ABSTRACT: Maize stands as a vital agricultural crop worldwide, serving as a crucial source of sustenance for humans, livestock feed, biofuel, and a raw material for various products. The detection and management of natural diseases pose a significant challenge for food crops. Swift identification of plant diseases remains a time-consuming and arduous task, particularly for small-scale farmers. Conventional methods and tools lack efficacy, demanding extensive manual labor and time investment. Timely disease detection is imperative for effective treatment and timely implementation of pesticide measures to curb the spread. This research introduces an efficient image classification model based on advanced deep learning techniques, specifically tailored for accurately identifying three prevalent maize leaf diseases. The proposed model employs the Xception model, leveraging transfer learning through pre-trained Xception models for robust feature extraction. The amalgamation of deep features creates a sophisticated feature set, enhancing the model's ability to derive valuable insights from the dataset. With reduced computational costs and the capability to capture essential characteristics, this depth-wise separable Convolutional Neural Network (CNN) exhibits superior efficiency. Comparative analysis against other CNNs, such as EfficientNetB0 and DenseNet121, highlights the exceptional performance of the suggested model, achieving an impressive accuracy of 99.40%. The study underscores the suggested model's superior accuracy and its proficiency in diagnosing various corn leaf diseases.
Keywords – Deep Learning, Corn Leaf Diseases, Image Classification, Convolutional Neural Network (CNN), Xception Model, Disease Detection, Agricultural Automation, Precision Farming, Crop Disease Management, Plant Pathology.
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DOI:
10.17148/IJARCCE.2024.13464
