DEV Community: Somnath Das

🚀 How to Solve Arrays and Hashing Problems in Data Structure?

Somnath Das — Sun, 10 May 2026 03:09:58 +0000

Arrays and Hashing are among the most important topics in Data Structures & Algorithms (DSA) and are frequently asked in coding interviews at companies like FAANG, startups, and product-based companies. Here’s a simple roadmap to master them 👇

📌 1. Understand the Problem Type

Before coding, identify the pattern:
✅ Searching elements
✅ Frequency counting
✅ Duplicate detection
✅ Pair/target sum problems
✅ Prefix sum or subarray problems
✅ Grouping & mapping

📌 2. Master Array Basics

Arrays are all about indexing and traversal. Focus on:
🔹 Traversing efficiently
🔹 Sorting techniques
🔹 Two-pointer approach
🔹 Sliding Window technique
🔹 Prefix Sum optimization

Example Problems:
✔ Two Sum
✔ Best Time to Buy & Sell Stock
✔ Maximum Subarray
✔ Product of Array Except Self

📌 3. Learn Hashing (HashMap / Dictionary / Set)

Hashing helps reduce time complexity from O(n²) → O(n) by storing values smartly.

Use:
🔹 unordered_map → key-value storage
🔹 unordered_set → unique elements checking

When to use hashing?
👉 Fast lookup required
👉 Counting frequencies
👉 Finding duplicates
👉 Tracking visited elements

📌 Example Code (Two Sum using Hashing in C++)

#include <iostream>
#include <vector>
#include <unordered_map>
using namespace std;

vector<int> twoSum(vector<int>& nums, int target) {
    unordered_map<int, int> mp;

    for (int i = 0; i < nums.size(); i++) {
        int complement = target - nums[i];

        if (mp.find(complement) != mp.end()) {
            return {mp[complement], i};
        }

        mp[nums[i]] = i;
    }

    return {};
}

int main() {
    vector<int> nums = {2, 7, 11, 15};
    int target = 9;

    vector<int> ans = twoSum(nums, target);

    cout << "Indices: ";
    for (int x : ans) {
        cout << x << " ";
    }

    return 0;
}

✅ Time Complexity: O(n)
✅ Space Complexity: O(n)

📌 4. Follow This Problem-Solving Strategy

1️⃣ Read the problem carefully
2️⃣ Spend at least 30 minutes trying to solve it yourself
3️⃣ Do dry runs with sample test cases on paper
4️⃣ If stuck, try solving the brute force approach first
5️⃣ Then optimize using Arrays / Hashing techniques
6️⃣ Analyze Time Complexity & Space Complexity
7️⃣ Practice similar variations

📌 5. Golden Questions to Practice

🔥 Two Sum
🔥 Contains Duplicate
🔥 Valid Anagram
🔥 Group Anagrams
🔥 Top K Frequent Elements
🔥 Longest Consecutive Sequence

💡 Pro Tip:

Whenever you see words like “frequency”, “duplicate”, “fast lookup”, or “pair finding”, think about HashMap/HashSet instantly!

Remember: Don’t jump to solutions too quickly. Spending time thinking builds problem-solving skills. Even if you can’t solve the optimal solution, getting the brute force approach right is progress 🚀

💻 Consistency beats talent in DSA. Practice 1–2 problems daily and focus on patterns instead of memorizing solutions.

🌿 Plant Disease Detection System

Somnath Das — Tue, 05 May 2026 19:46:34 +0000

Agriculture is the backbone of many economies, yet plant diseases continue to cause massive crop losses every year. What if farmers could detect diseases instantly using just an image?

That’s exactly what this project does 👇

🚀 Overview

The Plant Disease Detection System is an AI-powered web application that leverages Deep Learning (CNN) to identify plant diseases from leaf images. Along with detection, it also provides fertilizer recommendations, treatment steps, and prevention guidance.

Built using TensorFlow + Django, this project bridges the gap between AI and real-world agriculture 🌾

🎯 Key Features

🔍 Real-time Disease Detection
Upload a plant leaf image and get instant predictions
🎯 High Accuracy
Achieves 81–84% accuracy across 38 disease classes
💊 Smart Recommendations
Get treatment steps, fertilizers, and prevention tips
📊 Prediction History
Track all predictions with timestamps
📱 Responsive UI
Works smoothly on desktop, tablet, and mobile
🚀 REST API Support
Easy integration with mobile apps
🔐 Secure System
CSRF protection, file validation, and sanitized inputs

🌾 Supported Crops & Diseases

The model supports 38 disease classes across multiple crops:

🍅 Tomato (10 classes)
🥔 Potato (3 classes)
🌽 Corn (4 classes)
🌶️ Pepper (2 classes)
🍎 Additional crops included

📸 Application Workflow

Upload a leaf image
Model analyzes using CNN
Returns:

