README.md

metadata

title: Crop Adv
emoji: 🌍
colorFrom: blue
colorTo: yellow
sdk: gradio
sdk_version: 6.8.0
app_file: app.py
pinned: false
license: mit

🌾 Maharashtra Crop Recommendation System

An AI-powered crop advisory tool that recommends the most suitable crops for a Maharashtra farmer based on their soil chemistry, local weather, district & season, water supply, and budget — and explains why each crop is recommended in plain language.

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🧠 How It Works: The Full Pipeline

Step 1 — Two Models Run in Parallel

When you click Find Best Crops For Me, two independent models are run simultaneously:

Model 1 — Soil & Climate AI (Random Forest)

• Takes 7 numerical inputs: N, P, K, Temperature, Humidity, pH, Rainfall
• Runs them through a pre-trained Random Forest Classifier (model1_npk.pkl)
• Outputs a probability score (0–1) for every crop it knows (22 crops)
• Example: {"rice": 0.42, "maize": 0.28, "cotton": 0.08, ...}
• The label encoder (model1_label_encoder.pkl) maps numeric class indices → crop names
• Training: 2,200 samples × 22 crops, 99.6% cross-validation accuracy (5-fold stratified)

Model 2 — Regional History Scoring (Weighted Formula)

• Looks up the selected District + Season combination in model2_full_scored.csv
• Returns a pre-computed suitability score for each crop based on 26 years (1997–2022) of Maharashtra district-level crop production data
• No ML here — this is a transparent, rule-based scoring formula:

Suitability Score =
  0.35 × Crop Frequency %          (how often this crop is grown here)
+ 0.25 × Historically Grown Flag   (grown in ≥50% of years = 1, else 0)
+ 0.20 × Normalised Avg Yield      (yield relative to best crop in district)
+ 0.10 × Dominance Rate            (share of total district area)
+ 0.05 × Recent Years Grown        (grown post-2015 = recency bonus)
+ 0.05 × Yield Stability           (lower variance = higher score)

• Covers 35 districts × 4 seasons × 33 crops = 1,603 scored combinations

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Step 2 — Scores Are Combined

Final Score = 0.60 × Model1_probability + 0.40 × Model2_suitability_score

• Model 2 scores are normalised to 0–1 before combining (divide by max score in that district/season)
• Crops are then ranked highest → lowest by Final Score
• Only crops present in CROP_META (hardcoded dictionary, see below) are kept

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Step 3 — Budget & Land Filtering

For each ranked crop:

Condition	Result
input_cost × land_ha ≤ budget	budget_status = "within" — shown fully
input_cost × land_ha > budget	affordable_ha = budget / input_cost — shown with partial land warning
affordable_ha < 0.1 ha	Crop is skipped entirely

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Step 4 — Risk Scoring (4-Dimensional)

Every crop gets a risk score from 0–100 across four dimensions, then averaged equally:

Dimension	Weight	How It's Calculated
Weather Risk	25%	Penalises extreme temp (<8°C or >42°C), extreme humidity, and rainfall mismatches vs crop water need
Market Risk	25%	Based on hardcoded demand: High=20, Medium=40, Low=65
Budget Risk	25%	Ratio of total_cost / budget_max mapped to risk bands
Water Risk	25%	Gap between crop's irrigation need vs farmer's available supply

Irrigation need levels (hardcoded per crop):

• "high" — Rice, Sugarcane, Banana, Grapes, Jute, Apple
• "medium" — Cotton, Maize, Wheat, Onion, Tomato, etc.
• "low" — Chickpea, Bajra, Jowar, Pulses, Oilseeds, etc.

