Detección de enfermedades de la fresa en agricultura de precisión

La detección de enfermedades de los cultivos en la agricultura de precisión tiene un impacto importante en la agricultura, mejorando la producción y reduciendo las pérdidas económicas. Es por esto que se han hecho algunos esfuerzos en esta dirección. Este artículo compara 4 algoritmos de detección d...

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Autores:: Aguirre Rojas, Daniel Santiago

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2021

Institución:: Universidad Militar Nueva Granada

Repositorio:: Repositorio UMNG

Idioma:: spa

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dc.title.spa.fl_str_mv	Detección de enfermedades de la fresa en agricultura de precisión
dc.title.translated.spa.fl_str_mv	Strawberry disease detection in precision agriculture
title	Detección de enfermedades de la fresa en agricultura de precisión
spellingShingle	Detección de enfermedades de la fresa en agricultura de precisión AGRICULTURA DE PRECISION PLANTAS-DAÑOS Y LESIONES FRESAS - CULTIVO Precision agriculture Object detection Deep learning Crops disease Strawberry crops Agricultura de precision detección de objetos Aprendizaje profundo enfermedades en cultivos enfermedades en fresa
title_short	Detección de enfermedades de la fresa en agricultura de precisión
title_full	Detección de enfermedades de la fresa en agricultura de precisión
title_fullStr	Detección de enfermedades de la fresa en agricultura de precisión
title_full_unstemmed	Detección de enfermedades de la fresa en agricultura de precisión
title_sort	Detección de enfermedades de la fresa en agricultura de precisión
dc.creator.fl_str_mv	Aguirre Rojas, Daniel Santiago
dc.contributor.advisor.none.fl_str_mv	Solaque, Leonardo Velasco, Alexandra
dc.contributor.author.none.fl_str_mv	Aguirre Rojas, Daniel Santiago
dc.subject.lemb.spa.fl_str_mv	AGRICULTURA DE PRECISION PLANTAS-DAÑOS Y LESIONES FRESAS - CULTIVO
topic	AGRICULTURA DE PRECISION PLANTAS-DAÑOS Y LESIONES FRESAS - CULTIVO Precision agriculture Object detection Deep learning Crops disease Strawberry crops Agricultura de precision detección de objetos Aprendizaje profundo enfermedades en cultivos enfermedades en fresa
dc.subject.keywords.spa.fl_str_mv	Precision agriculture Object detection Deep learning Crops disease Strawberry crops
dc.subject.proposal.spa.fl_str_mv	Agricultura de precision detección de objetos Aprendizaje profundo enfermedades en cultivos enfermedades en fresa
description	La detección de enfermedades de los cultivos en la agricultura de precisión tiene un impacto importante en la agricultura, mejorando la producción y reduciendo las pérdidas económicas. Es por esto que se han hecho algunos esfuerzos en esta dirección. Este artículo compara 4 algoritmos de detección de objetos basados en Deep Learning para detectar enfermedades en cultivos de fresa. Aquí, presentamos un avance hacia la detección de las enfermedades más comunes presentes en la fresa para evitar pérdidas económicas. El objetivo principal es detectar tres enfermedades de los cultivos de fresa. Botrytis cinerea, quemaduras en las hojas y Oídio, para tomar medidas adicionales si los cultivos no son saludables. Hemos elegido estas tres enfermedades porque son problemas frecuentes e impredecibles, y el riesgo de que se generen es alto. Para ello, entrenamos cuatro algoritmos, dos basados en Single Shot MultiBox Detector y dos basados en el algoritmo EfficientDet. Centramos el análisis en los dos mejores resultados en función de la precisión media media. Usamos Google Colab para el entrenamiento, luego se usaron una computadora central Core i5 y una Nvidia Jetson nano para las pruebas. Hemos conseguido una red de detección con una precisión media media del 81% en el mejor de los casos, en la detección de las tres clases propuestas. Al usar una NVIDIA Jetson nano, la precisión aumenta hasta un 86% debido a la GPU dedicada que procesa redes neuronales convolucionales (CNN).
