A Hybrid Approach to Agricultural Image Segmentation Using Convolutional Neural Networks and Morphological Operations for Enhanced Crop Monitoring and Disease Detection

Abstract

A hybrid approach combining morphological color segmentation and Convolutional Neural Networks (CNNs) was employed to improve color image segmentation for agricultural applications. The primary goal was to partition agricultural images into significant regions representing plant components, soil, and background features. The method was evaluated against traditional algorithms such as K-Means, Improved K-Means, Fuzzy C-Means (FCM), and Region-growing, demonstrating superior accuracy in agricultural tasks like crop surveillance and disease detection. Morphological operations like dilation, erosion, opening, and closing were used to refine the segmentation by enhancing pixel characteristics, defining borders, and minimizing noise. Quantitative evaluation using metrics such as Rand Index (RI), Global Consistency Error (GCE), and Variation of Information (VOI) showed the proposed method achieved an RI of 0.95, outperforming conventional approaches with lower GCE and VOI scores. Additionally, CNN architectures like U-Net and Mask R-CNN were integrated to capture both local and global features. This hybrid approach offers a powerful solution for precision agriculture, enabling better crop monitoring and disease identification in challenging environments. Future work will explore further integration with more advanced CNN models and fine-tuning the methodology for real-time agricultural applications.