Disease name
Confidence score
Treatment
Fertilizer advice
Prevention tips

🚀 Quick Start

🔧 Prerequisites

Python 3.8+
pip
Virtual environment (recommended)
4GB RAM
2GB storage

⚙️ Installation

# Clone repo
git clone https://github.com/dassomnath99/Plant-Disease-Detection.git
cd Plant-Disease-Detection

# Create virtual environment
python -m venv venv

# Activate (Windows)
venv\Scripts\activate

# Activate (Linux/macOS)
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

🤖 Model Setup

Option A: Use Pre-trained Model

Place these files in /models:

plant_disease_model.h5
class_names.json

Option B: Train Your Own Model

python download_data.py
python train_with_test.py

🌐 Run Django Server

python manage.py makemigrations
python manage.py migrate
python manage.py runserver

Open 👉 http://127.0.0.1:8000/

🔌 API Example

Predict Disease

POST /api/predict/

Response:

{
  "success": true,
  "prediction": {
    "disease": "Tomato_Late_blight",
    "confidence": 94.32,
    "plant_type": "Tomato",
    "treatment": "Apply fungicide",
    "prevention": "Avoid wet foliage"
  }
}

🧪 Model Performance

Metric	Training	Validation	Test
Accuracy	82.32%	77.15%	68.78%

📊 Training Details

Dataset: PlantVillage (54K+ images)
Architecture: MobileNetV2 (Transfer Learning)
Framework: TensorFlow 2.15
Input Size: 224x224
Model Size: ~13MB

🛠️ Tech Stack

Backend

Django
Django REST Framework
TensorFlow / Keras

Frontend

HTML5
CSS3
JavaScript

ML Techniques

CNN (MobileNetV2)
Data Augmentation
Adam Optimizer

📱 Mobile Integration

Works seamlessly with:

React Native
Flutter

You can easily send images using multipart API requests.

🚀 Deployment Options

🔹 Heroku

heroku create your-app-name
git push heroku main

🔹 Docker

docker build -t plant-disease-detection .
docker run -p 8000:8000 plant-disease-detection

🐛 Common Issues

Model not loading?

ls models/plant_disease_model.h5

Dependency issues?

pip install -r requirements.txt --force-reinstall

CORS error?

CORS_ALLOW_ALL_ORIGINS = True

🤝 Contributing

Contributions are welcome!

Fork the repo
Create a branch
Commit changes
Open PR

📝 License

MIT License

👨‍💻 Author

Somnath Das

GitHub: @dassomnath99
Email: somnathdas4462@gmail.com

🙏 Acknowledgments

PlantVillage Dataset (Kaggle)
TensorFlow Team
Django Community

💡 Final Thoughts

This project is more than just a machine learning model — it’s a step toward smart agriculture 🌍

By combining AI + Web Development, we can empower farmers with tools that are:

Fast
Accurate
Accessible

💬 If you found this useful, consider starring the repo and sharing your feedback!

🌿 AI-Powered Plant Disease Detection System

Somnath Das — Mon, 30 Mar 2026 15:03:25 +0000

Agriculture and Artificial Intelligence are coming together to solve real-world problems! In this project, I built a Plant Disease Detection System using Deep Learning (CNN) that can identify plant diseases from leaf images and provide actionable insights like treatment and fertilizer recommendations.

🔍 What This Project Does

The workflow is simple and effective:

Upload a plant leaf image → Get disease prediction → Receive treatment guidance

This makes it highly useful for farmers, agricultural professionals, and even researchers looking for quick and reliable plant health analysis.

🎯 Key Features

🔍 Real-time Disease Detection using Convolutional Neural Networks
🎯 81–84% Accuracy across 38 plant disease classes
💊 Treatment & Prevention Recommendations tailored to predictions
📊 Prediction History Tracking with timestamps
📱 Responsive Design (Desktop, Tablet, Mobile)
🚀 REST API Support for integration with mobile apps
🔐 Security Focused (CSRF protection, file validation, input sanitization)

🌾 Supported Plants & Diseases

The model currently supports 38 disease classes across multiple crops:

🍅 Tomato – 10 disease classes
🥔 Potato – 3 disease classes
🌽 Corn – 4 disease classes
🌶️ Pepper – 2 disease classes
🍎 Other crops also included

🛠️ Tech Stack

Backend: Django
Machine Learning: TensorFlow, CNN
Languages: Python
Frontend: HTML, CSS
API: RESTful services

🎯 Goal of This Project

The main goal is to empower farmers with AI-driven tools that can:

Improve crop health
Reduce agricultural losses
Provide quick decision support
Promote smart farming practices

🔗 GitHub Repository

Check out the full project here:
👉 https://github.com/buildwithsomnath/Plant-Disease-Detection/tree/main

📸 Screenshot

💬 Final Thoughts

This project reflects my passion for Data Science, Machine Learning, and Full Stack Development, and how these skills can be used to build impactful real-world solutions.

If you found this interesting or have suggestions, feel free to share your thoughts. I’d love to collaborate and improve this further!

🏷️ Tags

#AI #MachineLearning #DeepLearning #ComputerVision #Django #TensorFlow #DataScience #AgricultureTech