publishDate	2021
dc.date.issued.none.fl_str_mv	2021-10-06
dc.date.accessioned.none.fl_str_mv	2022-04-18T16:55:08Z
dc.date.available.none.fl_str_mv	2022-04-18T16:55:08Z
dc.type.local.spa.fl_str_mv	Tesis/Trabajo de grado - Monografía - Pregrado
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dc.relation.references.spa.fl_str_mv	Carisse, O. and Fall, M. (2021). Decision trees to forecast risks of strawberry powdery mildew caused by podosphaera aphanis. Agriculture (Switzerland), 11(1):1–16. c Chen, J., Yin, H., and Zhang, D. (2020). A self-adaptive classification method for plant disease detection using gmdh-logistic model. Sustainable Computing: Informatics and Systems, 28:100415. Chouhan, S., Singh, D. U., Sharma, U., and Jain, S. (2020). Leaf disease segmentation and classification of jatropha curcas l. and pongamia pinnata l. biofuel plants using computer vision based approaches. Measurement, 171. Ciaparrone, G., Sanchez, F. L., Tabik, S., Troiano, L., Tagli- ´ aferri, R., and Herrera, F. (2020). Deep learning in video multi-object tracking: A survey. Neurocomputing, 381:61 – 88. Girshick, R. (2015). Fast r-cnn. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 1440– 1448. Girshick, R. B., Donahue, J., Darrell, T., and Malik, J. (2013). Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR, abs/1311.2524. Hancock, J., Sjulin, T., and Lobos, G. (2008). Strawberries, volume 9781402069079. He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. CoRR, abs/1512.03385. Huang, J.-D., Wang, C.-F., Lian, C.-L., Huang, M.-Y., Zhang, C., and Liu, J.-Q. (2020). Isolation and identification of five new diterpenoids from jatropha curcas. Phytochemistry Letters, 40:37–41. Klerkx, L., Jakku, E., and Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS - Wageningen Journal of Life Sciences, 90-91:100315. Lezoche, M., Hernandez, J. E., del Mar Eva Alemany D´ıaz, M., Panetto, H., and Kacprzyk, J. (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Computers in Industry, 117:103187. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S. E., Fu, C., and Berg, A. C. (2015). SSD: single shot multibox detector. CoRR, abs/1512.02325. Mathew, D., Sathish Kumar, C., and Anita Cherian, K. (2020). Foliar fungal disease classification in banana plants using elliptical local binary pattern on multiresolution dual tree complex wavelet transform domain. Information Processing in Agriculture. Mojjada, R. K., Kiran Kumar, K., Yadav, A., and Satya Vara Prasad, B. (2020). Detection of plant leaf disease using digital image processing. Materials Today: Proceedings Park, H., Eun, J., and Kim, S. (2017). Image-based disease diagnosing and predicting of the crops through the deep learning mechanism. In 2017 International Conference on Information and Communication Technology Convergence (ICTC), pages 129–131. Petrasch, S., Knapp, S. J., van Kan, J. A. L., and BlancoUlate, B. (2019). Grey mould of strawberry, a devastating disease caused by the ubiquitous necrotrophic fungal pathogenBotrytis cinerea. Molecular Plant Pathology, 20(6):877–892. Prakash, R. M., Saraswathy, G. P., Ramalakshmi, G., Mangaleswari, K. H., and Kaviya, T. (2017). Detection of leaf diseases and classification using digital image processing. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pages 1–4. Priya, R. and Ramesh, D. (2020). Ml based sustainable precision agriculture: A future generation perspective. Sustainable Computing: Informatics and Systems, 28:100439. Perez-Borrero, I., Mar ´ ´ın-Santos, D., Gegundez-Arias, ´ M. E., and Cortes-Ancos, E. (2020). A fast and ac- ´ curate deep learning method for strawberry instance segmentation. Computers and Electronics in Agriculture, 178:105736. Redmon, J. and Farhadi, A. (2017). Yolo9000: Better, faster, stronger. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6517–6525. Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems 28, pages 91–99. Curran Associates, Inc. Santiago., A., Solaque., L., and Velasco., A. (2020). Deep learning algorithm for object detection with depth measurement in precision agriculture. In Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,, pages 490–497. INSTICC, SciTePress. Sheikh, M. H., Mim, T. T., Reza, M. S., and Hena, M. H. (2019). Leaf diseases detection for commercial cultivation of obsolete fruit in bangladesh using image processing system. In 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), pages 271–275. Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., and Batra, N. (2019). Plantdoc: A dataset for visual plant disease detection. CoRR, abs/1911.10317. Srivastava, K., Bhutoria, A. J., Sharma, J. K., Sinha, A., and Pandey, P. C. (2019). Uavs technology for the development of gui based application for precision agriculture and environmental research. Remote Sensing Applications: Society and Environment, 16:100258. Tan, M. and Le, Q. V. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. CoRR, abs/1905.11946. Tan, M., Pang, R., and Le, Q. V. (2020). Efficientdet: Scalable and efficient object detection. Torky, M. and Hassanein, A. E. (2020). Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges. Computers and Electronics in Agriculture, 178:105476. Vanti, G., Leshem, Y., and Masaphy, S. (2021). Resistance response enhancement and reduction of botrytis cinerea infection in strawberry fruit by morchella conica mycelial extract. Postharvest Biology and Technology, 175 Wang, L., Fan, X., Chen, J., Cheng, J., Tan, J., and Ma, X. (2020). 3d object detection based on sparse convolution neural network and feature fusion for autonomous driving in smart cities. Sustainable Cities and Society, 54:102002. Wu, X., Sahoo, D., and Hoi, S. C. (2020). Recent advances in deep learning for object detection. Neurocomputing. Umit Atila, Uc¸ar, M., Akyol, K., and Uc¸ar, E. (2021). ¨ Plant leaf disease classification using efficientnet deep learning model. Ecological Informatics, 61:101182.
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spelling	Solaque, LeonardoVelasco, AlexandraAguirre Rojas, Daniel SantiagoIngeniero en Mecatrónica2022-04-18T16:55:08Z2022-04-18T16:55:08Z2021-10-06http://hdl.handle.net/10654/40519instname:Universidad Militar Nueva Granadareponame:Repositorio Institucional Universidad Militar Nueva Granadarepourl:https://repository.unimilitar.edu.coLa detección de enfermedades de los cultivos en la agricultura de precisión tiene un impacto importante en la agricultura, mejorando la producción y reduciendo las pérdidas económicas. Es por esto que se han hecho algunos esfuerzos en esta dirección. Este artículo compara 4 algoritmos de detección de objetos basados en Deep Learning para detectar enfermedades en cultivos de fresa. Aquí, presentamos un avance hacia la detección de las enfermedades más comunes presentes en la fresa para evitar pérdidas económicas. El objetivo principal es detectar tres enfermedades de los cultivos de fresa. Botrytis cinerea, quemaduras en las hojas y Oídio, para tomar medidas adicionales si los cultivos no son saludables. Hemos elegido estas tres enfermedades porque son problemas frecuentes e impredecibles, y el riesgo de que se generen es alto. Para ello, entrenamos cuatro algoritmos, dos basados en Single Shot MultiBox Detector y dos basados en el algoritmo EfficientDet. Centramos el análisis en los dos mejores resultados en función de la precisión media media. Usamos Google Colab para el entrenamiento, luego se usaron una computadora central Core i5 y una Nvidia Jetson nano para las pruebas. Hemos conseguido una red de detección con una precisión media media del 81% en el mejor de los casos, en la detección de las tres clases propuestas. Al usar una NVIDIA Jetson nano, la precisión aumenta hasta un 86% debido a la GPU dedicada que procesa redes neuronales convolucionales (CNN).Crop disease detection in precision agriculture has an important impact on farming, improving production, and reducing economic losses. This is why some efforts have been done in this direction. This paper compares 4 object detection algorithms based on deep learning to