Small Plants Detection and Recognition using Machine Learning

Authors

  • Thales Santos Verne UNOESTE
  • Francisco Assis da Silva UNOESTE
  • Leandro Luiz de Almeida
  • Danillo Roberto Pereira
  • Almir Olivette Artero

Keywords:

CNN, Plants Detection and Recognition, Neural Network, Machine Learning

Abstract

The detection and recognition of plants has always been a difficult task even for connoisseurs and scholars, due to the vast variety of plants found around the world. With the advancement of technology, it has become possible to solve this problem computationally. In this paper, a method is presented to perform plant detection and recognition from images using computer vision and artificial intelligence algorithms. The results show that the computational cost and recognition rate were satisfactory for use in controlled environments. The processing time to recognize each plant was 375 milliseconds, with an accuracy of 92%.

Downloads

Download data is not yet available.

References

AGARAP, A. F. M. Deep Learning using Rectified Linear Units (ReLU). 2018. Disponível em: https://arxiv.org/pdf/1803.08375.pdf. Acesso em: 15 junho 2021.

AMIDI, A.; AMIDI, S. Convolutional Neural Networks cheatsheet. Disponível em: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks. Acesso em: 20 junho 2021.

ALBAWI, S.; MOHAMMED, T. A.; AL-ZAWI, S. Understanding of a convolutional neural network. 2017. In: INTERNATIONAL CONFERENCE ON ENGINEERING AND TECHNOLOGY (ICET). DOI: 10.1109/ICEngTechnol.2017.8308186.

DEEPAI. What is Stride (Machine Learning)?. Disponível em: https://deepai.org/machine-learning-glossary-and-terms/stride#:~:text=Stride%20is%20a%20parameter%20of,or%20unit%2C%20at%20a%20time. Acesso em: 15 junho 2021.

GOODFELLOW, I.; BENGIO, Y.; COURVILLE, A. Deep learning. 2016. DOI: https://doi.org/10.4258/hir.2016.22.4.351. Disponível em: https://e-hir.org/journal/view.php?id=10.4258/hir.2016.22.4.351. Acesso em: 15 junho 2021.

HANG, J.; ZHANG, D.; CHEN, P.; ZHANG, J.; WANG, B. Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network. 2019. DOI: https://doi.org/10.3390/s19194161. Disponível em https://www.mdpi.com/1424-8220/19/19/4161. Acesso em: 15 junho 2021.

INOVA. Agência de inovação da UNICAMP. 2017. Deep learning é tecnologia de aprendizado de máquina que mais cresce em todo o mundo. Disponível em: https://www.inova.unicamp.br/noticia/deep-learning-e-tecnologia-de-aprendizado-de-maquina-que-mais-cresce-em-todo-o-mundo. Acesso em: 15 junho 2021.

JAMIL, N; HUSSIN, N. A. C.; NORDIN, S.; AWANG, K. Automatic Plant Identification: Is Shape the Key Feature?. 2015. In: IEEE INTERNATIONAL SYMPOSIUM ON ROBOTICS AND INTELLIGENT SENSORS (IEEE IRIS2015). DOI: https://doi.org/10.1016/j.procs.2015.12.287. Disponível em: https://www.sciencedirect.com/science/article/pii/S1877050915037886. Acesso em: 10 junho 2021.

JOLY, A.; GOËAU, H.; BONNET, P.; BAKIĆ, V.; BARBE, J.; SELMI, S.; YAHIAOUI, I.; CARRÉ, J.; MOUYSSET, E.; MOLINO, J.; BOUJEMAA, N.; BARTHÉLÉMY, D. Interactive plant identification based on social image data, 2014, Disponível em: https://www.sciencedirect.com/science/article/pii/S157495411300071X. Acesso em: 15 junho 2021.

KERAS. 2021. Disponível em: https://keras.io. Acesso: 15 junho 2021.

KRIZHEVSKY, A.; SUTSKEVER, I.; HINTON, G. E. ImageNet Classification with Deep Convolutional Neural Networks. 2017. DOI: https://doi.org/10.1145/3065386. Disponível em: https://dl.acm.org/doi/pdf/10.1145/3065386. Acesso em: 10 junho 2021.

LI, Y. A Convergence Analysis of Two-layer Neural Networks with ReLU Activation. 2017. Disponível em: https://arxiv.org/pdf/1705.09886.pdf. Acesso em: 10 junho 2021.

MARTINS, J. P. Notas Sobre Redes Neurais Convolucionais. 2020. Disponível em: https://jpvmm.github.io/first-post.html. Acesso em: 20 junho 2021.

RICHARDSON, E.; WEISS, Y. On GANs and GMMs. 2019. Disponível em https://arxiv.org/abs/1805.12462. Acesso em: 23 junho 2021.

SHORTEN, C.; KHOSHGOFTAAR, T. A survey on Image Data Augmentation for Deep Learning. 2019. DOI: https://doi.org/10.1186/s40537-019-0197-0. Disponível em: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0197-0. Acesso em: 15 junho 2021.

TENSORFLOW. 2021. Disponível em: https://www.tensorflow.org. Acesso em: 15 junho 2021.

ULINDALA, P. R. Convolutional Neural Networks (CNN’s) — A practical perspective. 2020. Towards data science. Disponível em: https://towardsdatascience.com/convolutional-neural-networks-cnns-a-practical-perspective-c7b3b2091aa8. Acesso em: 24 junho 2021.

YALCIN, H.; RAZAVI, S. Plant Classification using Convolutional Neural Networks, 2016. In: FIFTH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS). DOI: https://doi.org/10.1109/Agro-Geoinformatics.2016.7577698. Disponível em: https://ieeexplore.ieee.org/document/7577698. Acesso em: 10 junho 2021.

ZHANG, N.; LIU, W. Plant leaf recognition method based on clonal selection algorithm and K nearest neighbor. Journal of Computer Applications, 2013.

Downloads

Published

2022-05-04

Issue

Vol. 14 No. 1 (2022): Colloquium Exactarum, Vol.14 N.1 Publicação Contínua (Continuous Publishing)

Section

Artigos Originais

How to Cite

Small Plants Detection and Recognition using Machine Learning. (2022). Colloquium Exactarum. ISSN: 2178-8332, 14(1), 36-45. https://journal.unoeste.br/index.php/ce/article/view/4099

Similar Articles

21-30 of 53

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

1 2 3 4 > >>