Proceedings
Authors
| Filter results4 paper(s) found. |
|---|
1. Reverse Modelling of Yield-Influencing Soil Variables in Case of Few Soil DataOur hypothesis was that simple models can be applied to predict yield by using only those yield data which spatially coincide with the soil data and the remaining yield data and the models can be used to test different sampling and interpolation approaches commonly applied in precision agriculture and to better predict soil variables at not observed locations. Three strategies for composite sample collection were compared in our study. Point samples were taken 1.) along lines within homogenous... I. Sisák, A. Benő, K. Szabó, M. Kocsis, J. Abonyi |
2. Characterization of Soil Properties, Nutrient Distribution and Rice (Oryza Sativa.) Productivity As Influenced by Tillage Methods in a Typical GleysolsGlobal emphasis and interest in conservation Tillage in agricultural soils has tremendously increased in the last few years, especially no tillage with its potential to improve soil physicochemical properties, reduce nutrient leaching as well as improve crop productivity in a more sustainable manner. Several questions still exist with regard to the true role of no tillage in improving soil fertility. A two year field study was conducted to characterize the effects of different tillage methods... F. Issaka, L. Yongtao, L. Jiuhao, M.M. Buri, E. Asenso, A. Sheka kanu, Z. Zhao |
3. Eco-friendly LiDAR Drone Surveying for Sugarcane Land Leveling in the Cauca River Valley, ColombiaLand leveling is a crucial process in sugarcane cultivation in the Cauca River Valley. It plays a vital role in ensuring proper water flow within the fields, reducing fuel consumption for water pumping, promoting seed emergence, and facilitating other mechanized tasks that can be carried out more quickly and efficiently. Traditionally, land leveling involves the use of high-powered tractors (typically around 310 horsepower) equipped with high-precision topographic survey systems from... S. Anderson-guerrero, A.M. Caballero-rodriguez, O. Munar vivas, J.F. Mateus-rodriguez |
4. Within Field Cotton Yield Prediction Using Temporal Satellite Imagery Combined with Deep LearningCrop yield prediction at the field scale plays a pivotal role in enhancing agricultural management, a vital component in addressing global food security challenges. Regional or county-level data, while valuable for broader agricultural planning, often lacks the precision required by farmers for effective and timely field management. The primary obstacle in utilizing satellite imagery to forecast crop yields at the field level lies in its low temporal and spatial resolutions. This study aims to... R. Karn, O. Adedeji, B.P. Ghimire, A. Abdalla, V. Sheng, G. Ritchie, W. Guo |