Proceedings
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| Filter results6 paper(s) found. |
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1. Compatible ISOBUS Applications Using a Computational Tool for Support the Phases of the Precision Agriculture Cycle... W.C. Lopes, G. Domingues, R.V. Sousa, A.J. Porto, R.Y. Inamasu, R.R. Pereira |
2. A Comparison Of Performance Between UAV And Satellite Imagery For N Status Assessment In CornA number of platforms are available for the sensing of crop conditions. They vary from proximal (tractor-mounted) to satellites orbiting the Earth. A lot of interest has recently emerged from the access to unmanned aerial vehicles (UAVs) or drones that are able to carry sensors payloads providing data at very high spatial resolution. This study aims at comparing the performance of a UAV and satellite imagery acquired over a corn nitrogen response trial set-up. The nitrogen (N) response... P. Vigneault, N. Tremblay, M.Y. Bouroubi, C. Bélec, E. Fallon |
3. Development of Sensor Reflection Indices To Predict Yield And Protein Content Based On In-Season N StatusEnvironmental and economic demands make it necessary for farmers to adopt management systems that improve Nitrogen Use Efficiency. The premium paid to producers has made farmers striving for maximum grain protein levels because protein is a very important quality component of grains and an important attribute in the market place. The protein content of wheat grains approximately ranges from 8 to 20%. The optimization of nitrogen (N) fertilization is the object of intense research efforts... U. Yegul, B. Talebpour, U. TÜrker, B.M. EmİnoĞlu, G.T. Seyhan, A. Çolak |
4. Generative Modeling Method Comparison for Class Imbalance CorrectionAn image dataset, for use in object detection of hay bales, with over 6000 images of both good and bad hay bales was collected. Unfortunately, the dataset developed a class imbalance, with more good bale images than bad bales. This dataset class imbalance caused the bad bale class to over train and the good bale class to under train, severely impacting precision, and recall. To correct this imbalance and provide a comparison of differing generative modeling methods; three different... B. Vail, Z. Oster, B. Weinhold |
5. Machine Vision in Hay Bale ProductionThe goal of this project is to develop a system capable of real-time detection, pass/fail classification, and location tracking of large square hay bales under field conditions. First, a review of past and current methods of object detection was carried out. This led to the selection of the YOLO family of detectors for this project. The image dataset was collected through help from our sponsor, collection of images from the K-STATE research farm, and images collected from the... B. Vail |
6. Integration of Post Emergence Herbicide (PoE) with Nano-urea for Optimized Management of Weed in Indian Black Mustard (Brassica Juncea L.)Nano-urea (NU) is gaining attention due to its environmental benefits and precise application. Unlike traditional urea fertilizers, NU is engineered at the nanoscale, which increases its efficiency and reduces environmental impacts. However, limited research has been done to evaluate the combined effect of herbicides and NU. Therefore, the overarching goal of our study is to conduct field trials to understand the optimization rates of the synergized composition of herbicide and NU. Our hypothesis... B. Duary, U. Debangshi, W. Dutta, G. Jha |