flask backed added

Soil Analyzer and Optimized Crop Finder - IT20233808/RP-MobileAPP-FE/components/common/Home.js

@@ -2,8 +2,9 @@ import React from 'react';
 import { View, Text, TouchableOpacity, StyleSheet, ImageBackground } from 'react-native';
 
 const HomePage = ({ navigation }) => {
+
   const handleOptionSelect = (option) => {
-    // Handle navigation to the selected option
+
     if (option === 'pestDisease') {
       navigation.navigate('PestDiseasePage');
     } else if (option === 'soilData') {

Soil Analyzer and Optimized Crop Finder - IT20233808/soil-component-BE/.vscode/launch.json

+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "justMyCode": true
+        }
+    ]
+}
\ No newline at end of file

Soil Analyzer and Optimized Crop Finder - IT20233808/soil-component-BE/app.py

+from flask import Flask, request, jsonify
+import csv
+
+app = Flask(__name__)
+
+class Crop:
+    def __init__(self, name, nutrient_requirements):
+        self.name = name
+        self.nutrient_requirements = nutrient_requirements
+
+# Function to calculate the suitability score for a crop
+def calculate_suitability_score(crop, user_soil_nutrients, optimization_criteria):
+    score = 0
+    for nutrient in user_soil_nutrients:
+        nutrient_value = user_soil_nutrients[nutrient]
+        required_value = crop.nutrient_requirements.get(nutrient, 0)
+        weight = optimization_criteria[nutrient]
+        difference = abs(nutrient_value - required_value)
+
+        if required_value == 0:
+            if difference == 0:
+                suitability = 100.0
+            else:
+                suitability = 0.0
+        else:
+            suitability = (1 - difference / required_value) * 100.0
+
+        weighted_suitability = suitability * weight
+        score += weighted_suitability
+
+    return score
+
+def convert_user_soil_nutrients(user_soil_nutrients):
+    return ', '.join(f"{key}: {value}" for key, value in user_soil_nutrients.items())
+
+def append_crop_data(top_crops, user_soil_nutrients, optimization_criteria):
+    field_names = ['Crop Name', 'N', 'P', 'K']
+
+    csv_file_path = 'crop_data.csv'
+
+    with open(csv_file_path, mode='a', newline='') as file:
+        writer = csv.DictWriter(file, fieldnames=field_names)
+
+        is_file_empty = file.tell() == 0
+
+        if is_file_empty:
+            writer.writeheader()
+
+        user_soil_nutrients_str = convert_user_soil_nutrients(user_soil_nutrients)
+
+        for crop in top_crops:
+            crop_obj = Crop(crop, {})  
+            crop_data = {
+                'Crop Name': crop,
+                'N': user_soil_nutrients.get('N', 0),
+                'P': user_soil_nutrients.get('P', 0),
+                'K': user_soil_nutrients.get('K', 0)
+            }
+            writer.writerow(crop_data)
+
+
+@app.route('/npk-data', methods=['POST'])
+def receive_npk_data():
+    npk_data = request.get_json()
+    user_soil_nutrients = npk_data
+
+    crops = [
+        Crop("Soybean", {'N': 0.4, 'P': 0.3, 'K': 0.4}),
+        Crop("Barley", {'N': 0.5, 'P': 0.4, 'K': 0.5}),
+        Crop("Wheat", {'N': 0.7, 'P': 0.4, 'K': 0.6}),
+        Crop("Rice", {'N': 0.1, 'P': 0.2, 'K': 0.1}),
+        Crop("Maize", {'N': 0.6, 'P': 0.4, 'K': 0.5}),
+        Crop("Potato", {'N': 0.3, 'P': 0.4, 'K': 0.5}),
+        Crop("Tomato", {'N': 0.6, 'P': 0.6, 'K': 0.6}),
+        Crop("Cotton", {'N': 0.6, 'P': 0.4, 'K': 0.6})
+    ]
+
+    optimization_criteria = {
+        'N': 1,  
+        'P': 1, 
+        'K': 1  
+    }
+
+    crop_scores = []
+    for crop in crops:
+        suitability_score = calculate_suitability_score(crop, user_soil_nutrients, optimization_criteria)
+        crop_scores.append((crop, suitability_score))
+
+    crop_scores.sort(key=lambda x: x[1], reverse=True)
+
+    top_three_crops = []
+    for crop, score in sorted(crop_scores[:3], key=lambda x: x[1], reverse=True):
+        top_three_crops.append(crop.name)
+
+    append_crop_data(top_three_crops, user_soil_nutrients, optimization_criteria)
+
+    response_data = {
+        'top_crops': top_three_crops,
+        'highest_score': crop_scores[0][1]
+    }
+
+    return jsonify(response_data)
+
+if __name__ == '__main__':
+    app.run()

