KrishivSeth AI-(AI for farmers and Agriculture)

KrishivSeth AI is an offline-first farming assistant that detects crop diseases,predicts outbreaks, fertilizer, gives smart irrigation advice,shows mandi price trends, and supports multilingual voice

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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

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 ,SQLite (Offline-first design)

AI Model

  • CNN-based classifier
  • Built using TensorFlow / PyTorch TensorFlow Lite (on-device), PyTorch (training), OpenCV Backend: Node.js, Express, Firebase Firestore

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

Backend

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

Database: Firestore (Cloud), SQLite (Local cache) DevOps: Docker, GitHub Actions, Firebase Hosting Security: JWT Authentication, Encrypted Storage

⚔ 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

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 )

Mathematical Modeling

  1. Crop Yield Optimization Model: Yield = f(Soil, Weather, Seed, Fertilizer, Irrigation) Objective: Maximize (Yield − Cost)

  2. Convolution Operation (Disease Detection CNN): S(i,j) = Σm Σn I(m,n)K(i−m,j−n)

  3. Softmax Probability Function: P(y=i|x) = e^z_i / Σj e^z_j

  4. Cross-Entropy Loss Function: Loss = − Σ y log(ŷ)

  5. Fertilizer Optimization Model: Required_N = Target_Yield × Nutrient_Factor Deficiency = Required − Available

  6. Logistic Regression (Disease Outbreak Prediction): P(Disease) = 1 / (1 + e^(−z))

  7. Profit Estimation: Profit = Selling Price − Production Cost Coding Implementation Sample CNN Model (TensorFlow):

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'])

Sample Market Price Prediction (Linear Regression):

from sklearn.linear_model import LinearRegression

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

predicted_price = model.predict(X_test)

  1. Mathematical Framework

A. Crop Disease Detection (CNN Model) Convolution Operation:

S(i,j) = Σm Σn I(m,n)K(i−m,j−n) ReLU(x) = max(0, x) Softmax: P(y=i|x) = e^z_i / Σj e^z_j Cross-Entropy Loss: Loss = − Σ y log(ŷ) Accuracy = (TP + TN) / (TP + TN + FP + FN) B. Pest Outbreak Probability (Logistic Regression) P(Disease) = 1 / (1 + e^(−z)) z = w1x1 + w2x2 + b

C. Smart Irrigation Model (Evapotranspiration)

ET0 = [0.408Δ(Rn − G) + γ(900/(T+273))u2(es − ea)] / [Δ + γ(1 + 0.34u2)] Decision: If Soil Moisture < Threshold → Irrigation Required D. Fertilizer Optimization Minimize Cost(N, P, K) Constraints: N ≥ Nmin, P ≥ Pmin, K ≥ Kmin Deficiency = Required − Available E. Market Price Prediction (Linear Regression) y = β0 + β1x1 + β2x2 + ε MSE = (1/n) Σ(y − ŷ)^2 θ = θ − α∇J(θ) F. Reinforcement Learning (Future Scope) Q(s,a) = Q(s,a) + α[r + γ max Q(s',a') − Q(s,a)]

  1. Coding Implementation CNN Disease Detection (TensorFlow)

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.Dropout(0.5), layers.Dense(10, activation='softmax') ])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Market Price Prediction (Scikit-Learn)

from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error

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

predictions = model.predict(X_test) mse = mean_squared_error(y_test, predictions) print("MSE:", mse)

Weather API Integration (Node.js)

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; }

Fertilizer Optimization Function (Python)

def fertilizer_recommendation(required, available): deficiency = required - available return max(deficiency, 0)

recommended_n = fertilizer_recommendation(120, 80) print("Recommended Nitrogen:", recommended_n)

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