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Divya Rathore

Publications and source records attributed to Divya Rathore.

2 recordsLinked to original sources

Design, Integration, and Evaluation of a Dual-Arm Robotic System for High Throughput Tissue Sampling from Potato Tubers

Manual tissue extraction from potato tubers for molecular pathogen detection is highly laborious. This study presents a machine-vision-guided, dual-arm coordinated inline robotic system integrating tuber grasping and tissue sampling mechanisms. Tubers are transported on a conveyor that halts when a YOLOv11-based vision system detects a tuber within the workspace of a one-prismatic-degree-of-freedom (P-DoF) robotic arm. This arm, equipped with a gripping end-effector, secures and positions the tuber for sampling. The second arm, a 3-P-DoF Cartesian manipulator with a biopsy punch-based end-effector, then performs tissue extraction guided by a YOLOv10-based vision system that identifies the sampling sites on the tuber such as eyes or stolon scars. The sampling involves four stages: insertion of the punch into the tuber, punch rotation for tissue detachment, biopsy punch retraction, and deposition of the tissue core onto a collection site. The system achieved an average positional error of 1.84 mm along the tuber surface and a depth deviation of 1.79 mm from a 7.00 mm target. The success rate for core extraction and deposition was 81.5%, with an average sampling cycle of 10.4 seconds. The total cost of the system components was under $1,900, demonstrating the system's potential as a cost-effective alternative to labor-intensive manual tissue sampling. Future work will focus on optimizing for multi-site sampling from a single tuber and validation in commercial settings.

cs.RO

Prediction of soil fertility parameters using USB-microscope imagery and portable X-ray fluorescence spectrometry

This study investigated the use of portable X-ray fluorescence (PXRF) spectrometry and soil image analysis for rapid soil fertility assessment, with a focus on key indicators such as available boron (B), organic carbon (OC), available manganese (Mn), available sulfur (S), and the sulfur availability index (SAI). A total of 1,133 soil samples from diverse agro-climatic zones in Eastern India were analyzed. The research integrated color and texture features from microscopic soil images, PXRF data, and auxiliary soil variables (AVs) using a Random Forest model. Results showed that combining image features (IFs) with AVs significantly improved prediction accuracy for available B (R2 = 0.80) and OC (R2 = 0.88). A data fusion approach, incorporating IFs, AVs, and PXRF data, further enhanced predictions for available Mn and SAI, with R2 values of 0.72 and 0.70, respectively. The study highlights the potential of integrating these technologies to offer rapid, cost-effective soil testing methods, paving the way for more advanced predictive models and a deeper understanding of soil fertility. Future work should explore the application of deep learning models on a larger dataset, incorporating soils from a wider range of agro-climatic zones under field conditions.

eess.IV