Paper Title
Smart Agricultural Monitoring: Leveraging Machine Learning for Precision Disease Detection and Crop Health Optimization

Abstract
In the face of rising global concerns about food security and agricultural productivity, particularly with the challenges posed by climate change and pest outbreaks, there is a growing need for efficient and smart farming techniques. This research aims to develop a Smart Disease Detection and Field Monitoring System using IoT (Internet of Things) sensors, coupled with AI/ML (Artificial Intelligence and Machine Learning) algorithms, to enhance plant health management and optimize field monitoring. The system integrates multiple sensors for temperature, humidity, and soil moisture, which are crucial for determining crop health. Additionally, AI-based disease detection models are employed to recognize plant diseases from images of leaves and other plant parts. The core components of this system include the use of an ESP8266 microcontroller that connects various sensors to the internet, enabling real-time data transmission to a Firebase cloud database. The collected data is then used to train an AI/ML model, which helps identify potential diseases and anomalies in the plants' health. This model processes visual data from cameras, analyzes the environmental factors such as temperature, humidity, and soil moisture, and provides actionable insights into plant health, guiding the appropriate interventions such as irrigation or pesticide application. The disease detection algorithm employed utilizes a Convolutional Neural Network (CNN), which has been trained to recognize patterns associated with specific plant diseases, ensuring accurate and early detection. The system also incorporates a dashboard interface, built using Flask or Django, where farmers and users can monitor real-time data from the field, track historical trends, and receive recommendations for managing plant health. Keywords - Agricultural Productivity, Climate Change, Pest Outbreaks, Smart Farming, IoT Sensors, Artificial Intelligence (AI), Machine Learning (ML), Plant Health Management, Field Monitoring, ESP8266 Microcontroller, Real-time Data Transmission, Convolutional Neural Network (CNN) Visual Data Analysis, Irrigation Management, Pesticide Application, Crop Health Optimization, Plant Disease Recognition