Prof. Mohammed Danish

I am Mohammed Danish, currently serving as an Assistant Professor in the School of Computer Science and Engineering at RV University, Bengaluru. I hold a Master of Computer Applications (MCA) from JSS Science and Technology University, Mysuru, where I built a strong foundation in software development, artificial intelligence, and data analytics.

My areas of academic interest include artificial intelligence, data science, software engineering, and emerging technologies. I am deeply passionate about exploring how technology can be used to create impactful and inclusive solutions for society. During my academic journey, I have worked on projects involving machine learning applications and have participated in discussions and workshops on AI, image processing, and enterprise technology, further strengthening my research orientation.

As an educator, I strongly believe that teaching is not just about transferring knowledge, but about inspiring curiosity, critical thinking, and lifelong learning. My approach to teaching integrates conceptual clarity with real-world problem-solving, encouraging students to connect theory with practice.

I am committed to creating a collaborative and inclusive classroom environment where every student feels valued and motivated to explore their potential. I believe in the continuous process of learning — both as a teacher and as a learner — and I am excited to contribute to RV University’s vision of nurturing innovation, diversity, and academic excellence.

When I’m not teaching, I enjoy exploring new technologies, mentoring students, and engaging in creative pursuits that blend education with innovation.

“If you can read this, thank a teacher.”– American proverb.

• Existing citrus disease detection systems primarily focused on identifying a single type of disease (e.g., Canker or Greening) rather than multiple infections on a single leaf.
• Earlier models utilized basic CNN architectures or pre-trained deep learning models such as VGG16, ResNet, and InceptionV3 for feature extraction and classification.
• Most studies used small, imbalanced, or laboratory-based datasets, limiting their ability to generalize in real agricultural environments.
• Disease localization or infected area detection (ROI marking) was rarely implemented — models could classify but not visually indicate infected regions.
• Traditional image-processing methods (e.g., color thresholding, texture analysis) were combined with CNNs, but results lacked consistency under varied lighting and background conditions.
• Systems were designed mainly for research purposes, with limited focus on real-time usability or deployment for farmers.
• Few models integrated hybrid approaches, such as combining CNN and SVM, to improve accuracy and robustness.
• Performance metrics across existing systems typically achieved 70–92% accuracy, indicating room for improvement.
• Lack of multi-disease detection and visual explanation remained the most significant research gap.
• The current research aims to overcome these limitations by developing an InceptionV3-based CNN model capable of detecting and highlighting up to three infections per leaf with improved accuracy and interpretability."
• Research Interest
• Deep learning and convolutional neural networks (CNNs) for agricultural image classification
• Multi-disease detection and classification in plant leaves
• Computer vision applications in precision agriculture
• Image preprocessing and enhancement for disease identification
• Feature extraction and transfer learning using pre-trained CNN architectures
• Real-time disease detection and visualization using deep learning
• Model optimization and accuracy enhancement techniques in CNNs
• Explainable AI (XAI) for visualizing infected areas in plant disease detection
• Integration of machine learning models for hybrid performance improvement (e.g., CNN–SVM)
• Development of user-friendly systems for automated agricultural diagnostics

• NET 2024(Phd) Qualified