regional concerns. A two-year plot-scale experiment was established to evaluate adaptive N-management using remote sensing compared to conventional practices for... B. Bohman, D. Mulla, C. Rosen |
4. Evaluating the Potential of Improving In-season Nitrogen Status Diagnosis of Potato Using Leaf Fluorescence Sensors and Machine LearningPrecision nitrogen (N) management is particularly important for potato crops due to their high N fertilizer demand and high N leaching potential caused by their shallow root systems and preference for coarse-textured soils. Potato farmers have been using a standard lab analysis called petiole nitrate-N (PNN) test as a tool to diagnose potato N status and guide in-season N management. However, the PNN test suffers from many disadvantages including time constraints, labor, and cost of analysis.... S. Wakahara, Y. Miao, S. Gupta, C. Rosen, K. Mizuta, J. Zhang, D. Li |
5. Limitations of Yield Monitor Data to Support Field-scale ResearchPrecision agriculture adoption on farms continues to grow globally on farms. Today, yield monitors have become standard technologies on grain, cotton and sugarcane harvesters. In recent years, we have seen industry and even academics leveraging the adoption of precision agriculture technologies to conduct field-scale, on-farm research. Industry has been a primary driver of the increase in on-farm research globally through the development of software to support on-farm research. ... J.P. Fulton, S.A. Shearer, A. Gauci, A. Lindsey, D. Barker, E. Hawkins |
6. Multi-sensor Remote Sensing: an AI-driven Framework for Predicting Sugarcane FeedstockPredicting saccharine and bioenergy feedstocks in sugarcane enables stakeholders to determine the precise time and location for harvesting a better product in the field. Consequently, it can streamline workflows while enhancing the cost-effectiveness of full-scale production. On one hand, Brix, Purity, and total reducing sugars (TRS) can provide meaningful and reliable indicators of high-quality raw materials for industrial food and fuel processing. On the other hand, Cellulose, Hemicellulose,... M. Barbosa, D. Duron, F. Rontani, G. Bortolon, B. Moreira, L. Oliveira, T. Setiyono, L. Shiratsuchi, R.P. Silva, K.H. Holland |
7. Algorithm to Estimate Sorghum Grain Number from Panicles Using Images Collected with a Smartphone at Field-scaleAn estimation of on-farm yield before harvest is important to assist farmers on deciding additional input use, time to harvest, and options for end uses of the harvestable product. However, obtaining a rapid assessment of on-farm yield can be challenging, even more for sorghum (Sorghum bicolor L.) crop due to the complexity for accounting for the grain number at field-scale. One alternative to reduce labor is to develop a rapid assessment method employing computer vision and artificial intelligence... G.N. Nocera santiago, P. Cisdeli magalhães, I. Ciampitti, L. Marziotte |
8. Evaluating Different Strategies for In-season Potato Nitrogen Status Diagnosis Using Two Leaf SensorsAccurate and efficient in-season diagnosis of potato nitrogen (N) status is key to the success of in-season N management for improved profitability and environmental protection. Sensor-based approaches will support more timely decision making compared to plant tissue-based approaches. SPAD-502 (SPAD; Konica Minolta, Tokyo, Japan) is a commonly used sensor for potato N status diagnosis. Dualex Scientific+ (Dualex; METOS® by Pessl Instruments, Weiz, Austria) is a new leaf chlorophyll... S. Wakahara, Y. Miao, S. Gupta, C. Rosen |
9. A Digital Interactive Decision Dashboard to Analyze, Store and Share Year-to-year Crop Genotype YieldThe lag time between data collection and sharing is a critical bottleneck in order to make impactful decision at farmer field-scale. Following this line, there is a need for developing a digital interactive decision dashboard for sharing results of crop trials, in parallel to establish a database for storing data. These crop trials, invaluable for farmers seeking to determine the optimal genotype for their crops, are at risk of becoming obsolete due to the current format and the lack of more near... P. Magalhaes cisdeli, G.N. Nocera santiago, I. Ciampitti, C. Hernandez |
10. From Scientific Literature to the End User: Democratizing Access to Data Products Through Interactive ApplicationsIn recent years, the sustained advance in the creation of powerful programming libraries is allowing not only the creation of complex models with predictive capabilities but also revolutionizing visualization processes and the deployment of interactive applications. Some of these tools, such as Streamlit or Shiny frameworks in languages such as Python or R, allow us to create from simple applications with friendly interfaces to complex tools. These interactive digital decision dashboards allow... C. Hernandez, A. Correndo, J. Lacasa, P. Magalhaes cisdeli, G.N. Nocera santiago, I. Ciampitti |