Technology Development Projects

#1 Portable tool for diabetic retinopathy detection

(Project is supported by DRISHTI CPS under CHANAKYA PhD Fellowship)

Diabetic Retinopathy (DR) is a common cause of preventable blindness, affecting approximately 20% of India’s 25 million diabetics, with the majority of cases occurring in rural areas. However, the majority of DR treatment facilities are in cities. To identify DR patients who require intervention, all DR patients require regular check-ups, which are currently only available in medical colleges and come at a high cost for rural patients. To address this issue, a group is working on a portable imaging system linked to a smartphone app that will deliver DR biomarker results to rural clinics. The technology is based on automated data-driven modelling to screen DR patients (no DR or mild DR) and identify them for further evaluation and treatment with high reliability. Beta version of the technology is being tested in five districts of West Bengal with the help of health care workers, primary care clinics, medical schools, and corporate hospitals.

#2 A low-Cost Intelligent System for Road Monitoring and Maintenance for Indian roads

(Project is supported by DRISHTI CPS under CHANAKYA Chair Professorship)

The state of a country’s roads and infrastructure is critical to its economic development, and India aspires to have one of the best road networks in the world in order to increase GDP and progress towards becoming a developed nation. Maintenance, automation, and transparency are critical to achieving this goal in such a large network. Poorly maintained roads can impede mobility, slow economic growth, raise vehicle operating costs, and have an impact on safety and comfort. A self-contained, integrated solution that includes inspection, data collection, modelling, and project management can optimise road and infrastructure maintenance while lowering costs and improving quality. A low-cost integrated solution for intelligent road maintenance and monitoring is being developed specifically for Indian road conditions. The system is being built on an IoT platform with sensors and optimised communications and networking modules for distress detection and classification, as well as 3D/LiDAR vision and other sensor data augmentation technologies. With recent government policies emphasising road and infrastructure maintenance, DRISHTI CPS is assisting in the commercialization of this technology and contributing to India’s economic growth.

#3 An Automated Tool for Job Shop Scheduling

(Project is supported by DRISHTI CPS under its various schemes)

When seen through the lens of industrial operations, we are faced with a myriad of problems, some of which include new orders, processing backlogs, machine breakdowns, a lack of raw materials, equipment, or workers, and a great number of other unanticipated events. Because of this, a real world Shop Scheduling Problem, also known as the JSP, is considered to be one of the most difficult manufacturing challenges in the body of academic research. Moreover, achieving quick and  automatic scheduling is a requirement of Industry 4.0, and this must be done based on information about the available machines, the capabilities of those machines, the parts to be made, and the current loading. The technology that was developed by the cohort offers a solution that is all-encompassing, responsive, and efficient for a variety of job Shop scenarios. This web service for multi-strategy job scheduling uses a variety of heuristics and metaheuristics for problem resolution, taking into consideration one or more performance measurements, and applying those considerations to a variety of industrial contexts. The primary goal of this tool is to provide a complete and innovative solution for the Job Shop Scheduling Problem appropriate for MSMEs. A beta version of the tool was jointly presented by the collaborators at the AceMicromatic Group pavilion at the IMTEX 2023 trade show held at Bengaluru.

#4 An indigenous multi-sensor embedded disease warning system to monitor crop health

(Project is supported by DRISHTI CPS under CHANAKYA PhD Fellowship)

Many diseases and insects pose a grave threat to agricultural crops and harvests. These attacks can affect crop quality, putting the livelihoods of farmers at risk. Advanced disease detection systems are essential for minimising agricultural damage, maximising crop yield, and reducing the occurrence of diseases. This technology enables farmers to take proactive measures to protect their crops by focusing on early disease detection. This indigenous multi-sensor embedded disease warning system combines sensor- and image-based approaches into a single artificial intelligence/machine learning model using orthogonal measurements. The system generates time series data from the sensor and self-collected images for precise measurements. The ability to remotely monitor field parameters and establish the cause of leaf wetness, soil moisture and temperature, ambient humidity, and ambient temperature provides insights into crop health that farmers may utilise to improve irrigation and disease control strategies. The whole prototype is undergoing real-time testing for crop production systems, new generations of farmers, the agri-food business, and startups selling cutting-edge precision farming equipment.