Proceedings
Authors
| Filter results11 paper(s) found. |
|---|
1. On-the-go Condition Mapping For Harvesting MachineryIn recent years control systems have been used to alleviate the task of harvesting machinery operators. Automation allows the operator to spend more time on other tasks such as coordinating transport. Moreover, such control systems guarantee constant performance throughout the day whereas an operator gets tired. The perfect control system anticipates on the harvest condition, just like an experienced operator would. The operator makes a visual assessment of the condition in terms of... T. Coen, J. De baerdemaeker, W. Saeys |
2. 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 |
3. 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 |
4. Applications Of Small UAV Systems For Tree And Nursery Inventory ManagementUnmanned aerial vehicles (UAV) systems could provide low-cost and high spatial resolution aerial images. These features and ease of operation make it a practical tool for applications in precision agriculture and horticulture. This paper highlights the application of UAV systems in tree counting, which is vital for tree inventory management and yield estimation. In this paper, two types of trees were discussed. One type is with non-uniform canopy area (e.g. container plants and citrus... Y. She, R. Ehsani, J. Robbins, J. Owen, J.N. Leiva |
5. Towards Automated Pneumatic Thinning Of Floral Buds On Pear TreesThinning of pome and stone fruit is an important horticultural practice that is used to enhance fruit set and quality by removing excess floral buds. As it is still mostly conducted through manual labor, thinning comprises a large part of a grower’s production costs. Various thinning machines developed in recent years have clearly demonstrated that mechanization of this technique is both feasible and cost effective. Generally, these machines still lack sufficient selectivity... N. Wouters, R. Van beers, B. De ketelaere, T. Deckers, J. De baerdemaeker, W. Saeys |
6. Joint Structure and Colour Based Parametric Classification of Grapevine Organs from Proximal Images Through Several Critical Phenological StagesProximal colour imaging is the most time and cost-effective automated technology to acquire high-resolution data describing accurately the trellising plane of grapevine. The available textural information is meaningful enough to provide altogether the assessment of additional agronomic parameters that are still estimated either manually or with dedicated and expensive instrumentations. This paper proposes a new framework for the classification of the different organs visible in the trellising... F.Y. Abdelghafour, R. Rosu, B. Keresztes, C. Germain, J. Da costa |
7. Designated Value for a Field Polygon Based on Imagery Data: A Case Study of Crop Vigor in Agricultural Application for IrrigationAny irrigation action for a field management zone, which is based on images, requires a transformation into single value. Since data distribution is ab-normal in an image, using a mean value to estimate the crop coefficient (Kc), an overlaid polygon may not represent properly its water demand. Therefore, this project’s aim was to examine to which extent different statistics of potential designated values will affect an estimated Kc, and consequently affect irrigation practices. Satellite... R. Rud, O. Beeri, S. Mey-tal |
8. Detecting Variability in Plant Water Potential with Multi-Spectral Satellite ImageryIrrigation Intelligence is a practice of precise irrigation, with the goal of providing crops with the right amount of water, at the right time, for optimized yield. One of the ways to achieve that, on a global scale, is to utilize Landsat-8 and Sentinel-2 images, providing together frequent revisit cycles of less than a week, and an adequate resolution for detection of 1 ha plots. Yet, in order to benefit from these advantages, it is necessary to examine the information that can be extracted... O. Beeri, S. May-tal, R. Rud, Y. Raz, R. Pelta |
9. Field Test of a Satellite-Based Model for Irrigation Scheduling in CottonCotton irrigation in Israel began in the mid-1950s. It is based on an irrigation protocol developed over dozens of years of cotton farming in Israel, and proved to provide among the world's best cotton yield results. In this experiment, we examined the use of an irrigation recommendation system that is based on satellite imagery and hyper-local meteorological data, "Manna treatment", compared to the common irrigation protocols in Israel, which use a crop coefficient (Kc) table and... O. Beeri, S. May-tal, J. Raz, R. Rud |
10. Sun Effect on the Estimation of Wheat Ear Density by Deep LearningEar density is one of the yield components of wheat and therefore a variable of high agronomic interest. Its traditional measurement necessitates laborious human observations in the field or destructive sampling. In the recent years, deep learning based on RGB images has been identified as a low-cost, robust and high-throughput alternative to measure this variable. However, most of the studies were limited to the computer challenge of counting the ears in the images, without aiming to convert... S. Dandrifosse, E. Ennadifi, A. Carlier, B. Gosselin, B. Dumont, B. Mercatoris |
11. 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 |