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Author |
Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon |
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Title |
Open Source system for identification of corn leaf chlorophyll contents based on multispectral images |
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2019 |
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International Conference on Applied Technologies (ICAT 2019); Quito, Ecuador |
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572-581 |
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It is important for farmers to know the level of chlorophyll in plants since this depends on the treatment they should give to their crops. There are two common classic methods to get chlorophyll values: from laboratory analysis and electronic devices. Both methods obtain the chlorophyll level of one sample at a time, although they can be destructive. The objective of this research is to develop a system that allows obtaining the chlorophyll level of plants using images.
Python programming language and different libraries of that language were used to develop the solution. It was decided to implement an image labeling module, a simple linear regression and a prediction module. The first module was used to create a database that links the values of the images with those of chlorophyll, which was then used to obtain linear regression in order to determine the relationship between these variables. Finally, the linear
regression was used in the prediction system to obtain chlorophyll values from the images. The linear regression was trained with 92 images, obtaining a root-mean-square error of 7.27 SPAD units. While the testing was perform using 10 values getting a maximum error of 15.5%.
It is concluded that the system is appropriate for chlorophyll contents identification of corn leaves in field tests.
However, it can also be adapted for other measurement and crops. The system can be downloaded at github.com/JoeSvr95/NDVI-Checking [1]. |
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116 |
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Luis Jacome-Galarza, Monica Villavicencio-Cabezas, Miguel Realpe-Robalino, Jose Benavides-Maldonado |
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Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. |
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2021 |
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19th LACCEI International Multi-Conference for Engineering, Education, and Technology |
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processing, software engineering, deep learning, intelligent vision systems, cloud computing. |
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Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field
of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most
utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems.
Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud. |
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English |
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154 |
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Pabelco Zambrano, Fernanda Calderon, Héctor Villegas, Jonathan Paillacho, Doménica Pazmiño, Miguel Realpe |
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UAV Remote Sensing applications and current trends in crop monitoring and diagnostics: A Systematic Literature Review |
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2023 |
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IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) 2023, 4-7 julio 2023 |
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979-835033337-4 |
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214 |
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Sara Nieto, Evelyn Mejia, Ricardo Villacis, Fernanda Calderon, Hector Villegas, Jonathan Paillacho and Miguel Realpe |
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A Practical Study on Banana (Musa spp.) Plant Counting and Coverage Percentage Using Remote Sensing and Deep Learning |
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2024 |
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Lecture Notes in Geoinformation and Cartography: 3rd International Conference on Geospatial Information Sciences, iGISc 2023 Ciudad de México 14-17 November 2023 |
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147 - 158 |
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cidis @ cidis @ |
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228 |
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Henry O. Velesaca , Miguel Realpe, Angel D. Sappa & Alice Gomez |
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Title |
Analysis of Hidden Patterns in Road Accident Dataset using Clustering Techniques |
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2024 |
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SmartTech-IC 2024 4th International Conference on Smart Technologies, Systems and Applications |
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cidis @ cidis @ |
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257 |
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