Proceedings
Authors
| Filter results4 paper(s) found. |
|---|
1. Economic Profitability Of Site-specific Pesticide Management At The Farm Scale For Crop Systems In Haute-Normandie (France)Modern agriculture requires decision making criteria applicable to different scales of territory in order to reconcile productivity and respect of the environment, particularly for pest management. Taking into account the recent environmental... O. Bourgain, C. Duval, J. Llorens |
2. Site-specific Fertilization Management: Influence Of The Past History Of The Addition Of Fertilizers On The Intra Field Variability Of The Rate Of P And K In The Soil.Site specific crop management adapts the fertilizer amount applied in relation to the intra field crop needs. In this context, tries were carried out under field conditions. The aim of the trials was to develop technico-economic baseline data and methodology of soil sampling for precision agriculture in Upper-Normandy. ... C. Duval, J. Llorens, C. Duval, C. Duval, S. Ta |
3. Assessing Crop Yield and Profitability with Site-specific Seed Rate Management in Corn and Soybean Cropping SystemsIntegrating the information about soil and topographic properties for variable rate seeding is a prerequisite for improved crop production and thus profit. However, limited studies have explored the geospatial and machine learning approaches to understand factors influencing crop yield and profit under site-specific seed rate management. The objectives of this study were to: a) observe the effect of variable seeding rate based on soil and topographic properties on soybean and corn grain yield,... J. Neupane, N. Joshi, J.P. Fulton, S. Khanal, A. B K, B. Bhattarai |
4. Using Machine Vision to Build Field Maps of Forage Quality and the Need for Agriculture-specific Machine Vision NetworksMachine vision systems have truly come of age over the past decade. These networks are relatively simple to implement with systems such as YOLOv5 or the more recent YOLOv8. They are also relatively easy and computationally cheap to retrain to a custom data set, allowing for customization of these networks to new object detection and classification tasks. With this ease, it is no surprise that we are seeing an explosion of these networks and their application through all aspects of agriculture.... P. Nugent, J. Neupane |