Mr. Pravin Kolhe will present his Ph.D. Pre-synopsis presentation as per the details below:

Date: 7th August 2026 (Friday)
 

Time: 1130 – 1300 hrs.
 

Venue: C-TARA Conference Room No.1

Supervisor: Prof. Pennan Chinnasamy
 

External Supervisor: Dr. Sanjay Belsare

RPC Members: Prof. Priya Jadhav, Prof. Raaj Ramsankaran

Title: "Synergized Mapping™ of Agriculture Area and Crops Using Satellites, Drones and Crowd Sourcing – A Case Study in Maharashtra"

Abstract:

Agriculture supports more than half of India's workforce and consumes nearly 84% of the country's utilizable water resources. However, agricultural planning continues to rely on crop statistics that are often delayed, incomplete, and inaccurate. Conventional government records frequently underestimate cultivated area, resulting in incorrect assessment of crop water requirements, irrigation planning, agricultural revenue, and policy decisions. This research addresses this critical gap by developing a Synergized Mapping™ Framework, which integrates government administrative records, farmer-generated crowdsourced data, satellite imagery, and high-resolution drone surveys through a structured iterative validation process.

A systematic PRISMA 2020-based review of 350 international research studies established that no single crop mapping technique simultaneously satisfies the requirements of accuracy, timeliness, scalability, and affordability. Based on these findings, a village-level case study was conducted at Loni Devkar Village, Indapur, Maharashtra, over two agricultural seasons (2021–22 and 2022–23). Comparative analysis demonstrated that satellite, drone, and crowdsourced datasets consistently estimated the cultivated area to be 1.6–2.0 times higher than official records.

The proposed Synergized Mapping™ Framework employs a seven-step iterative validation algorithm that escalates from low-cost to high-cost data sources only where discrepancies exist, thereby optimising both accuracy and operational cost. The study demonstrates that integrating multiple complementary data sources substantially improves crop area estimation while remaining scalable from village to state and national levels. The research makes three major contributions: (i) the first comprehensive quantitative comparison of records, satellite imagery, drone surveys, and crowdsourced crop mapping in an Indian smallholder agricultural context; (ii) the development and validation of an operational, cost-optimised Synergized Mapping™ Framework with explicit decision rules for multi-source data integration; and (iii) establishing a direct linkage between crop mapping accuracy and village-scale water budgeting, thereby providing an evidence-based framework for improving agricultural planning, irrigation management, and policy formulation in India.