Redes neuronales convolucionales para la clasificación de la mancha negra en los cítricos
DOI:
https://doi.org/10.71068/4x9sw526Palabras clave:
Visión artificial, Redes neuronales convolucionales, Mancha negra, Inteligencia artificialResumen
Se presenta un innovador modelo de visión artificial basado en redes neuronales convolucionales (CNN) para la clasificación de la mancha negra en los cítricos. Este estudio adopta una metodología que fusiona Investigación y Desarrollo con principios ágiles de Scrum. La evaluación comparativa con los modelos existentes de clasificación de cítricos en diferentes contextos demuestra que nuestro modelo muestra diferencias significativas en la precisión de clasificación respecto a los modelos B y C. El análisis estadístico, incluyendo la prueba de McNemar, confirma la eficacia del modelo, resaltando su fiabilidad y competitividad en la detección de enfermedades en cítricos. Los resultados obtenidos no solo proporcionan un modelo eficiente para la clasificación de la mancha negra en los cítricos, sino que también promueven el avance en la aplicación de la inteligencia artificial en la agricultura. Este enfoque sugiere nuevas direcciones de investigación y subraya la importancia de la visión artificial en la mejora de la salud de los cultivos. La implementación de este modelo puede reducir pérdidas económicas y optimizar la productividad, aportando beneficios significativos tanto para los agricultores como para la industria agrícola.
Referencias
Almeyda, E., & Ipanaqué, W. (2022). Recent Developments of Artificial Intelligence for Banana: Application Areas, Learning Algorithms, and Future Challenges. Engenharia Agricola, 42(SpecialIssue). https://doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210144/2022 DOI: https://doi.org/10.1590/1809-4430-eng.agric.v42nepe20210144/2022
Bai, X., Wang, X., Liu, X., Liu, Q., Song, J., Sebe, N., & Kim, B. (2021). Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments. Pattern Recognition, 120, 108102. https://doi.org/10.1016/j.patcog.2021.108102 DOI: https://doi.org/10.1016/j.patcog.2021.108102
Benallal, M. A., & Tayeb, M. S. (2023). An image-based convolutional neural network system for road defects detection. IAES International Journal of Artificial Intelligence, 12(2). https://doi.org/10.11591/ijai.v12.i2.pp577-584 DOI: https://doi.org/10.11591/ijai.v12.i2.pp577-584
Booij, T. M., Chiscop, I., Meeuwissen, E., Moustafa, N., & Hartog, F. T. H. den. (2022). ToN_IoT: The Role of Heterogeneity and the Need for Standardization of Features and Attack Types in IoT Network Intrusion Data Sets. IEEE Internet of Things Journal, 9(1), 485–496. https://doi.org/10.1109/JIOT.2021.3085194 DOI: https://doi.org/10.1109/JIOT.2021.3085194
Carstens, E., Linde, C. C., Slabbert, R., Miles, A. K., Donovan, N. J., Li, H., Zhang, K., Dewdney, M. M., Rollins, J. A., Glienke, C., Schutte, G. C., Fourie, P. H., & McLeod, A. (2017). A Global Perspective on the Population Structure and Reproductive System of Phyllosticta citricarpa. Phytopathology®, 107(6), 758–768. https://doi.org/10.1094/PHYTO-08-16-0292-R DOI: https://doi.org/10.1094/PHYTO-08-16-0292-R
Chaniago, M. B., & Rahma, Y. D. (2021). Implementation of Pest Detection Based on International Technological Education in Orange Plants Using Neural Network and Svm Methods. Review of International Geographical Education Online, 11(3), 33–42. https://doi.org/10.33403/rigeo.800466
Cheng, N.-Y., Chen, C.-C., Liang, B.-J., & Tseng, S.-H. (2019). Nondestructive Evaluation of Apple Fruit Quality by Frequency-Domain Diffuse Reflectance Spectroscopy: Variations in Apple Skin and Flesh. Applied Sciences, 9(11), 2355. https://doi.org/10.3390/app9112355 DOI: https://doi.org/10.3390/app9112355
Chowdhury, M. E. H., Rahman, T., Khandakar, A., Ayari, M. A., Khan, A. U., Khan, M. S., Al-Emadi, N., Reaz, M. B. I., Islam, M. T., & Ali, S. H. M. (2021). Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques. AgriEngineering, 3(2), 294–312. https://doi.org/10.3390/agriengineering3020020 DOI: https://doi.org/10.3390/agriengineering3020020
