Proceedings
Authors
| Filter results12 paper(s) found. |
|---|
1. Winter Wheat Genotype Effect on Canopy Reflectance: Implications for Using NDVI for In-season Nitrogen Topdressing RecommendationsActive optical sensors (AOSs) measure crop reflectance at specific wavelengths and calculate vegetation indices (VIs) that are used to prescribe variable N fertilization. Visual observations of winter wheat (Triticum aestivum L.) plant greenness and density suggest that VI values may be genotype specific. Some sensor systems use correction coefficients to eliminate the effect of genotype on VI values. This study was conducted to assess the effects of winter wheat cultivars and growing conditions... O.S. Walsh, S.M. Samborski, M. Stępień, D. Gozdowski, D.W. Lamb, E.S. gacek, T. Drzazga |
2. On-Farm Evaluation of an Active Optical Sensor Performance for Variable Nitrogen Application in Winter WheatWinter wheat (Triticum aestivum L.) represents almost 50% of total cereal production in the European Union, accounting for approximately 25% of total mineral nitrogen (N) fertilizer applied to all crops. Currently, several active optical sensor (AOS) based systems for optimizing variable N fertilization are commercially available for a variety of crops, including wheat. To ensure successful adoption of these systems, definitive measurable benefits must be demonstrated. Nitrogen management strategies... O.S. Walsh, S.M. Samborski, D. Gozdowski, M. Stępień, E. Leszczyńska |
3. Smart Agriculture: A Futuristic Vision of Application of the Internet of Things (IoT) in Brazilian AgricultureWith the economy based on agribusiness, Brazil is an important representative on the world stage in agricultural production, either in terms of quantity or cultivated diversity due to a scenario with vast arable land and favorable climate. There are many crops that are adapteble to soils of the country. Despite the global representation, it is known that the Brazilian agricultural production does not yet have a modern agriculture by restricting the use of new technologies to farmers with better... C.L. Bazzi, R. Araujo, E.G. Souza, K. Schenatto, A. Gavioli, N.M. Betzek |
4. A Comparison of Three-Dimensional Data Acquisition Methods for Phenotyping ApplicationsCurrently Phenotyping is primarily performed using two-dimensional imaging techniques. While this yields interesting data about a plant, a lot of information is lost using regular cameras. Since a plant is three-dimensional, the use of dedicated 3D-imaging sensors provides a much more complete insight into the phenotype of the plant. Different methods for 3D-data acquisition are available, each with their inherent advantages and disadvantages. These have to be addressed depending on the particular... O. Scholz, F. Uhrmann, S. Gerth, K. Pieger, J. Claußen |
5. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS ImageryDeep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high resolution,... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal |
6. Report on Research and Extension of Precision Agriculture in JapanThe objective of this report is to present the current status of precision agriculture and smart agriculture in Japan. As of 2023, there are approximately 150 precision agriculture-related venture companies in Japan, and the number is increasing every year. Research related to precision agriculture is mainly conducted by the IT and Mechatronics Subcommittee of the Japanese Society for Agricultural and Biological Engineering, which consists of about 1,000... E. Morimoto |
7. Creating a Comprehensive Software Framework for Sensor-driven Precision AgricultureRobots and GPS-guided tractors are the backbone of smart farming and precision agriculture. Many companies and vendors contribute to the market, each offering their own customized solutions for common tasks. These developments are often based on vendor-specific, proprietary components, protocols and software. Many small companies that produce sensors, actuators or software for niche applications could contribute their expertise to the global efforts of creating smart farming solutions, if their... O. Scholz, F. Uhrmann, M. Weule, T. Meyer, A. Gilson, J. Makarov, J. Hansen, T. Henties |
8. Cherry Yield Forecast: Harvest Prediction for Individual Sweet Cherry TreesDigitalization continues to transform the agricultural sector as a whole and also affects specific niches like horticulture. Particularly in fruit and wine production, the focus is on the application of sensor systems and data analysis aiming at automated detection of drought stress or pests in vineyards or orchards. As part of the “For5G” project, we are developing an end-to-end methodology for the creation of digital twins of fruit trees, with a strong focus... A. Gilson, L. Meyer, A. Killer, F. Keil, O. Scholz, D. Kittemann, P. Noack, P. Pietrzyk, C. Paglia |
9. Adoption of Precision Agriculture in JapanJapan is a country facing global challenges in terms of a declining and aging agricultural population, making the establishment of a sustainable production system a matter of urgency from the perspective of food security. While respecting Japan's traditional knowledge, the author believes that precision agriculture is an effective solution to resolve this situation. We argue that data-driven agriculture presents a higher degree of affinity with Japanese farmers, providing a more viable pathway... E. Morimoto |
10. Development of a Measurement and Analysis System for Tillage Operations in Paddy FieldsThis study developed a foundational technology for real-time tillage depth measurement using Inertial Measurement Units (IMUs). The ultimate goal is to enable variable-rate tillage operations tailored to spatial variations in topsoil depth. The system consisted of an RTK-GNSS module and two IMUs to measure the respective pitch angles of the tractor and implement. Tillage depth was estimated using a model derived from the geometric relationship between the implement’s pitch angle and its... E. Morimoto |
11. Estimating Rice Canopy Height Using a Ground-based Slam Lidar SystemThis study evaluates the application of a ground-based LiDAR system, integrated with a Simultaneous Localization and Mapping (SLAM) algorithm, to estimate rice crop canopy height (CH). Using the Velodyne VLP-16 LiDAR sensor, point cloud data were collected and processed to map the rice field. The experimental area covered approximately 600 m² during the crop’s vegetative stage. LiDAR-derived canopy height (LCH) was extracted using percentile-based metrics and compared with manual measurements... E. Morimoto |
12. Development of Rgb and Lidar Fusion Based Pear Fruit Quantification and Mapping SystemThis study presents a system for accurate fruit quantification using LiDAR-RGB sensor fusion. The system projects 2D fruit detections from a YOLO model onto a 3D map generated via SLAM, assigning a unique coordinate to each fruit to prevent double-counting. This approach achieved an aggregate accuracy of 98.5%, with a predicted total of 535 fruits compared to the 527 observed. The resulting data revealed significant fruit density variations (3.2 to 12.6 fruits/m²), establishing the system... E. Morimoto |