AI-Based Early Detection Systems for Crop Diseases

  • Authors

    • Josh Rogers President and CEO, NewTown Macon, USA. Author
    • Brandon Truaxe CEO, Foremost Group, USA. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2018PII2T5B

    Published 08-04-2018

  • Artificial Intelligence, Crop Disease Detection, Machine Learning, Deep Learning, Image Processing, Precision Agriculture, Early Detection Systems, Convolutional Neural Networks

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    [1]
    J. Rogers and B. Truaxe, “AI-Based Early Detection Systems for Crop Diseases”, ijmiet, vol. 1, no. 2, pp. 01–14, Aug. 2018, doi: 10.67228/30715628/IJMIET-2018PII2T5B.

  • Abstract

    Agriculture is a key sector in developing economies, but crop diseases significantly impact productivity, food security, and farmers’ livelihoods. Early detection is crucial to minimize losses, yet traditional methods are slow, error-prone, and depend heavily on human expertise. Recent advancements in Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled more efficient automated crop disease detection. This study reviews pre-2018 AI-based approaches, focusing on techniques such as image processing, feature extraction, and classification methods. It highlights models like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid systems combining traditional and modern techniques. The proposed approach includes preprocessing, segmentation, color transformation, and extraction of texture, color, and shape features, followed by supervised learning for classification. AI systems can detect subtle disease symptoms early, achieving over 90% accuracy under controlled conditions. Integration with mobile and IoT technologies enables real-time monitoring and decision support for farmers. However, challenges such as limited datasets, environmental variability, and computational constraints remain. Future work should focus on developing scalable, robust, and field-deployable solutions for diverse agricultural conditions.

  • References

    [1] Gonzalez, R. C., & Woods, R. E. (2008). Digital Image Processing (3rd ed.). Pearson.

    [2] Canny, J. (1986). “A Computational Approach to Edge Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.

    [3] Otsu, N. (1979). “A Threshold Selection Method from Gray-Level Histograms.” IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.

    [4] Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). “Textural Features for Image Classification.” IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610–621.

    [5] Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern Classification (2nd ed.). Wiley.

    [6] Cortes, C., & Vapnik, V. (1995). “Support-Vector Networks.” Machine Learning, 20(3), 273–297.

    [7] Cover, T., & Hart, P. (1967). “Nearest Neighbor Pattern Classification.” IEEE Transactions on Information Theory, 13(1), 21–27.

    [8] Quinlan, J. R. (1986). “Induction of Decision Trees.” Machine Learning, 1(1), 81–106.

    [9] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

    [10] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). “ImageNet Classification with Deep Convolutional Neural Networks.” Advances in Neural Information Processing Systems (NIPS).

    [11] Simonyan, K., & Zisserman, A. (2015). “Very Deep Convolutional Networks for Large-Scale Image Recognition.” International Conference on Learning Representations (ICLR).

    [12] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). “Gradient-Based Learning Applied to Document Recognition.” Proceedings of the IEEE, 86(11), 2278–2324.

    [13] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

    [14] Szeliski, R. (2010). Computer Vision: Algorithms and Applications. Springer.

    [15] Zhang, D., & Lu, G. (2004). “Review of Shape Representation and Description Techniques.” Pattern Recognition, 37(1), 1–19.

  • Downloads