Dhiman, P., Manoharan, P., Lilhore, U. K., Alroobaea, R., Kaur, A., Iwendi, C., Alsafyani, M., Baqasah, A. M., & Raahemifar, K. (2023). PFDI: a precise fruit disease identification model based on context data fusion with faster-CNN in edge computing environment. Eurasip Journal on Advances in Signal Processing, 2023(1). https://doi.org/10.1186/s13634-023-01025-y DOI: https://doi.org/10.1186/s13634-023-01025-y
Eh Teet, S., & Hashim, N. (2023). Recent advances of application of optical imaging techniques for disease detection in fruits and vegetables: A review. Food Control, 152, 109849. https://doi.org/10.1016/j.foodcont.2023.109849 DOI: https://doi.org/10.1016/j.foodcont.2023.109849
Feng, S., Wu, J., Zhou, S., & Li, R. (2019). The Implementation of LeNet-5 with NVDLA on RISC-V SoC. 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), 39–42. https://doi.org/10.1109/ICSESS47205.2019.9040769 DOI: https://doi.org/10.1109/ICSESS47205.2019.9040769
Ghanei Ghooshkhaneh, N., & Mollazade, K. (2023). Optical Techniques for Fungal Disease Detection in Citrus Fruit: A Review. Food and Bioprocess Technology. https://doi.org/10.1007/s11947-023-03005-4 DOI: https://doi.org/10.1007/s11947-023-03005-4
Hincapie, M., Wang, N.-Y., Peres, N. A., & Dewdney, M. M. (2014). Baseline Sensitivity of Guignardia citricarpa Isolates from Florida to Azoxystrobin and Pyraclostrobin. Plant Disease, 98(6), 780–789. https://doi.org/10.1094/PDIS-03-13-0335-RE DOI: https://doi.org/10.1094/PDIS-03-13-0335-RE
Ibrahim, M. R., Haworth, J., & Cheng, T. (2020). Understanding cities with machine eyes: A review of deep computer vision in urban analytics. Cities, 96, 102481. https://doi.org/10.1016/j.cities.2019.102481 DOI: https://doi.org/10.1016/j.cities.2019.102481
Ito, K., Higashi, H., Hietanen, A., Fält, P., Hine, K., Hauta-Kasari, M., & Nakauchi, S. (2023). The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions. Journal of Imaging, 9(1). https://doi.org/10.3390/jimaging9010007 DOI: https://doi.org/10.3390/jimaging9010007
Jasim, W. N., Almola, S. A. S., Alabiech, M. H., & Harfash, E. J. (2022). Citrus Diseases Recognition by Using CNN Model. Informatica (Slovenia), 46(7), 85–94. https://doi.org/10.31449/inf.v46i7.4284 DOI: https://doi.org/10.31449/inf.v46i7.4284
John, M. A., Bankole, I., Ajayi-Moses, O., Ijila, T., Jeje, T., & Lalit, P. (2023). Relevance of Advanced Plant Disease Detection Techniques in Disease and Pest Management for Ensuring Food Security and Their Implication: A Review. American Journal of Plant Sciences, 14(11), 1260–1295. https://doi.org/10.4236/ajps.2023.1411086 DOI: https://doi.org/10.4236/ajps.2023.1411086
Maxwell, A. E., Warner, T. A., & Guillén, L. A. (2021). Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—part 1: Literature review. In Remote Sensing (Vol. 13, Issue 13). https://doi.org/10.3390/rs13132450 DOI: https://doi.org/10.3390/rs13132450
Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Overfitting, Model Tuning, and Evaluation of Prediction Performance. In Multivariate Statistical Machine Learning Methods for Genomic Prediction (pp. 109–139). Springer International Publishing. https://doi.org/10.1007/978-3-030-89010-0_4 DOI: https://doi.org/10.1007/978-3-030-89010-0_4
Muschelli, J. (2020). ROC and AUC with a Binary Predictor: a Potentially Misleading Metric. Journal of Classification, 37(3). https://doi.org/10.1007/s00357-019-09345-1 DOI: https://doi.org/10.1007/s00357-019-09345-1
Ouchra, H., & Belangour, A. (2021). Object detection approaches in images: a survey. In X. Jiang & H. Fujita (Eds.), Thirteenth International Conference on Digital Image Processing (ICDIP 2021) (p. 85). SPIE. https://doi.org/10.1117/12.2601452 DOI: https://doi.org/10.1117/12.2601452
Palei, S., Behera, S. K., & Sethy, P. K. (2023). A Systematic Review of Citrus Disease Perceptions and Fruit Grading Using Machine Vision. Procedia Computer Science, 218, 2504–2519. https://doi.org/10.1016/j.procs.2023.01.225 DOI: https://doi.org/10.1016/j.procs.2023.01.225
Patil, A., & Rane, M. (2021). Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition (pp. 21–30). https://doi.org/10.1007/978-981-15-7078-0_3 DOI: https://doi.org/10.1007/978-981-15-7078-0_3
Rasheed, A., Younis, M. S., Qadir, J., & Bilal, M. (2021). Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection.
