KrishivSeth AI-(AI for farmers and Agriculture)

KrishivSeth AI is an offline farming assistant that detects crop diseases, predicts risks,irrigation & fertilizer, analyzes market trends,& uses quantum-optimization to choose the most profitable crop

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KrishivSeth AI INSPIRATION STORY:

In many villages, farmers don’t fail because they lack effort — they fail because they lack predictive intelligence. They decide crops based on tradition, not data. They sell in mandis without knowing future price trends. They overuse water and fertilizers because advisory systems are generic, not hyper-local. After studying platforms like Crop in and nurture farm, we noticed something critical: They provide data, but not decision optimization. There is no system that answers: “What should this farmer grow, this season, on this land, to maximize net profit with minimal risk?” That gap inspired us to build an AI-driven agricultural profit engine — not just an advisory app, but a decision-making system that predicts ROI, optimizes inputs, and connects farmers directly to high-value buyers. We believe farming should move from guesswork to algorithmic precision.

KrishivSeth AI – AI for Agriculture Inspiration India is one of the largest agricultural economies, yet farmers face:

Late disease detection Unpredictable weather Poor market price awareness Excess fertilizer usage Lack of localized advisory Our mission is making AI accessible to every farmer using only a smartphone.

What it does Crop Disease Detection Farmers upload an image → AI predicts disease → treatment suggestion provided.

💡 What It Does 🌿 1. Crop Disease Detection (CNN Based) We classify plant leaf images using Convolutional Neural Networks. Image transformation: $$ I' = W * I + b $$ Convolution operation: $$ S(i,j) = (I * K)(i,j) = \sum_m \sum_n I(m,n)K(i-m,j-n) $$

Activation function (ReLU):

$$ ReLU(x) = \max(0,x) $$

Softmax classification:

$$ P(y=i|x) = \frac{e^{z_i}}{\sum_{j} e^{z_j}} $$

Cross-entropy loss:

$$ Loss = -\sum y \log(\hat{y}) $$

Python Model Code import tensorflow as tf from tensorflow.keras import layers, models

model = models.Sequential([ layers.Conv2D(32, (3,3), activation='relu', input_shape=(128,128,3)), layers.MaxPooling2D((2,2)), layers.Conv2D(64, (3,3), activation='relu'), layers.MaxPooling2D((2,2)), layers.Flatten(), layers.Dense(128, activation='relu'), layers.Dense(10, activation='softmax') ])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'] Accuracy calculation:

\( Accuracy = \frac{Correct\ Predictions}{Total\ Predictions} \)

🌦 Weather-Based Advisory

Evapotranspiration model: ET0=Δ+γ(1+0.34u2)0.408Δ(Rn−G)+γT+273900u2(es−ea)

Soil Moisture Index: SMI= Field Capacity Current Moisture

If SMI<0.5⇒Irrigation=Required Uses forecast data to suggest irrigation timing.

Example moisture estimation:

$$ Soil\ Moisture\ Index = \frac{Rainfall + Irrigation}{Evaporation + Crop\ Usage} $$

```const axios = require('axios');

async function getWeather(city) { const response = await axios.get( https://api.weatherapi.com/v1/current.json?key=API_KEY&q=${city} ); return response.data; }```

Market Price Intelligence

Displays mandi prices to optimize selling decisions.

Profit estimation formula:

$$ Profit = Selling\ Price - Production\ Cost $$ Using Linear Regression \y=β0+β1x1+β2x2+ϵ\ $$ MSE=n1i=1∑n(yi−y^i)2 $$

$$ θ:=θ−α∇J(θ) $$

```from sklearn.linear_model import LinearRegression

model = LinearRegression() model.fit(X_train, y_train)

predicted_price = model.predict(X_test)```

🧪 Fertilizer Optimization

Recommends fertilizer quantity based on soil nutrients.

\( Recommended\ Fertilizer = Required\ Nutrient - Available\ Nutrient \)

  1. AI Decision Engine Using Logistic Regression $$ P(Disease)=1+e−z1

    z=w1x1+w2x2+b

    P(A∣B)=P(B)P(B∣A)P(A) $$

System Architecture

Farmer → Mobile App → API Gateway → AI Model → Decision Engine → Recommendation

Time Complexity: $$ CNN Training :O(n⋅d⋅k2) , Inference : O(d⋅k2) $$

Multilingual Voice Support

Speech-to-text and text-to-speech integration for accessibility.

How we built it

Frontend

  • Flutter / React Native

AI Model

  • CNN-based classifier
  • Built using TensorFlow / PyTorch

Loss function:

$$ Loss = -\sum y \log(\hat{y}) $$

Backend

  • Node.js / Firebase
  • REST APIs for weather and mandi data

⚔ Challenges we ran into

  1. Low-quality image inputs
  2. Limited dataset for regional crops
  3. Ensuring safe fertilizer recommendations
  4. Real-time API integration
  5. UI simplicity for rural users

Regularization : Lreg=L+λ∣∣w∣∣2

Dropout: y=f(Wx)⋅mask

Performance Metrics

Precision: Precision=TP/TP+FP

Recall=TP/TP+FN

F1 Score: F1=2⋅Precision+RecallPrecision⋅Recall

Quantum Farming Mathematical Layer

Quantum State of Farm Conditions Represent farm condition as superposition: ∣ψ⟩=α∣Yield⟩+β∣Profit⟩+γ∣Water⟩+δ∣Risk⟩

Constraint: ∣α∣2+∣β∣2+∣γ∣2+∣δ∣2=1 This models uncertainty in:

Weather

Soil

Market

Quantum Decision Probability Probability of selecting decision P(Di)=∣⟨Di∣ψ⟩∣2

System chooses highest probability farming strategy.

Quantum Optimization Objective Instead of single optimum: min H where Hamiltonian : H=w1Yield+w2Profit−w3Risk−w4Water

Quantum Annealing Inspired Optimization Energy minimization: E=i,j∑Jijxixj+i∑hixiUsed to find:

✔ Best crop ✔ Best irrigation ✔ Best fertilizer combination

Quantum-Inspired Code Layer :

Quantum Decision Simulator ;

import numpy as np

def quantum_decision(yield_val, profit, risk, water): state = np.array([yield_val, profit, -risk, -water]) norm = np.linalg.norm(state) psi = state / norm

probabilities = psi**2 return probabilities Quantum Optimization Score : def hamiltonian(yield_val, profit, risk, water): w = [0.4, 0.3, 0.2, 0.1] return w[0]*yield_val + w[1]*profit - w[2]*risk - w[3]*water

Quantum-inspired Best Plan Selection : best_plan = min(plans, key=lambda x: hamiltonian(*x))

Future Work:

LSTM for time-series price prediction:ht = σ(Whht−1+Wxxt) Reinforcement Learning for irrigation control:Q(s,a)=Q(s,a)+α[r+γmaxQ(s′,a′)−Q(s,a)]

Accomplishments that we're proud of

  • High validation accuracy
  • Working end-to-end prototype
  • Voice interaction support
  • Scalable backend architecture

What we learned

  • Dataset quality directly impacts model performance
  • Simplicity improves adoption
  • Real-world deployment constraints differ from lab environments

What's next for KrishivSeth AI

Phase 1 – Pilot Deployment

  • Field testing with farmers

Phase 2 – Advanced AI

  • Soil nutrient prediction
  • Pest outbreak forecasting

Outbreak prediction model:

$$ P(Disease) = \frac{1}{1 + e^{-z}} $$

Phase 3 – Scale

  • Government partnerships
  • Nationwide deployment

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