AI proposal analyzer

Automated quality evaluation across seven criteria. This is decision support for faculty — the final selection is made by the panel in the offline round.

Demo data

KrishiScan — Offline AI Crop Disease Diagnosis

Team Innovexa · SIH1601

Under AI Analysis
86Overall

Analysed 28 Jul 2026, 10:20 am

Shortlisted

Evaluation breakdown

Problem Understanding90/100
Innovation84/100
Feasibility88/100
Technical Strength87/100
Impact89/100
Scalability82/100
Clarity84/100

AI summary

A technically credible and well-scoped agricultural AI proposal with a genuine differentiator in offline on-device inference. The main gap is evaluation rigour — the team should quantify model performance and address misdiagnosis risk before the presentation round.

Strengths

3
  • Clear articulation of the offline-first constraint and a concrete technical answer to it (quantised 6.8 MB model).
  • Strong dataset strategy — locally collected Pune-district images meaningfully differentiate this from generic PlantVillage solutions.
  • Measurable impact claim tied to a specific farm size rather than vague social benefit.

Weaknesses

3
  • Model accuracy figures for the locally collected dataset are not reported.
  • Voice output layer is described but no language coverage or TTS approach is specified.
  • No discussion of what happens when the model is uncertain — false diagnosis risk is unaddressed.

Recommendations

3
  • Add a confidence threshold with a 'consult KVK officer' fallback for low-confidence predictions.
  • Report top-1 / top-5 accuracy and confusion between visually similar diseases.
  • Include a cost-of-deployment estimate per 1,000 farmers to strengthen the scalability section.

Missing information

3
  • Model accuracy metrics
  • Risk & mitigation table
  • Data privacy note for uploaded farm images
JSPM Group Internal SIH Portal (JGI-SIH) · Prototype with demo data · Final selection is made by the faculty panel in the offline presentation round.