Abstract
This paper concerns an approach for developing a set of tools based on computer vision and deep learning to detect and classify plant diseases in smart agriculture. The main reason that motivated this work is that early detection of plant diseases can help farmers effectively monitor the health of their culture, as well as make the best decision to avoid the spread of the pathogens. In this work, a novel way for training and building a fast and extensible solution to detect plant diseases with images and a convolutional neural network is described. The development of this methodology is achieved in two main steps. The first one introduces Mask R-CNN to give bounding boxes and masks over the area of plant leaves in images. The model is trained on the PlantDoc dataset made of labeled images of leaves with their corresponding bounding boxes. And the second one presents a convolutional neural network that returns the class of the plant. This CNN is trained on the PlantVillage dataset to recognize 38 classes across 14 plant species. Experimental results of the proposed approach show an average accuracy of 76% for leaf segmentation and 83% for disease classification.
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References
Mohanty, S., Hughes, D., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7 (2016). https://doi.org/10.3389/fpls.2016.01419
UNEP. Smallholders, Food Security, and the Environment. Rome : International Fund for Agricultural Development (IFAD) (2013). https://www.ifad.org/documents/10180/666cac2414b643c2876d9c2d1f01d5dd
Abdu, A., Mokji, M., Sheikh, U.: Machine Learning for Plant Disease Detection: Investigative Comparison Between Support Vector Machine and Deep Learning (2020). https://doi.org/10.11591/ijai.v9.i3
Fang, Y., Ramasamy, R.: Current and prospective methods for plant disease detection. Biosensors 5(3), 537–561 (2015). https://doi.org/10.3390/bios5030537
Reddy, P.R., Divya, S.N., Vijayalakshmi, R.: Plant disease detection technique tool—a theoretical approach. Int. J. Innov. Technol. Res. 4, 91–93 (2015)
Chaudhary, P., Chaudhari, A.K., Cheeran, A.N., Godara, S.: Color transform based approach for disease spot detection on plant leaf. Int. J. T. N
Tete, T.N., Kamlu, S.: Detection of plant disease using threshold, k-mean cluster and ann algorithm. In: 2017 2nd International Conference for Convergence in Technology (I2CT), Mumbai, pp. 523–526 (2017). https://doi.org/10.1109/I2CT.2017.8226184.
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 2016, 1–11 (2016). https://doi.org/10.1155/2016/3289801
Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., Batra, N.: PlantDoc, Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (2020). https://doi.org/10.1145/3371158.3371196
How MaskRCNN Works? | ArcGIS for Developers. Developers.arcgis.com. https://developers.arcgis.com/python/guide/how-maskrcnn-works/. Accessed 8 Aug 2020
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017). https://doi.org/10.1109/iccv.2017
Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4), 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9
Pawara, P., Okafor, E., Schomaker, L., Wiering, M.: Data Augmentation for Plant Classification. Adv. Concepts Intell. Vis. Syst. pp. 615–626 (2017). https://doi.org/10.1007/978-3-319-70353-4_52
PlantVillage Disease Classification Challenge, crowdAI (2016). https://www.crowdai.org/challenges/1. Accessed 04 Sep 2020
Mohanty, S.: spMohanty/PlantVillage-Dataset. GitHub (2016). https://github.com/spMohanty/PlantVillage-Dataset. Accessed 4 Sept 2020
Kuznichov, D., Zvirin, A., Honen, Y., Kimmel, R.: Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2019). https://doi.org/10.1109/cvprw.2019.00314
Goodfellow, I., Bengio, Y., Courville, A.: 6.2.2.3 Softmax Units for Multinoulli Output Distributions. Deep Learning. MIT Press, pp. 180–184 (2016). ISBN 978–0–26203561–3
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Masmoudi, I., Lghoul, R. (2021). A Deep Convolutional Neural Network Approach for Plant Leaf Segmentation and Disease Classification in Smart Agriculture. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-80126-7_73
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DOI: https://doi.org/10.1007/978-3-030-80126-7_73
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