Towards Smart Farming: Image-Based Crop Health Assessment and Disease Diagnosis Using Deep Learning Techniques

Author

Kristina Botova, Fort Hays State UniversityFollow

Department

Informatics

Degree Name

Master of Science (MS)

Abstract

Accurate crop monitoring is essential for optimizing agricultural productivity and ensuring food security. This study presents a comprehensive deep learning framework for image crop type recognition, health status prediction, and disease detection using multiple Convolutional Neural Network (CNN) models. The proposed approach uses open-source datasets consisting of five crop types (apple, corn, grape, potato, tomato), varying health conditions, and common diseases. By deploying specialized CNN architecture focused on each task, the system achieves a high accuracy of 99.25% in classifying crop types, identifying health status, and detecting specific diseases. Compared to a single CNN model, the use of the proposed multi-model approach yields significantly improved performance in terms of precision (0.9254), recall (0.9258), F-1 score (0.9248), and overall accuracy. This architecture enables more effective learning and decision- making. The integrated system offers a robust, scalable, and automated solution for real-time crop monitoring. Precision agriculture can assist farmers in making informed decisions, lead to sustainable agricultural practices, and improve crop and resource management.

Keywords

Multiple CNN models, Disease detection, Precision agriculture, Image leaf recognition, Artificial Intelligence

Advisor

Dr. Anas Hourani

Date of Award

Spring 2025

Document Type

Thesis

Recommended Citation

Botova, Kristina, "Towards Smart Farming: Image-Based Crop Health Assessment and Disease Diagnosis Using Deep Learning Techniques" (2025). Master's Theses. 3255.
Available at: https://scholars.fhsu.edu/theses/3255

Rights

© The Author