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Classification of crop leaf diseases using image to image translation with deep-dream

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Abstract

Crop diseases are one of the primary triggers of yield devastation. As a result, early detection of crop diseases is critical to avert crop losses. In this study, a Deep-Dream (DD) based crop leaf disease detection (CLDD) architecture is proposed using a combination of Deep Learning (DL) along with Machine Learning (ML) techniques. DD is used to pre-process and segment the lesions present in leaves. The proposed novel framework involves 24 different Hybrid Deep Neural (HDN) Models that comprise the integration of eight distinct variations of the pre-trained DL model, namely EfficientNet (EffiNet) ranges from EffiNet B0-B7 in form of a feature extractor. Subsequently, three ML algorithms, namely Random Forest (RF), AdaBoost (ADB), and Stochastic Gradient Boosting (SGB) are employed as classifiers. In this study, the Optuna framework was also applied to tune the hyperparameters of these classifiers. For this implementation, the tomato crop dataset (a subset of the PlantVillage dataset) was used. It has been observed that the proposed model achieved the highest accuracy with the DD-EffiNet-B4-ADB model. The accuracy results of all the models have ranged from 84% to 96%. Moreover, DD shows a better interpretation of the disease lesions, and hence classification accuracy is also enhanced by 3% (93% to 96%) after applying DD segmentation. The proposed model was also validated with an actual-field tomato crop leaf image database gathered from the Indian Agricultural Research Institute with a high-level accuracy of 100%. This technique could help farmers to predict the pathogenic diseases at an early stage of disease visibility.

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Data availability

The IARI-TEBD dataset belongs to the multiple agencies of the Govt. of India and needs approval before making it public. Therefore, with the approval of appropriate agency, data will be made public in later stage. Moreover, the PlantVillage-Tomato datasets are available at the following publicly-accessible site: https://github.com/spMohanty/PlantVillage-Dataset.

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Acknowledgments

Authors are thankful to the Department of Science & Technology, Government of India, Delhi, for funding a project on “Application of IoT in Agriculture Sector” through the ICPS division. This work is a part of the project work.

Funding

The work was funded by the Department of Science and Technology under a project with reference number “DST/Reference.No.T-319/2018–19”. We are also thankful to the Department of Plant Pathology of Indian Agricultural Research Institute (IARI) for their immense support to conduct this study.

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Authors and Affiliations

  1. University School of Information, Communication, and Technology, Guru Gobind Singh Indraprastha University, New Delhi, India

    Priyanka Sahu, Anuradha Chug & Amit Prakash Singh

  2. Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, India

    Dinesh Singh

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  1. Priyanka Sahu
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  2. Anuradha Chug
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Priyanka Sahu, Anuradha Chug, Amit Prakash Singh and Dinesh Singh. The first draft of the manuscript was written by Priyanka Sahu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Priyanka Sahu.

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Sahu, P., Chug, A., Singh, A.P. et al. Classification of crop leaf diseases using image to image translation with deep-dream. Multimed Tools Appl 82, 35585–35619 (2023). https://doi.org/10.1007/s11042-023-14994-x

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