Irrigation supply levels (farmer input):

• Assured (Canal / River) = 3
• Borewell / Pump = 2
• Seasonal / Rain-fed = 1
• No Irrigation (Dryland) = 0

Risk label thresholds:

• Score < 35 → 🟢 Low
• Score 35–64 → 🟡 Medium
• Score ≥ 65 → 🔴 High

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Step 5 — Confidence Labelling

Each result is tagged with an AI confidence level based on Model 1's raw probability:

Model 1 Score	Label	Meaning
≥ 35%	High confidence	Soil & climate strongly match
12–34%	Moderate confidence	Partial soil match, regional history confirms
< 12%	Based on regional history	Driven primarily by Model 2

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Step 6 — "Why We Recommend This" Explanation

Every card generates 5 plain-language bullet points dynamically from real data — nothing is templated or hardcoded:

Bullet	Data Source
Soil fit	m1_score from Model 1 probability
Season fit	season_match boolean (best_season == selected season)
Water fit	Irrigation gap calculation (need − available)
Risk summary	risk label + highest risk_breakdown dimension

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📥 Inputs

Location & Season

Input	Type	Options
District	Dropdown	35 Maharashtra districts
Season	Dropdown	Kharif (Jun–Sep), Rabi (Oct–Mar), Summer (Apr–Jun), Whole Year

Farm Details

Input	Type	Range
Land (Hectares)	Number	0.1 – 100
Total Budget (₹)	Number	₹1,000+
Water source	Radio	Assured / Borewell / Seasonal / Dryland

Soil Test Results (from Krishi Vigyan Kendra or soil lab)

Input	Range	Unit
Nitrogen (N)	0 – 300	mg/kg
Phosphorus (P)	0 – 300	mg/kg
Potassium (K)	0 – 300	mg/kg
Soil pH	3.5 – 9.5	—

Local Weather

Input	Range	Unit
Avg Temperature	5 – 50	°C
Avg Humidity	10 – 100	%
Annual Rainfall	20 – 3000	mm/year

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📤 Output: What Each Card Shows

Element	Source	Notes
Crop name + medal	Combined score ranking	🥇🥈🥉 for top 3
Best season	CROP_META hardcoded	e.g. "Kharif", "Rabi", "Whole Year"
Days to harvest	CROP_META hardcoded	Approximate growing period
Budget badge	Budget filter logic	✅ Fits / ⚠️ Partial
Risk badge	Risk score calculation	Low / Medium / High
"Why we recommend this"	Live data from both models	4 dynamic bullet points
Irrigation verdict	Irrigation gap formula	✅ / ⚠️ / ❌
Cost overview	CROP_META × land_ha	Input cost per ha and total
Market demand	CROP_META hardcoded	High / Medium / Low
Risk breakdown bars	4-dimensional risk formula	0–100 per dimension
Model confidence	Model 1 probability	High / Moderate / Regional
Score breakdown	Both model scores	Soil %, Regional %, Combined %

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🗄️ What Is Hardcoded (CROP_META)

The following per-crop values are static estimates based on Maharashtra Agriculture Department averages. They are not fetched from any live source:

Field	Description	Example (Rice)
input_cost	Estimated cost to grow 1 hectare (₹)	₹30,000
yield_qtl	Expected yield in quintals per hectare	25 qtl/ha
demand	Market demand category	"High"
days	Days from sowing to harvest	120 days
best_season	Ideal growing season	"Kharif"

Full crop list in CROP_META (50 crops): Rice, Maize, Chickpea, Kidneybeans, Pigeonpeas, Mothbeans, Mungbean, Blackgram, Lentil, Pomegranate, Banana, Mango, Grapes, Watermelon, Muskmelon, Apple, Orange, Papaya, Coconut, Cotton, Jute, Coffee, Arhar/Tur, Bajra, Castor Seed, Gram, Groundnut, Jowar, Linseed, Moong (Green Gram), Niger Seed, Onion, Other Cereals, Other Kharif Pulses, Other Rabi Pulses, Other Summer Pulses, Ragi, Rapeseed & Mustard, Safflower, Sesamum, Small Millets, Soyabean, Sugarcane, Sunflower, Tobacco, Tomato, Urad, Wheat, Other Oilseeds, Cotton(Lint)

⚠️ Note: input_cost, yield_qtl, and demand are estimates. Actual values vary by year, variety, and farming practices.