Saedi, S. I., & Khosravi, H. (2020). A deep neural network approach towards real-time on-branch fruit recognition for precision horticulture. Expert Systems with Applications, 159, 113594. https://doi.org/10.1016/j.eswa.2020.113594 DOI: https://doi.org/10.1016/j.eswa.2020.113594
Shrivastava, A., Chakkaravathy, M., & Shah, M. A. (2022). A Comprehensive Analysis of Machine Learning Techniques in Biomedical Image Processing Using Convolutional Neural Network. Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022, 1363–1369. https://doi.org/10.1109/IC3I56241.2022.10072911 DOI: https://doi.org/10.1109/IC3I56241.2022.10072911
Strano, M. C., Altieri, G., Allegra, M., Di Renzo, G. C., Paterna, G., Matera, A., & Genovese, F. (2022). Postharvest Technologies of Fresh Citrus Fruit: Advances and Recent Developments for the Loss Reduction during Handling and Storage. Horticulturae, 8(7), 612. https://doi.org/10.3390/horticulturae8070612 DOI: https://doi.org/10.3390/horticulturae8070612
Taha, M. F., ElMasry, G., Gouda, M., Zhou, L., Liang, N., Abdalla, A., Rousseau, D., & Qiu, Z. (2022). Recent Advances of Smart Systems and Internet of Things (IoT) for Aquaponics Automation: A Comprehensive Overview. Chemosensors, 10(8), 303. https://doi.org/10.3390/chemosensors10080303 DOI: https://doi.org/10.3390/chemosensors10080303
Tran, N. T., Miles, A. K., Dietzgen, R. G., Shuey, T. A., Mudge, S. R., Papacek, D., Chandra, K. A., & Drenth, A. (2020). Inoculum Dynamics and Infection of Citrus Fruit by Phyllosticta citricarpa. Phytopathology®, 110(10), 1680–1692. https://doi.org/10.1094/PHYTO-02-20-0047-R DOI: https://doi.org/10.1094/PHYTO-02-20-0047-R
Tripathi, M. (2021). Analysis of Convolutional Neural Network based Image Classification Techniques. Journal of Innovative Image Processing, 3(2), 100–117. https://doi.org/10.36548/jiip.2021.2.003 DOI: https://doi.org/10.36548/jiip.2021.2.003
Vaidya, G., Chaudhary, N., Nasare, S., Warutkar, S., Kawathekar, S., & Dhengre, S. (2023). Disease Detection of Citrus Plants Using Image Processing Techniques. 2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP), 1–6. https://doi.org/10.1109/ICETET-SIP58143.2023.10151494 DOI: https://doi.org/10.1109/ICETET-SIP58143.2023.10151494
Wang, W., & Yang, Y. (2019). Development of convolutional neural network and its application in image classification: a survey. Optical Engineering, 58(04), 1. https://doi.org/10.1117/1.OE.58.4.040901 DOI: https://doi.org/10.1117/1.OE.58.4.040901
Wu, Y. (2023). Joint comparison of the predictive values of multiple binary diagnostic tests: an extension of McNemar’s test. Journal of Biopharmaceutical Statistics, 33(1). https://doi.org/10.1080/10543406.2022.2065500 DOI: https://doi.org/10.1080/10543406.2022.2065500
Yang, L., Yu, X., Zhang, S., Long, H., Zhang, H., Xu, S., & Liao, Y. (2023). GoogLeNet based on residual network and attention mechanism identification of rice leaf diseases. Computers and Electronics in Agriculture, 204, 107543. https://doi.org/10.1016/j.compag.2022.107543 DOI: https://doi.org/10.1016/j.compag.2022.107543
Zacarias, J. R. O., Sullca-Mendoza, Y. C., Valenzuela-Lino, Y. S., Del Carpio-Ramirez, S. I., Moggiano, N., & Coaquira-Rojo, C. (2022). Simulation of an Automated Tahitian lemon grading system based on computer vision. 2022 IEEE 13th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2022. https://doi.org/10.1109/UEMCON54665.2022.9965667 DOI: https://doi.org/10.1109/UEMCON54665.2022.9965667
Zheng, H., Fu, J., Zha, Z.-J., Luo, J., & Mei, T. (2020). Learning Rich Part Hierarchies With Progressive Attention Networks for Fine-Grained Image Recognition. IEEE Transactions on Image Processing, 29, 476–488. https://doi.org/10.1109/TIP.2019.2921876 DOI: https://doi.org/10.1109/TIP.2019.2921876
Zhu, J., Wang, A., Wu, W., Zhao, Z., Xu, Y., Lei, R., & Yue, K. (2023). Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications. Electronics, 12(3), 496. https://doi.org/10.3390/electronics12030496 DOI: https://doi.org/10.3390/electronics12030496
Publicado
Número
Sección
Licencia
Derechos de autor 2025 Andrés Alfonso Huanca Namuche, Bruno Sebastian Terry Alvarado, Cristian García Estrella (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Los artículos publicados en la revista se distribuyen bajo la licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0). Esta licencia permite a terceros descargar, copiar, distribuir, adaptar y reutilizar una obra, incluso con fines comerciales, siempre que se otorgue el crédito adecuado al autor original.
