Abstract
Deep Learning has solved complicated applications with increasing accuracies over time. The recent interest in this technology, especially in its potential application in agriculture, has powered the growth of efficient systems to solve real problems, such as non-destructive methods for plant anomalies recognition. Despite the advances in the area, there remains a lack of performance in real-field scenarios. To deal with those issues, our research proposes an efficient solution that provides farmers with a technology that facilitates proper management of crops. We present two efficient techniques based on deep learning for plant disease recognition. The first method introduces a practical solution based on a deep meta-architecture and a feature extractor to recognize plant diseases and their location in the image. The second method addresses the problem of class imbalance and false positives through the introduction of a refinement function called Filter Bank. We validate the performance of our methods on our tomato plant diseases and pest dataset. We collected our own data and designed the annotation process. Qualitative and quantitative results show that despite the complexity of real-field scenarios, plant diseases are successfully recognized. The insights drawn from our research helps to better understand the strengths and limitations of plant diseases recognition.
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Acknowledgments
This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2019R1A6A1A09031717), and by the “Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01389105)” Rural Development Administration, Republic of Korea.
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Fuentes, A., Yoon, S., Park, D.S. (2020). Deep Learning-Based Techniques for Plant Diseases Recognition in Real-Field Scenarios. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_1
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