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Position Papers of the 20th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 44

Integrating Real-ESRGAN with CNN Models for UAV Image Based Plant Disease Detection

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DOI: http://dx.doi.org/10.15439/2025F2775

Citation: Position Papers of the 20th Conference on Computer Science and Intelligence Systems, M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 44, pages 69–73 ()

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Abstract. The integration of deep learning models with UAV captured images for plant disease detection has been explored in many papers and has the potential to revolutionize commercial precision agriculture, by allowing for early and efficient detection and classification of crop disease stages. In order to address the limitations posed by low-resolution aerial imaging, this paper proposes the additional integration of an Enhanced Super Resolution Generative Adversarial Network (ESRGAN) with a Convolutional Neural Network model for field monitoring through UAV captured imagery. UAVs are a cost effective method of monitoring large swaths of agricultural land; however, it is difficult to capture images of a high enough quality and clarity to be adequately analyzed by a CNN. The images typically lack the necessary resolution for accurate classification, especially for diseases with smaller, less noticable symptoms. The Real-ESRGAN model is employed to generate a dataset of high-resolution images, from low-resolution inputs, allowing the disease detection CNN to more accurately and effectively identify and classify disease stages in Armillaria afflicted cherry trees. This solution offers a solution to the problem posed by traditional UAV based approaches that enhances classification accuracy even in suboptimal conditions. Through this integrated approach, the model was able to reach an increased validation accuracy, as well as significantly decreased loss values due to the ESRGAN enhanced imagery allowing for clearer detection of early stage Armillaria symptoms. This integrated system provides a practical scalable solution for commercial agriculture, allowing for more comprehensive and efficient crop disease monitoring. Future research can be explored to optimize the architecture of this model and expand its applicability to other crops and environmental conditions, allowing more efficient precision agriculture and paving the way for more sustainable farming practices.

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