An Intelligent Computer Vision and CNN Framework for Automated Plant Disease Detection and Precision Agriculture

2026-06-18T07:08:02+00:00

Plant diseases represent one of the most critical threats to global agricultural productivity, where maize crops are particularly susceptible to multiple fungal diseases that significantly reduce yield quality and quantity. Traditional disease identification methods depend on manual visual inspection by agricultural experts, which is time-consuming, inconsistent, and impractical for large-scale precision farming applications. Within the paradigm of intelligent agricultural systems, this study proposes an automated deep learning and computer vision framework for maize leaf disease identification and classification. The proposed system introduces a novel Multi-Resolution Weighted CNN (MRW-CNN) architecture that leverages multi-scale feature extraction to achieve superior classification performance across five disease categories, including Common Rust, Grey Leaf Spot, Healthy, Northern Leaf Blight, and Southern Rust. A comprehensive dataset of 2,500 annotated maize leaf images with disease stage annotations (early, advancing, and severe) is utilized for training and evaluation. Comparative analysis of nine fine-tuned CNN architectures demonstrates that MRW-CNN achieves the highest testing accuracy of 97.04% with validation accuracy of 96.56%, outperforming established architectures including Xception (95.80%), MobileNet (94.64%), and Inception V3 (94.48%). The experimental results confirm the effectiveness of the proposed approach in delivering accurate, scalable, and computationally efficient automated disease detection, contributing toward intelligent precision agriculture and early crop disease management systems.

Development of an Adaptive E-Learning Model for Personalized Learning Path Recommendation Using Ant Colony Optimization and Hybrid Filtering

2026-06-18T06:50:30+00:00

The majority of traditional e-learning models often fail to cater to individual learners’ needs due to limited parameter flexibility across diverse populations and the scope of adaptive courses. To address these challenges, this research developed an adaptive e-learning model using Ant Colony Optimization (ACO), collaborative filtering, and content-based filtering. The ACO was used for adapting the learning contents and activities, while also dynamically adjusting the learning path. K-Nearest Neighbor (KNN) was used for e-learning styles and teaching strategies to generate courses for learners. The Felder-Silverman Learning Style Model is employed to identify and accommodate different learning styles, ensuring a tailored educational experience. The implementation of the model is done using Python and PHP frameworks. An online survey was conducted with 500 undergraduate students from various academic disciplines to evaluate the developed model. The KNN algorithm achieved the best performance with an R² of 0.986 and an MAE of 0.012, indicating both high accuracy and minimal prediction error. A direct comparison with the benchmark study reveals that our models outperform their counterparts, with the KNN model achieving (0.012, 0.986) against theirs (0.013, 0.875). It demonstrates significant improvements in learner satisfaction and academic performance. This research contributes to e-learning by offering a flexible, adaptive solution that enhances educational outcomes through personalized learning experiences.

Journal of Data Engineering and Knowledge Discovery

An Intelligent Computer Vision and CNN Framework for Automated Plant Disease Detection and Precision Agriculture

Development of an Adaptive E-Learning Model for Personalized Learning Path Recommendation Using Ant Colony Optimization and Hybrid Filtering