Date of Award

8-2024

Document Type

Project

Degree Name

Master of Science in Information Systems and Technology

Department

Information and Decision Sciences

First Reader/Committee Chair

Dr. Conrad Shayo

Abstract

Corn is a widely cultivated agricultural product, serving as a cornerstone in food production and industrial applications such as biofuels, playing a crucial role in the global economy. This study explores the application of deep transfer learning to accurately classify major corn diseases from leaf images, aiming to enhance disease management strategies for improved agricultural productivity and sustainability. The customized Dense net 201 model achieved 95% prediction accuracy on an untrained dataset. Data augmentation improved the model’s accuracy from 91% to 95%. This supervised learning approach enhances the model’s performance by increasing the diversity, leading to better generalization and accuracy. Experimentation of the four optimizers, namely Adagrad, SGD, AdaDelta, and Adam, achieved the same accuracy (95%).

Increasing the data by a significant margin leads to a considerable enhancement of the model from 91% to 95% and thus serves as evidence of the effectiveness of the proposed method in improving the model performance, therefore improving the

generalization samples for better training samples. It is testified that even the optimizer selection influences the accuracy rate. AdaDelta and Adagard achieved the highest accuracy at 95%, emphasizing the importance of selecting the right optimizer for optimal performance. The optimized deep learning model achieved 95% accuracy in detecting and classifying corn leaf diseases, benefiting farmers in disease identification.

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