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      Deep Learning for Anthracnose Diagnosis in Turnip Leaves

        1 ,
      ScienceOpen Posters
      Computer Vision, Deep Learning, Convolutional Neural Networks, Plant Disease Detection


            Fungal diseases in plants are extremely pressing issues in the agricultural industry, threatening global food security by reducing crop yields and quality. Traditional approaches to disease diagnosis and management have failed to recognize symptoms when they first appear. Leaves of the turnip, a plant of high agricultural value, has been especially affected by the fungal disease Anthracnose. Therefore, this study aimed to develop a novel convolutional neural network that can identify turnip leaves with early symptoms of Anathrosce blight. The model had 4 convolutional blocks and was trained on a custom dataset of 1,470 images, randomly split into 60% train, 20% validation, and 20% test. To compare how the CNN model fared with other machine learning algorithms, a support vector machine(SVM) model was developed and trained with the same image dataset. The CNN model’s accuracy 98.75% compared to the SVM model’s 80.50% accuracy. These results validate the efficacy of the CNN model to accurately identify infected turnip leaves and demonstrate that it can be implemented into a practical disease diagnosis system. Future studies are warranted to improve the model through means such as k-fold cross validation as well as apply the model architecture to other crops and diseases.


            Author and article information

            ScienceOpen Posters
            6 November 2021
            [1 ] Mission San Jose High School, 41717 Palm Ave, Fremont, CA 94539
            Author notes

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Computer vision & Pattern recognition,Artificial intelligence,Pests, Diseases & Weeds
            Computer Vision,Deep Learning,Convolutional Neural Networks,Plant Disease Detection


            1. Kumar Pankaj, Ashtekar Sunidhi, Jayakrishna S. S, Bharath K P, Vanathi P. T, Rajesh Kumar M. Classification of Mango Leaves Infected by Fungal Disease Anthracnose Using Deep Learning. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). 2021. IEEE. [Cross Ref]

            2. Kumar Santhosh S., Raghavendra B.K.. Diseases Detection of Various Plant Leaf Using Image Processing Techniques: A Review. 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS). 2019. IEEE. [Cross Ref]

            3. Chapaneri Radhika, Desai Maithili, Goyal Anmolika, Ghose Shreya, Das Sheona. Plant Disease Detection: A Comprehensive Survey. 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA). 2020. IEEE. [Cross Ref]

            4. Khirade Sachin D., Patil A.B.. Plant Disease Detection Using Image Processing. 2015 International Conference on Computing Communication Control and Automation. 2015. IEEE. [Cross Ref]


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