https://doi.org/10.4081/jae.2022.1432

Tomato leaf diseases recognition based on deep convolutional neural networks

Vol. 54 No. 1 (2023)
Published: 25 August 2022
Image augmented, deep learning, image classification, android application.
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Authors

Kai Tian
tiankai@nsccsz.cn
National Supercomputing Center in Shenzhen, Shenzhen, Guangdong, China.
Jiefeng Zeng
College of Water Conservancy and Civil Engineering, China Agricultural University, Beijing, China.
Tianci Song
Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States.
Zhuliu Li
Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States.
Asenso Evans
College of Water Conservancy and Civil Engineering, South China Agricultural University, Guangzhou, China.
Jiuhao Li
College of Water Conservancy and Civil Engineering, South China Agricultural University, Guangzhou, China.

Tomato disease control remains a major challenge in the agriculture sector. Early stage recognition of these diseases is critical to reduce pesticide usage and mitigate economic losses. While many research works have been inspired by the success of deep learning in computer vision to improve the performance of recognition systems for crop diseases, few of these studies optimized the deep learning models to generalize their findings to practical use in the field. In this work, we proposed a model for identifying tomato leaf diseases based on both in-house data and public tomato leaf images databases. Three deep learning network architectures (VGG16, Inception_v3, and Resnet50) were trained and tested. We packaged the trained model into an Android application named TomatoGuard to identify nine kinds of tomato leaf diseases and healthy tomato leaf. The results showed that TomatoGuard could be adopted as a model for identifying tomato diseases with a 99% test accuracy, showing significantly better performance compared with APP Plantix, a widely used APP for general purpose plant disease detection.

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How to Cite

Tian, K. (2022) “Tomato leaf diseases recognition based on deep convolutional neural networks”, Journal of Agricultural Engineering, 54(1). doi: 10.4081/jae.2022.1432.
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