Development of a robotic structure for acquisition and classification of images (ERACI) in sugarcane crops

José Ricardo Ferreira Cardoso, Carlos Eduardo Angeli Furlani, José Eduardo Pitelli Turco, Cristiano Zerbato, Franciele Morlin Carneiro, Francisca Nivanda de Lima Estevam


Digital agriculture contributes to agricultural efficiency through the use of such tools as computer vision, robotics, and precision agriculture. In this study, the objective was to develop a system capable of classifying images through the recognition of pre-established patterns. For this purpose, a geographically distributed system was created, based on the Raspberry Pi 3B+ computer, which captures images in the field and stores them in a database, where they are available to receive a pre-classification by a supervisor. Subsequently, classifiers are generated, evaluated, and sent to the remote device to conduct a classification in real time. For an evaluation of the system, 23 classes were defined and grouped into 3 superclasses, 36,979 images were captured, and 1,579 pre-classifications were conducted, which allowed the classification tests to be carried out by means of a cross-validation by randomly dividing into the equivalent number of classes. These tests revealed that the accuracy delivered by each classifier is different and directly proportional to the quantity and balance of the samples, with a variation of 11% to 79%, with 26 and 2,200 samples considered, respectively. The response time of the system was evaluated during 1,585 periods and was maintained within approximately 0.20 s, and under controlled speed of the vehicle, can be used for the dispersion of inputs in real time.


Digital Agriculture; Machine Learning; Open source; Raspberry Pi; Computer Vision

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Alvares, C. A. et al. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, Schweizerbart Science Publishers, Stuttgart, Germany, v. 22, n. 6, p. 711–728, 2013. Retrieved From: .

Bernardes, M. S.; Belardo, G. D. C. Espaçamentos de plantio: Espaçamentos para a cultura de cana-de-açúcar: Manejo nutricional da cultura da cana-de-açúcar. In: Processos Agrícolas e Mecanização da Cana-de-Açúcar. [S.l.: s.n.] 2015, 1. Ed. Cassia, Belardo Marcelo Tufaile, R. P. d. S. Guilherme De C. p. 243-258.

Cardoso, J. R. F. Desenvolvimento de estrutura robótica para aquisição e classificação de imagens (ERACI) de lavoura de cana-de-açúcar. Dissertação (Mestrado) - Universidade Estadual de Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, SP, Brasil. 2020. Retrieved From: <>.

Castro, S. G. Q. d. Manejo da adubação nitrogenada em cana-de-açúcar e diagnose por meio de sensores de dossel. Tese (Doutorado) — Universidade Estadual de Campinas, Faculdade de Engenharia Agrícola, Campinas, SP, Campinas, Brasil 2016. Retrieved From: .

CLIMATE-DATA. Barretos clima (Brasil). Accessed: 2020 October 04. Retrieved From: .

CONAB. Companhia Nacional de Abastecimento. Acompanhamento da Safra brasileira de cana-de-açúcar: Safra 2019/20. Accessed: 2020 October 04. Retrieved from: .

Gimenez, L. M.; Molin, J. P. Agricultura de Precisão Sob a Perspectiva de Seus Diversos Atores. Informações Agronômicas, International Plant Nutrition Institute - IPNI Brasil, 2018.

Gonzalez, R. C.; Woods, R. E. Processamento digital de imagens. 3. In: Brasil: Pearson Education do Brasil. 2010. ed. São Paulo, S. P.

Hao, Z. et al. Research on driver fatigue detection method based on parallel convolution neural network 2019. In: IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). [S.l.: s.n.], 2019. p. 164-168.

Hu, R.;, Jia, W.; Ling, H.; Huang, D. Multiscale distance matrix for fast plant leaf recognition. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, v. 21, n. 11, p. 4667-4672, 2012.

INPE. AMBDATA 2020. Variáveis Ambientais para modelagem de distribuição de Espécies. Mapa de solos. Accessed: 2020 October 04. Retrieved from: .

Landge, I. A.; Satopay, H. “Secured IoT through hashing using MD5,” 2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, 2018. p. 1-5, doi: 10.1109/AEEICB.2018.8481007.

Liski, E. et al. Modeling the productivity of mechanized CTL harvesting with statistical machine learning methods. International Journal of Forest Engineering, 1-10, doi: 10.1080/14942119.2020.1820750, 2020, 2020.

Luger, G. Inteligência artificial. 6. In: Brasil: Pearson Education do Brasil. 2013. ed. São Paulo, S. P.

Kang, E.; Oh, I. Weak constraint leaf image recognition based on convolutional neural network 2018. In: International Conference on Electronics, Information, and Communication (ICEIC). [S.l.: Sn], 2018. p. 1-4.

Khmag, A.; Al-Haddad, S. A. R.; Kamarudin, N. Recognition system for leaf images based on its leaf contour and centroid. In: IEEE 2017 15th Student Conference on Research and Development (SCOReD). [S.l.: s.n.], 2017. p. 467-472.

Mishra, P. K.; Dhar, S.; Kalra, M. K. Landslide detection system using computer vision approach and raspberry pi 2019. In: International Conference on Communication and Electronics Systems (ICCES). [S.l.: s.n.], 2019. p. 1201-1206.

Osroosh, Y.; Khot, L. R.; Peters, R. T. Economical thermal-rgb imaging system for monitoring agricultural crops. Computers and Electronics in Agriculture, v. 147, p. 34-43, 2018. ISSN 0168-1699. Retrieved From: .

Resende, A. V. D.; Coelho, A. M. Amostragem para mapeamento e manejo da fertilidade do solo na abordagem de agricultura de precisão. Informações Agronômicas, Piracicaba, v. 1, n. 159, p. 1-8, set. 2017.

Rustogi, R.; Prasad, A. Swift imbalance data classification using smote and extreme learning machine 2019. In: International Conference on Computational Intelligence in Data Science (ICCIDS). [S.l.: s.n.], 2019. p. 1-6.

Sahu, B. K. et al Development of hardware setup of an autonomous robotic vehicle based on computer vision using raspberry pi. In: Innovations in Power and Advanced Computing Technologies (i-PACT). [S.l.: s.n.], v. 1, p. 1-5, 2019.

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