Use of Trained Convolutional Neural Networks for Analysis of Symptoms Caused by Botrytis fabae Sard




artificial intelligence, deep learning, Botrys fabae Sard, severity scale


This study evaluated the use of convolutional neural networks (CNN) in agricultural disease recognition, specifically for Botrytis fabae symptoms. An experimental bean culture was used to capture images of healthy and affected leaflets, which were then used to perform binary classification and severity classification tests using several CNN models. The results showed that CNN models achieved high accuracy in binary classification, but performance decreased in severity classification due to the complexity of the task. InceptionResNet and ResNet101 were the models that performed best in this task. The study also utilized the Grad-CAM algorithm to identify the most significant B. fabae symptoms recognized by the CNNs. Overall, these findings can be used to develop a smart farming tool for crop production support and plant pathology research.


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Arnal, J. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering. 180(1): 96–107.

Ateş, A.; Dan, H.; Yilmaz, N.; Konuksal, A. (2017). Botrytis fabae and Bean common mosaic virus (BCMV) are the most common diseases of Faba bean (Vicia faba L.) in TRNC. Akademik Ziraat Dergisi. 6(2): 115–122. 10.29278/azd.371067

Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. IEEE. Apr(1): 1–8.

Chollet, F. (2018). Deep learning with Python. Shelter Island: Manning Publications. 86 p.

Géron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). United States of America: ’Reilly Media. 102p.

Ghosal, S.; Blystone, D.; Singh, A.; Ganapathysubramanian, B.; Singh, A.; Sarkar, S. (2018). An explainable deep machine vision framework for plant stress phenotyping. PNAS. Agricultural Sciences. 115(18): 4613–4618. 10.1073/pnas.1716999115

Hammad, M.; Potgieter, J.; Mahmood, K. (2019). Plant disease detection and classification by Deep Learning. Plants. 8(468): 1-22.

He, K.; Zhang, X.; Ren, S.; Sun, J. (2016). Deep residual learning for image recognition. IEEE. Dec(1): 1–12.

Howard, A.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv. arXiv:1704.04861.

ICARDA- International Center for Agricultural Research in the Dry Areas (2005). Faba bean pathology progress report 2003/4: Food Legume Improvement Program. Syrian Arab Republic: ICARDA. 24p.

Liu, J.; Wang, X. (2021). Plant diseases and pests’ detection based on deep learning: A review. Plant Methods. 17(1): 1-18. 10.1186/s13007-021-00722-9

Lozada, W.; Suarez, M.; Avendaño, E. (2021). Aplicación de redes neuronales convolucionales para la detección del tizón tardío Phytophthora infestans en papa Solanum tuberosum. Revista U.D.C.A Actualidad & Divulgación Científica. 24(2): 1–9.

Maeda, V.; Guerrero, C.; Olvera, C.; Araiza, M.; Espinoza, G.; Bordón, R. (2018). Redes neuronales convolucionales para la detección y clasificación de enfermedades de plantas basadas en imágenes digitales. Rev. Científica Biológico Agropecuaria Tuxpan. 6(1): 275-282.

Olle, M.; Sooväli, P. (2020). The severity of diseases of faba bean depending on sowing rate and variety. Acta Agriculturae Scandinavica. 70(7): 572–577.

Ouhami, M.; Es-Saady, Y.; Hajji, M.; Hafiane, A.; Canals, R.; Yassa, M. (2020). Deep transfer learning models for tomato disease detection. Including Subseries Lecture Notes in Artificial Intelligence and Lecture. Notes in Bioinformatics. 12119(LNCS): 65-73. 10.1007/978-3-030-51935-3_7

Paymode, A.; Malode, V. (2022). Transfer Learning for multi-crop leaf disease image classification using Convolutional Neural Network VGG. Artificial Intelligence in Agriculture. 6(1): 23-33.

Selvaraju, R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; Batra, D. (2020). Grad-CAM: Visual explanations from Deep Networks via Gradient-Based Localization. International Journal of Computer Vision. 128(2): 336-359.

Simonyan, K.; Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv. arXiv:1409.1556.

Szegedy, C.; Ioffe, S.; Vanhoucke, V.; Alemi, A. (2016a). Inception-V4, inception-ResNet and the impact of residual connections on learning. arXiv. arXiv:1602.07261v2.

Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; Wojna, Z. (2016b). Rethinking the inception architecture for computer vision. arXiv. arXiv:1512.00567v3.

Türkoğlu, M.; Hanbay, D. (2019). Plant disease and pest detection using Deep Learning-based features. Turkish Journal of Electrical Engineering and Computer Sciences. 27(3): 1636–1651.

Yang, G.; Xu, N.; Hong, Z. (2018). Identification of navel orange lesions by nonlinear Deep Learning algorithm. Engenharia Agrícola. 38(5): 783–796.




How to Cite

Álvarez-Sánchez, D.-E. ., Arévalo, A. . ., Benavides , I. F. ., Salazar-González, C. ., & Betancourth, C. . . (2023). Use of Trained Convolutional Neural Networks for Analysis of Symptoms Caused by Botrytis fabae Sard. Revista De Ciencias Agrícolas, 40(1), e1198.