Early detection and management of crop diseases and pest infestations
Government Of Maharashtra · Agriculture, FoodTech & Rural Development · Software
The classifier is the commodity everyone builds — if you take this, the weather-driven forecast and the expert feedback loop must be your headline, because a leaf-photo demo alone puts you in the most crowded category at the entire hackathon.
What it actually is
Farmers usually notice a disease or pest only after damage has spread, and expert diagnosis is rarely available in time. Weather, crop stage and local pest history all shape the risk but never get combined into a farm-level warning. The ask is a system that identifies problems from images, forecasts risk from conditions, and gives locally relevant treatment advice.
What to build
A crop health system combining image-based symptom identification from a farmer's photograph with a weather-driven risk forecast that uses crop stage, variety and local pest history to predict infestation pressure days ahead, a geospatial hotspot map aggregating confirmed cases across a block so neighbouring farmers are warned, an expert validation loop where extension staff confirm or correct a diagnosis and that confirmation feeds back into the model, and multilingual advisories recommending integrated pest management actions with safe dosage rather than defaulting to pesticide.
Smallest thing that wins the room
Photograph an infected leaf and get an identification with confidence and an integrated pest management advisory in Marathi, then show the block-level hotspot map where three confirmed cases this week have raised the risk forecast for surrounding farms.
How crowded this one gets
A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.
Quieter than 5% of the 226 · #215 of 226 by expected field · reaches the 500 cap
Forecast to blow past the 500-idea cap. Submissions close when it fills, so late teams may not get in at all.
Why: state governments drew the biggest crowds in 2025.
This is a guess, not a fact
Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.
The scores
The number is the shorthand. The line under it is the reason.
Acceptance potential
2/5Crop disease detection from leaf images is possibly the single most cloned student ML project in existence — a judge will have seen it many times, PlantVillage-trained models notoriously collapse on real field photographs, and the differentiating features are exactly the ones teams skip.
Feasibility
5/5PlantVillage and several successor datasets provide tens of thousands of labelled crop disease images, weather APIs are free, and the whole stack is ordinary application work — this is among the most data-rich problems in the entire set.
Innovation scope
2/5Image-based crop disease classification is one of the most solved and most reproduced applications in machine learning, and the description prescribes the surrounding features, leaving room only in the risk forecasting and the feedback loop.
Clarity
3/5The expected capabilities are listed clearly but no crops, diseases or region are specified, so you must choose the scope and no acceptance bar is given for diagnostic accuracy.
Effort
HeavyThe classifier is quick given public data, but the risk forecasting, hotspot aggregation, expert validation loop and multilingual advisory layer together make a substantial application.
Demo-ability
EasyPhotographing a leaf and receiving an instant diagnosis is a tactile, self-explanatory demo that works with a real infected plant brought into the room.
In its favour
- Green flag: Public labelled disease image datasets are enormous, so the classifier is working within hours and your time goes to the differentiating features
- Green flag: Weather-driven risk forecasting using degree-day and humidity thresholds is established agronomy and is the part almost nobody builds
- Green flag: The expert validation loop turning extension officer confirmations into training data is a genuinely good idea that addresses the field-accuracy problem head-on
- Green flag: Recommending integrated pest management rather than defaulting to pesticide is explicitly asked for and shows agronomic literacy
Against it
- Red flag: This is the most reproduced student machine learning project there is, so a leaf classifier alone is indistinguishable from dozens of prior submissions
- Red flag: Models trained on PlantVillage's uniform laboratory backgrounds degrade severely on real field photographs with mixed foliage and variable light, and an agriculture judge will hand you a real photo to test it
- Red flag: Misdiagnosis leads to wrong pesticide application, which the description itself names as a harm, so confidence handling and referral to an expert are requirements rather than extras
- Red flag: The description asks for detection, forecasting, mapping, validation and advisory, and teams routinely build only the classifier
What you will be writing
- PlantVillage / PlantDoc disease classification datasets
- EfficientNet-Lite or MobileViT on-device inference
- Weather-driven disease risk models (degree-day / humidity thresholds)
- PostGIS block-level hotspot aggregation
- Bhashini multilingual advisory generation
- TFLite offline mobile app
- Crop protection
- Agricultural advisory
- Image classification
Prior art to read before you start
image-based crop disease diagnosis · weather-driven pest risk forecasting · geospatial outbreak hotspot mapping
Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.