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Smart farming, food supply chain, and agricultural innovation
6 Problem Statements in this Domain (5 Standard + 1 Open Innovation)
Aflatoxin contamination in stored wheat, maize, and groundnut causes major economic loss and serious health risk (liver damage, carcinogenicity), and is a leading cause of grain rejection during procurement and export. Current detection relies on ELISA/HPLC lab testing that requires sample transport, trained technicians, and hours-to-days of turnaround — completely impractical for screening thousands of tonnes moving through mandis and warehouses daily during peak procurement season. Contamination is also highly heterogeneous within a single storage lot (hotspot pockets caused by localized moisture ingress), meaning a handful of lab samples routinely miss dangerous pockets that later spoil an entire consignment.
Over-irrigation and under-irrigation both damage fruit yield and quality, and groundwater depletion is forcing farmers toward strict water budgets. True root-zone moisture (30–90 cm depth, depending on crop and age) is the variable that matters for irrigation decisions, but it cannot be measured cheaply at scale — buried capacitance/tensiometer probes are expensive, fragile, require per-tree calibration, and are frequently damaged by tillage, rodents, or theft of cabling. Farmers currently irrigate on fixed schedules or shallow surface-moisture proxies, both of which are poor predictors of actual root-zone water availability, especially in heterogeneous soils with varying clay content.
Milk adulteration (water, urea, detergent, starch, melamine, vegetable oil) is a widespread food-safety and economic-fraud problem in India's dairy supply chain. Village-level collection centres currently test only for basic fat/SNF content using simple analyzers; multi-adulterant detection requires laboratory-grade chemistry that is unavailable at the point of collection, meaning adulterated milk often enters the supply chain undetected until much later — if at all. A workable solution must detect multiple, chemically dissimilar adulterants simultaneously, function within the few minutes available during collection, and be operable by a collection-centre worker with minimal training.
Locust and other migratory pest swarms can devastate standing crops within hours of arrival, and effective control depends on predicting where a swarm will move next — but ground-truth data is extremely sparse (scattered farmer reports of unknown reliability, patchy field survey coverage) and satellite-derived vegetation/wind data is indirect and noisy. Existing swarm models rely heavily on wind-drift physics but struggle to incorporate real-time, uncertain, and delayed observational data from the ground. The problem is compounded by cross-border movement (locust swarms frequently originate outside India), meaning the system must reason under significant data gaps rather than assuming complete observability.
Fruit crops with high bruise-sensitivity and non-uniform ripening (mango, guava) are almost entirely hand-harvested in India due to lack of affordable selective mechanical harvesting, driving up labor cost and dependency on scarce skilled labor during short harvest windows. Existing mechanical harvesters (shaker types used abroad) are designed for uniform ripening crops on flat, trellised orchards and cause unacceptable bruising and indiscriminate harvesting of unripe fruit — unsuitable for Indian orchard layouts, tree architectures, and terrain. A viable system must identify ripeness non-destructively, handle irregular tree canopy shapes and uneven ground, and detach fruit without bruising — a combined perception, manipulation, and mechanical design challenge.
Have your own innovation in Agriculture & Food Technology? Propose and build your team's unique software or hardware solution addressing smart farming, precision irrigation, crop disease diagnostics, cold-chain preservation, agri-fintech, farm-to-table traceability, or sustainable rural tech.