N.S. Sudarmanian

Innovative Approaches to Bengal gram Yield Mapping: Integration of Sentinel-1 SAR and Crop Simulation Models for Precision Agriculture

Sellaperumal Pazhanivelan, N.S. Sudarmanian, S. Satheesh & K.P. Ragunath · Journal of Scientific Research and Reports · 2025

Accurate spatial yield estimation is crucial for optimizing agricultural management and ensuring food security. This study integrates Sentinel-1A SAR remote sensing data and the DSSAT crop simulation model to predict Bengal gram (chickpea) yield in Nagaur district, Rajasthan, Ind...

Open access Research Article 10.9734/jsrr/2025/v31i12788

Deep Learning-Based Multi-Class Pest and Disease Detection in Agricultural Fields

Sellaperumal Pazhanivelan, K.P. Ragunath, N.S. Sudarmanian, S. Satheesh & P. Shanmugapriya · Journal of Scientific Research and Reports · 2025

Farmers and agricultural workers would manually inspect crops for signs of pests or use traps to monitor pest populations. The advent of deep learning algorithms such as vision transformers and FastAI ResNet has brought about a significant transformation in pest detection practic...

Open access Research Article 10.9734/jsrr/2025/v31i12797

Enhancing Flood Area Mapping Accuracy Using Advanced SAR Data Processing

S. Pazhanivelan, K. P. Ragunath, N.S. Sudarmanian, S. Satheesh & K. Sneka · International Journal of Environment and Climate Change · 2024

Aim: To assess the spatial distribution of floods in 2024 using remote sensing data, specifically Synthetic Aperture Radar (SAR), a powerful tool in flood monitoring and mapping due to its ability to capture data under all weather conditions, including rain and cloud cover provid...

Open access Research Article 10.9734/ijecc/2024/v14i124662