DeepDetectCorn: Transfer Learning-Powered Deep Learning ‎for Corn Crop Disease Recognition

  • Authors

    • Nitesh Sureja Department of Computer Science and Engineering, Krishna School of Emerging Technology and Applied Research, KPGU, ‎Vadodara, INDIA
    • Priyanka Patel Department of Computer Science and Engineering, Sardar Vallabhbhai Patel institute of Technology, Vasad, INDIA
    • Sonia Panesar Department of Computer Science and Engineering, Krishna School of Emerging Technology and Applied Research, KPGU, ‎Vadodara, INDIA
    • Monali Parikh Department of Information Technology, Krishna School of Emerging Technology and Applied Research, KPGU, ‎Vadodara, INDIA
    • Apoorva Shah Department of Computer Science and Engineering, Krishna School of Emerging Technology and Applied Research, KPGU, ‎Vadodara, INDIA
    • Vaibhavi Pandya Department of Computer Science and Engineering, Krishna School of Diploma Studies, KPGU, ‎ Vadodara, INDIA
    https://doi.org/10.14419/a367wx11

    Received date: May 14, 2025

    Accepted date: June 3, 2025

    Published date: June 9, 2025

  • Convolution Neural Networks; Corn Crop Diseases; Deep Learning; Transfer Learning; Machine Learning

  • Abstract

    Plant diseases and pests are the main variables affecting corn output and quality in agriculture. Identifying corn leaf diseases is challenging ‎for farmers worldwide, hindering effective prevention efforts. Using manual methods to diagnose corn infections can be time-consuming, ‎subjective, and difficult. This investigation employs various convolutional neural network (CNN) models to identify three corn diseases and ‎healthy leaves through classification techniques, feature extraction, and image enhancement. Preprocessing using data augmentation expanded datasets and reduced overfitting. CNN layers automatically retrieve features. The dataset contains images in four different categories. ‎Three of them are disease-related, and one is about healthy leaves. Common rust, blight, and gray leaf spot are the diseases taken into consideration. The dataset includes 4,188 corn leaf images. To verify, CNN models (CNN, VGG19, Xception, ResNet-152, EfficientNetV2L, ‎and InceptionResNetV2) were implemented and compared. The EfficientNetV2L and Xception models performed well with 99.37% and ‎‎99.15% test accuracy. The performance of the other models is also noteworthy. The study confirmed that the CNN algorithms can be a suitable option in identifying corn leaf diseases‎.

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    Sureja, N., Patel, P., Panesar, S., Parikh, M., Shah, A., & Pandya, V. (2025). DeepDetectCorn: Transfer Learning-Powered Deep Learning ‎for Corn Crop Disease Recognition. International Journal of Basic and Applied Sciences, 14(2), 58-67. https://doi.org/10.14419/a367wx11