Soil Analyzer and Optimized Crop Finder - IT20233808/soil-component-BE/crop_data.csv

+Crop Name,N,P,K
+Wheat,0.8,0.5,0.9
+Tomato,0.8,0.5,0.9
+Cotton,0.8,0.5,0.9
+Soybean,0.2,0.1,0.3
+Potato,0.2,0.1,0.3
+Barley,0.2,0.1,0.3
+Wheat,4.46,0.5,5.04
+Tomato,4.46,0.5,5.04
+Cotton,4.46,0.5,5.04
+Wheat,4.46,0.5,5.04
+Tomato,4.46,0.5,5.04
+Cotton,4.46,0.5,5.04
+Wheat,4.46,0.5,5.04
+Tomato,4.46,0.5,5.04
+Cotton,4.46,0.5,5.04
+Wheat,4.46,0.5,5.04
+Tomato,4.46,0.5,5.04
+Cotton,4.46,0.5,5.04
+Potato,0.3,0.2,0.5
+Soybean,0.3,0.2,0.5
+Barley,0.3,0.2,0.5
+Wheat,4.52,0.11,5.38
+Cotton,4.52,0.11,5.38
+Tomato,4.52,0.11,5.38
+Wheat,4.52,0.11,5.38
+Cotton,4.52,0.11,5.38
+Tomato,4.52,0.11,5.38
+Wheat,4.52,0.11,5.38
+Cotton,4.52,0.11,5.38
+Tomato,4.52,0.11,5.38
+Soybean,0,0,0
+Barley,0,0,0
+Wheat,0,0,0
+Wheat,4.52,0.29,5.19
+Cotton,4.52,0.29,5.19
+Tomato,4.52,0.29,5.19
+Wheat,4.57,0.11,5.33
+Cotton,4.57,0.11,5.33
+Tomato,4.57,0.11,5.33
+Tomato,0,0.52,9.48
+Wheat,0,0.52,9.48
+Cotton,0,0.52,9.48
+Wheat,4.52,0.11,5.38
+Cotton,4.52,0.11,5.38
+Tomato,4.52,0.11,5.38
+Wheat,4.52,0.11,5.38
+Cotton,4.52,0.11,5.38
+Tomato,4.52,0.11,5.38
+Wheat,4.52,0.11,5.38
+Cotton,4.52,0.11,5.38
+Tomato,4.52,0.11,5.38
+Soybean,0.5,0.3,0.2
+Barley,0.5,0.3,0.2
+Maize,0.5,0.3,0.2
+Potato,0.3,0.5,0.2
+Barley,0.3,0.5,0.2
+Tomato,0.3,0.5,0.2
+Potato,0.3,0.5,0.2
+Barley,0.3,0.5,0.2
+Tomato,0.3,0.5,0.2
+Wheat,4.47,0.29,5.24
+Cotton,4.47,0.29,5.24
+Tomato,4.47,0.29,5.24

Soil Analyzer and Optimized Crop Finder - IT20233808/soil-component-BE/soil_randomForest.py

+from flask import Flask, request, jsonify
+import joblib
+import pandas as pd
+from sklearn.metrics import accuracy_score
+
+model = joblib.load('./soil_crop_model.joblib')
+
+eval_data = pd.read_csv('soil_crop.csv')
+eval_X = eval_data[['nitrogen', 'phosphorus', 'potassium']]
+eval_y = eval_data['crop']
+
+predicted_labels = model.predict(eval_X)
+
+accuracy = accuracy_score(eval_y, predicted_labels)
+
+app = Flask(__name__)
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    input_data = request.get_json()
+    nitrogen = input_data['N']
+    phosphorus = input_data['P']
+    potassium = input_data['K']
+
+    predicted_crop = model.predict([[nitrogen, phosphorus, potassium]])
+
+    response = {
+        'crop': predicted_crop[0],
+        'accuracy': accuracy
+    }
+    return jsonify(response)
+
+if __name__ == '__main__':
+    app.run()