Borja Espejo García

Borja Espejo García
Agricultural University of Athens · Department of Natural Resources Management and Agricultural Engineering (NRM&AE)

Doctor of Engineering

About

19
Publications
6,634
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
299
Citations
Citations since 2016
18 Research Items
297 Citations
2016201720182019202020212022050100150
2016201720182019202020212022050100150
2016201720182019202020212022050100150
2016201720182019202020212022050100150

Publications

Publications (19)
Article
Full-text available
Robotics has been increasingly relevant over the years. The ever-increasing demand for productivity, the reduction of tedious labor, and safety for the operator and the environment have brought robotics to the forefront of technological innovation. The same principle applies to agricultural robots, where such solutions can aid in making farming eas...
Article
Full-text available
Wine grapes need frequent monitoring to achieve high yields and quality. Non-destructive methods, such as proximal and remote sensing, are commonly used to estimate crop yield and quality characteristics, and spectral vegetation indices (VIs) are often used to present site-specific information. Analysis of laboratory samples is the most popular met...
Article
Early diagnosis of nutrient deficiencies can play a major role in avoiding significant agricultural losses and increasing the final yield while preserving the environment through efficient fertilizer usage. In this work, we study how well nutrient deficiency symptoms can be recognized in RGB images by using deep neural networks and transfer learnin...
Article
Full-text available
Broccoli is an example of a high-value crop that requires delicate handling throughout the growing season and during its post-harvesting treatment. As broccoli heads can be easily damaged, they are still harvested by hand. Moreover, human scouting is required to initially identify the field segments where several broccoli plants have reached the de...
Article
In modern agriculture, visual recognition systems based on deep learning are arising to allow autonomous machines to execute field operations in crops. However, for obtaining high performances, these methods need high amounts of data, which are usually scarce in agriculture. The main reason is that building an agricultural dataset covering exhausti...
Article
Full-text available
The most common method for determining wine grape quality characteristics is to perform sample-based laboratory analysis, which can be time-consuming and expensive. In this article, we investigate an alternative approach to predict wine grape quality characteristics by combining machine learning techniques and normalized difference vegetation index...
Article
Full-text available
In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, these techniques usually require extensive involvement of experts working iteratively in the developm...
Article
In recent years, automatic weed control has emerged as a promising alternative for reducing the amount of herbicide applied to the field, instead of conventional spraying. The use of artificial intelligence through the implementation of deep learning for early weeds identification has been one of the engines to boost this progress. However, these t...
Article
Nowadays, several studies in the field of deep learning in agriculture obtain high performances in weeds identification by fine-tuning neural networks, previously trained on general-purpose datasets containing images unrelated to agriculture. This work examines whether these achievements could be further improved by fine-tuning neural networks pre-...
Article
Reducing the use of pesticides through selective spraying is an important component towards a more sustainable computer-assisted agriculture. Weed identification at early growth stage contributes to reduced herbicide rates. However, while computer vision alongside deep learning have overcome the performance of approaches that use hand-crafted featu...
Article
Full-text available
This article presents key technological advances in the digital agriculture, which will have significant impact. Artificial intelligence-based techniques, together with big data analytics, address the challenges of agricultural production in terms of productivity and sustainability. Emerging new applications will transform agriculture from the trad...
Article
In the European Union, production standards in the form of legal regulations play an important role in farming. Because of the increasing amount of regulations, it is desirable to transform human-oriented regulations into a set of computer-oriented rules to provide decision support through the Farm Management Information System. To obtain the logic...
Article
Pests in crops produce important economic loses all around the world. To deal with them without damaging people or the environment, governments have established strict legislation and norms describing the products and procedures of use. However, since these norms frequently change to reflect scientific and technological advances, it is needed to pe...
Article
Currently, pest management practices require modern equipment and the use of complex information, such as regulations and guidelines. The complexity of regulations is the root cause of the emergence of automated solutions for compliance assessment by translating regulations into sets of machine-processable rules that can be run by specialized modul...
Article
Full-text available
Geospatial data catalogs enable users to discover and access geographical information. Prevailing solutions are document oriented and fragment the spatial continuum of the geospatial data into independent and disconnected resources described through metadata. Due to this, the complete answer for a query may be scattered across multiple resources, m...
Article
This work evaluates and compares different supervised learning algorithms using a costsensitive approach to find a model that classifies legal rules related to pesticides as prohibitions and permissions. The naive Bayes classifier achieves the best results and it would be applicable because it doesn't misclassify prohibitions as permissions.

Network

Cited By