The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Machine Vision Based Citrus Mass Estimation during Post Harvesting Using Supervised Machine Learning Algorithms
A machine vision system was investigated as a means of estimating citrus fruit mass during post harvesting operation. The system consisted of a CCD color camera with a high frame rate, two lamps, an incremental encoder and a data acquisition card. The system was implemented toward the development of an advanced citrus yield mapping system. Such yield mapping system is one of the viable precision technologies that allows the citrus grower to efficiently manage in-grove spatial variability of different factors such as soil type, soil fertility, moisture content, etc., and helps increase yield and profit. Thus, an image processing algorithm was developed to identify citrus fruit in images acquired in a commercial citrus grove located in Fort Basinger, Florida. Supervised machine learning algorithms, such as naive Bayes classifier, artificial neural network and decision tree, were utilized to implement fruit detection and segmentation. For the fruit mass estimation, an equation mapping fruit pixel area to fruit mass was established through a mass calibration process. Using the mapping equation, the fruit mass was estimated. The R-2 values in mass estimation using naive Bayes and artificial neural network yielded more than 0.92, whereas decision tree based mass estimation resulted in the R-2 of 0.804.
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