Farming App
Group Number: A16
Group Members:
Swastik Gavhane - 01
Kedar Jasud - 04
Kushal Tilke - 07
Anurag Pawar - 26
Project Guide: Mr. Swapnil Powar
Abstract
Objective
To develop a mobile app that helps farmers detect plant
diseases, provides treatment recommendations, offers real-
time weather updates, and includes an AI bot to guide farmers
in growing healthy plants.
Approach
Build a mobile app that uses image recognition for disease
detection, integrates real-time weather data, provides
treatment recommendations, and features an AI bot to guide
farmers in healthy plant growth.
Introduction
Agriculture is crucial for food, but farmers struggle with plant diseases, weather, and market prices. Our
project tackles these issues with a mobile app that uses image recognition and machine learning to detect
plant diseases early. It also provides treatment advice and real-time weather updates. An AI bot offers
personalized tips to help farmers make better decisions and promote sustainable farming.
Literature Survey
Mohanty et al. (2016) [1] used computer vision and machine
01 learning to identify 14 plant diseases with 96.3% accuracy,
showing how well image-based systems can diagnose plant
issues.
Singh et al. (2018) [4] predicted plant diseases using
02 weather data and achieved 85.7% accuracy, highlighting the
benefit of including weather information in disease
prediction.
Zhang et al. (2021) [5] combined IoT sensors with machine
03 learning to monitor soil and plant health, improving early
disease detection with up to 90% accuracy.
Lee et al. (2022) [6] developed an AI system using satellite
04 images and drone data to assess plant health and predict
diseases, leading to a 20% increase in crop yield and less
pesticide use.
Motivation
Improved Crop Health Efficient Resource Use:
01 AI can help detect plant 02 By predicting disease
diseases early, leading to outbreaks, AI helps farmers
healthier crops and higher use resources like water
yields. and pesticides more
efficiently.
Cost Savings: Informed Decisions:
03 Early detection of issues 04 AI provides farmers with
reduces the need for data-driven insights, leading
expensive treatments and to better decision-making
minimizes crop losses. and increased productivity.
Sustainable Farming:
05 Enhanced disease
management promotes
sustainable agricultural
practices and reduces
environmental impact.
Problem Definition
Current Challenges:
1. Inaccurate Disease Detection: Existing systems often rely on manual inspections or outdated
methods, resulting in delayed and inaccurate detection of plant diseases.
2. Lack of Real-Time Monitoring: Many current solutions do not integrate real-time
environmental and plant health data, leading to ineffective disease management.
3. Limited Scope of Detection: Current models may only identify a limited number of diseases
and do not cover a wide range of plant health issues.
4.General Recommendations: Existing systems often provide generic advice that
doesn’t
account for specific crop types or local conditions.
Project Goal:
To develop an AI-driven system that offers accurate, real-time detection and prediction of plant
diseases by integrating advanced machine learning techniques with real-time environmental
data and personalized recommendations, aiming to improve overall crop health and farming
efficiency.
Proposed Architecture
Experimental Setup:
Software Requirements:
Linux (Ubuntu 20.04 or later) or Windows 10/11
01
Visual Studio Code, Jupyter Notebook
Git (GitHub or GitLab)
Firebase or MongoDB
Hardware Requirements:
Intel Core i5 or AMD Ryzen 5 (or higher)
02 16 GB (32 GB recommended)
512 GB SSD (1 TB recommended)
Libraries:
Frontend: React Native, Tailwind CSS
03
Backend: NodeJS, MongoDB
Conclusion
Our AI system improves plant disease detection
by using advanced technology and real-time
data, offering more accurate and comprehensive
results than current methods. It helps farmers
manage crops better, save resources, and boost
productivity. As we continue to test and refine
the system, it holds great promise for enhancing
agriculture and could lead to further innovations
in the field.