detect diseases in strawberry crops. Here, we present a step towards detecting the most common diseases to prevent economical losses. The main purpose is to detect mainly three diseases of the strawberry crops, i.e. Botrytis cinerea, Leaf scorch, and Powdery mildew, to take further actions if the crops are unhealthy. We have chosen these three diseases because these are frequent and unpredictable issues, and the risk of infection is high. For this, we trained four algorithms, two based on Single Shot MultiBox Detector and two based on EfficientDet algorithm. We focus the analysis on the two best results based on the mean average precision. We have used Google colab for training, then a Core i5 host computer and an Nvidia Jetson nano were used for testing. We have achieved a detection network with a mean average precision of 81% in the best case, in detecting the three proposed classes. While using an NVIDIA Jetson nano, the accuracy increases up to 86% due to the dedicated GPU that processes Convolutional Neural Networks(CNN).Pregradoapplicaction/pdfspahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 InternationalAcceso abiertoDetección de enfermedades de la fresa en agricultura de precisiónStrawberry disease detection in precision agricultureAGRICULTURA DE PRECISIONPLANTAS-DAÑOS Y LESIONESFRESAS - CULTIVOPrecision agricultureObject detectionDeep learningCrops diseaseStrawberry cropsAgricultura de precisiondetección de objetosAprendizaje profundoenfermedades en cultivosenfermedades en fresaTesis/Trabajo de grado - Monografía - Pregradoinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fIngeniería en MecatrónicaFacultad de IngenieríaUniversidad Militar Nueva GranadaCarisse, O. and Fall, M. (2021). Decision trees to forecast risks of strawberry powdery mildew caused by podosphaera aphanis. Agriculture (Switzerland), 11(1):1–16. cChen, J., Yin, H., and Zhang, D. (2020). A self-adaptive classification method for plant disease detection using gmdh-logistic model. Sustainable Computing: Informatics and Systems, 28:100415.Chouhan, S., Singh, D. U., Sharma, U., and Jain, S. (2020). Leaf disease segmentation and classification of jatropha curcas l. and pongamia pinnata l. biofuel plants using computer vision based approaches. Measurement, 171.Ciaparrone, G., Sanchez, F. L., Tabik, S., Troiano, L., Tagli- ´ aferri, R., and Herrera, F. (2020). Deep learning in video multi-object tracking: A survey. Neurocomputing, 381:61 – 88.Girshick, R. (2015). Fast r-cnn. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 1440– 1448.Girshick, R. B., Donahue, J., Darrell, T., and Malik, J. (2013). Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR, abs/1311.2524.Hancock, J., Sjulin, T., and Lobos, G. (2008). Strawberries, volume 9781402069079.He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. CoRR, abs/1512.03385.Huang, J.-D., Wang, C.-F., Lian, C.-L., Huang, M.-Y., Zhang, C., and Liu, J.-Q. (2020). Isolation and identification of five new diterpenoids from jatropha curcas. Phytochemistry Letters, 40:37–41.Klerkx, L., Jakku, E., and Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS - Wageningen Journal of Life Sciences, 90-91:100315.Lezoche, M., Hernandez, J. E., del Mar Eva Alemany D´ıaz, M., Panetto, H., and Kacprzyk, J. (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Computers in Industry, 117:103187.Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S. E., Fu, C., and Berg, A. C. (2015). SSD: single shot multibox detector. CoRR, abs/1512.02325.Mathew, D., Sathish Kumar, C., and Anita Cherian, K. (2020). Foliar fungal disease classification in banana plants using elliptical local binary pattern on multiresolution dual tree complex wavelet transform domain. Information Processing in Agriculture.Mojjada, R. K., Kiran Kumar, K., Yadav, A., and Satya Vara Prasad, B. (2020). Detection of plant leaf disease using digital image processing. Materials Today: ProceedingsPark, H., Eun, J., and Kim, S. (2017). Image-based disease diagnosing and predicting of the crops through the deep learning mechanism. In 2017 International Conference on Information and Communication Technology Convergence (ICTC), pages 129–131.Petrasch, S., Knapp, S. J., van Kan, J. A. L., and BlancoUlate, B. (2019). Grey mould of strawberry, a devastating disease caused by the ubiquitous necrotrophic fungal pathogenBotrytis cinerea. Molecular Plant Pathology, 20(6):877–892.Prakash, R. M., Saraswathy, G. P., Ramalakshmi, G., Mangaleswari, K. H., and Kaviya, T. (2017). Detection of leaf diseases and classification using digital image processing. