International Journal of Machine Learning, AI & Data Science Evolution

E-ISSN: 3067-5073

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Volume 2 Issue 5
September-October 2026

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Artificial Intelligence-Assisted Crop Disease Recognition Using Digital Image Processing Techniques

Author(s) Emma Visser
Country Netherlands
Abstract Crop diseases can reduce agricultural productivity and quality when symptoms are not identified at an appropriate stage. Visual inspection remains important in plant-health assessment, but large cultivation areas and similarities among symptoms can make consistent recognition difficult. Digital image processing provides computational tools for enhancing leaf images, separating symptomatic regions, and extracting color, texture, and shape characteristics. Artificial intelligence can extend this process by learning complex image patterns and assigning disease categories automatically. This hypothetical methodological study examines an artificial intelligence-assisted crop disease recognition framework integrating digital image preprocessing, lesion segmentation, handcrafted visual descriptors, and convolutional image classification. A fictional dataset containing 1,200 crop-leaf images is divided into hypothetical training, validation, and testing subsets. Three conceptual configurations are compared: a conventional color-texture support vector machine, a convolutional neural network, and an integrated AI-assisted image-processing and convolutional model.
The illustrative analysis evaluates overall classification performance through accuracy, precision, recall, and F1 score. A second analytical comparison examines recognition across healthy leaves, leaf spot, blight, rust, and mildew categories. Under the assigned hypothetical values, the integrated architecture records the strongest overall performance, although recognition varies across symptom categories. These numerical results are illustrative rather than experimentally obtained. The study concludes that combining digital image processing with artificial intelligence offers a useful methodological framework for crop-disease recognition. However, reliable deployment requires representative field images, robust segmentation, control of illumination and background variability, independent testing, and careful examination of visually similar symptoms. Empirical experimentation is required before practical diagnostic accuracy can be established.
Keywords Artificial Intelligence, Crop Disease Recognition, Digital Image Processing, Plant Disease Detection, Convolutional Neural Network, Leaf Image Classification, Computer Vision, Agricultural Informatics, Image Segmentation, Precision Agriculture.
Field Engineering
Published In Volume 1, Issue 4, September-October 2025
Published On 2025-10-02
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E-ISSN: 3067-5073
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