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🗂️ Files in This Space

File	Description
app.py	Main Gradio application — all logic lives here
model1_npk.pkl	Trained Random Forest classifier (soil & climate, 22 crops)
model1_label_encoder.pkl	Label encoder — maps class indices → crop names
model2_full_scored.csv	Pre-computed regional suitability scores (35 districts × 4 seasons × 33 crops)
requirements.txt	Python dependencies
README.md	This file

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📦 Requirements

gradio>=4.0.0
joblib
numpy
pandas
scikit-learn

⚠️ scikit-learn must be listed even though it is never directly imported — joblib needs it to deserialise the .pkl model files.

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🏗️ System Architecture

Farmer Inputs (soil + weather + location + farm details)
        │
        ├──► Model 1 (Random Forest)
        │    model1_npk.pkl + model1_label_encoder.pkl
        │    → probability score per crop (22 crops)
        │
        ├──► Model 2 (Regional Scoring Table)
        │    model2_full_scored.csv
        │    → suitability score per crop (33 crops, filtered by district + season)
        │
        └──► Combined Score (60% Model 1 + 40% Model 2)
                    │
                    ▼
             Ranked crop list
                    │
                    ├──► Budget filter (skip if < 0.1 ha affordable)
                    ├──► Risk scoring (weather + market + budget + water)
                    ├──► Confidence labelling (based on Model 1 score)
                    └──► "Why" explanation (dynamically generated from real data)
                                │
                                ▼
                       HTML output cards (mobile-first UI)

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🌱 Crops by Model Coverage

Model 1 — Soil/Climate (22 crops): Apple, Banana, Blackgram, Chickpea, Coconut, Coffee, Cotton, Grapes, Jute, Kidneybeans, Lentil, Maize, Mango, Mothbeans, Mungbean, Muskmelon, Orange, Papaya, Pigeonpeas, Pomegranate, Rice, Watermelon

Model 2 — Regional (33 crops): Arhar/Tur, Bajra, Banana, Castor Seed, Cotton, Gram, Grapes, Groundnut, Jowar, Linseed, Maize, Mango, Moong (Green Gram), Niger Seed, Onion, Other Cereals, Other Kharif Pulses, Other Rabi Pulses, Other Summer Pulses, Ragi, Rapeseed & Mustard, Rice, Safflower, Sesamum, Small Millets, Soyabean, Sugarcane, Sunflower, Tobacco, Tomato, Urad, Wheat, Other Oilseeds

Crops in both models get a fully blended score. Crops in only one model get a 0 from the missing model but are still shown if the other model scores them highly.

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📊 Data Coverage

	Details
State	Maharashtra, India
Districts	35
Seasons	Kharif (Jun–Sep), Rabi (Oct–Mar), Summer (Apr–Jun), Whole Year
Historical data range	1997 – 2022 (26 years)
Regional data source	Maharashtra district-level crop production statistics
Soil model training data	2,200 samples across 22 crops (100 per crop)
Soil model accuracy	99.6% cross-validation (5-fold stratified)

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🏷️ Suitability & Risk Labels

Regional Suitability (Model 2 raw score)

Label	Score Range	Meaning
🟢 Highly Suitable	≥ 75%	Strong regional farming history
🟡 Moderately Suitable	50–74%	Good candidate, moderate history
🟠 Low Suitability	30–49%	Possible but limited evidence
🔴 Not Recommended	< 30%	Little or no regional history

Overall Risk (combined score)

Label	Score Range	Action
🟢 Low	< 35	Safe choice for this season
🟡 Medium	35–64	Proceed with planning for main risk factor
🔴 High	≥ 65	Consider reducing land allocation

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👨‍💻 Built For

Maharashtra farmers and agricultural advisors. Designed to be used on mobile phones in the field — large text, simple language, clear YES/NO verdicts, with detailed breakdown available on demand.

Hackathon criteria addressed:

• ✅ Integration of multiple data inputs (soil, weather, water, location, budget)
• ✅ Crop suitability prediction via dual-model AI pipeline
• ✅ Risk-aware scoring with 4-dimensional uncertainty handling
• ✅ Clear, simple recommendation outputs with plain-language explanations
• ✅ Farmer-friendly, mobile-responsive interface
• ✅ Scalable architecture (local model files, no external API dependencies)