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pages 1–4.Priya, R. and Ramesh, D. (2020). Ml based sustainable precision agriculture: A future generation perspective. Sustainable Computing: Informatics and Systems, 28:100439.Perez-Borrero, I., Mar ´ ´ın-Santos, D., Gegundez-Arias, ´ M. E., and Cortes-Ancos, E. (2020). A fast and ac- ´ curate deep learning method for strawberry instance segmentation. Computers and Electronics in Agriculture, 178:105736.Redmon, J. and Farhadi, A. (2017). Yolo9000: Better, faster, stronger. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6517–6525.Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems 28, pages 91–99. Curran Associates, Inc.Santiago., A., Solaque., L., and Velasco., A. (2020). Deep learning algorithm for object detection with depth measurement in precision agriculture. In Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,, pages 490–497. INSTICC, SciTePress.Sheikh, M. H., Mim, T. T., Reza, M. S., and Hena, M. H. (2019). Leaf diseases detection for commercial cultivation of obsolete fruit in bangladesh using image processing system. In 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), pages 271–275.Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., and Batra, N. (2019). Plantdoc: A dataset for visual plant disease detection. CoRR, abs/1911.10317.Srivastava, K., Bhutoria, A. J., Sharma, J. K., Sinha, A., and Pandey, P. C. (2019). Uavs technology for the development of gui based application for precision agriculture and environmental research. Remote Sensing Applications: Society and Environment, 16:100258.Tan, M. and Le, Q. V. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. CoRR, abs/1905.11946.Tan, M., Pang, R., and Le, Q. V. (2020). Efficientdet: Scalable and efficient object detection.Torky, M. and Hassanein, A. E. (2020). Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges. Computers and Electronics in Agriculture, 178:105476.Vanti, G., Leshem, Y., and Masaphy, S. (2021). Resistance response enhancement and reduction of botrytis cinerea infection in strawberry fruit by morchella conica mycelial extract. Postharvest Biology and Technology, 175Wang, L., Fan, X., Chen, J., Cheng, J., Tan, J., and Ma, X. (2020). 3d object detection based on sparse convolution neural network and feature fusion for autonomous driving in smart cities. Sustainable Cities and Society, 54:102002.Wu, X., Sahoo, D., and Hoi, S. C. (2020). Recent advances in deep learning for object detection. Neurocomputing.Umit Atila, Uc¸ar, M., Akyol, K., and Uc¸ar, E. (2021). ¨ Plant leaf disease classification using efficientnet deep learning model. Ecological Informatics, 61:101182.Calle 100ORIGINALAguirreRojasDanielSantiago2021.pdfAguirreRojasDanielSantiago2021.pdfArtículoapplication/pdf8185191http://repository.unimilitar.edu.co/bitstream/10654/40519/3/AguirreRojasDanielSantiago2021.pdfa974bc5e0dafb15b13674062f470964dMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-83420http://repository.unimilitar.edu.co/bitstream/10654/40519/4/license.txta609d7e369577f685ce98c66b903b91bMD54THUMBNAILAguirreRojasDanielSantiago2021.pdf.jpgAguirreRojasDanielSantiago2021.pdf.jpgIM Thumbnailimage/jpeg5947http://repository.unimilitar.edu.co/bitstream/10654/40519/5/AguirreRojasDanielSantiago2021.pdf.jpgff9be04c6d88853cc2e779fc65ad80f3MD5510654/40519oai:repository.unimilitar.edu.co:10654/405192022-04-19 01:03:47.823Repositorio Institucional 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