This paper proposes a non-destructive technique for silk content estimation in cocoons. The price of a cocoon is determined by the silk content which is determined manually by visual inspection or feeling the toughness of the cocoon shell. The above methods are subjective, non-repeatable and prone to human error. With such non-transparent conventional methods of silk estimation, the buyers and sellers are unhappy over any transaction. Our proposed non-destructive technique uses soft x-ray image analysis technique backed up by soft computing algorithm to estimate silk content. Advance image processing and analysis techniques have been applied to extract morphological features from the x-ray images of the cocoons and features are fed to GRNN to estimate the silk content. Total 594 tasar cocoons have been analyzed with the developed solution and the results have been validated with human experts. Accuracy of the system for silk content estimation has been calculated as more than 85%.

An important aspect of silkworm seed production is to ensure Pebrine disease free eggs. For that reason, the egg-laying moths are cut and their tissues are examined under microscope for presence of Pebrine spores in those tissues. If the tissues are found free of infection, then only the corresponding eggs are distributed amongst the villagers pursuing sericulture. Currently the entire process is manual, time and labor intensive. Many a time human error also creeps in leading to outbreak of Pebrine disease. This paper proposes automation of the Pebrine spore detection process by capturing photomicrographic images and classifying Pebrine spores using digital image processing technique thereby improving productivity and accuracy of this process. Captured RGB image has been enhanced by image enhancement process to get better processing result in further steps. Local threshold based segmentation technique has been applied to segment the foreground objects. The segmented foreground objects have been labeled individually by a stack-based connected-component labeling technique. Then advanced binary morphological technique based feature detection procedures have been performed to remove the unwanted noise and non-Pebrine objects and to extract various feature parameters of filtered Pebrine objects. Initially, more than 200 images have been analyzed using developed solution & the results have been validated with the human experts. Laboratory experiments found the accuracy of detection in the tune of 75%.