Kongunadu College of Engineering and Technology(Autonomous)

  Abstract Agriculture is increasingly integrating automation and IoT technologies to enhance productivity and efficiency. This project proposes an Automated Tomato Picker Machine that utilizes IoT and AI-based image processing to identify and harvest ripe tomatoes. The system employs an ESP32-CAM module for image capture, a color sensor for ripeness detection, and a 4-DOF robotic arm for precise picking. The AI model, trained using Google Teachable Machine or TensorFlow, is deployed on the ESP32-CAM to classify tomato ripeness in real time. Once a ripe tomato is detected, a Node-MCU processes the data and sends signals to control the robotic arm for picking. The system can be connected to a mobile application via IoT, providing real-time updates and notifications to farmers. This automation reduces labor dependency, enhances harvesting efficiency, and minimizes post-harvest losses. The IoT-based monitoring system ensures better decision-making, leading to optimized yield and res...

 ABSTRACT: Mango is one of the most important tropical fruits with high economic value, but it suffers from significant post-harvest losses due to improper handling, storage, and transportation conditions. Conventional post-harvest practices depend on manual monitoring and fixed storage parameters, which often fail to respond to dynamic environmental changes. Digital Twin technology provides a smart solution by creating a virtual representation of the physical mango fruit and its post-harvest environment. This article presents a comprehensive study on the application of digital twin technology for mango post-harvest management. The proposed approach integrates IoT sensors, cloud computing, and data analytics to monitor fruit quality in real time, predict spoilage risks, and optimize storage and transportation decisions. The study highlights the process, benefits, challenges, and future scope of digital twin implementation in the mango supply chain. INTRODUCTION: Mango is widely cul...

  ABSTRACT : The agricultural sector is moving through a phase of rapid technological change driven by Artificial Intelligence (AI) and robotics. Traditional farming methods are struggling to cope with modern issues such as shortage of skilled labor, irregular climate patterns, excessive use of water and fertilizers and the increasing pressure to produce more food for a growing population. To overcome these challenges, farmers are turning toward intelligent technologies that make agriculture more precise, automated and efficient. Artificial Intelligence enables farms to operate based on data rather than guesswork. Advanced algorithms analyze information related to soil properties, moisture levels, crop growth stages, weather conditions and pest behavior. Using tools like machine learning and computer vision, AI systems help farmers plan irrigation, monitor crop health, control pests and predict yields with greater accuracy. At the same time, robotic machines perform physically dema...

  ABSTRACT Agriculture today is rapidly embracing automation and IoT technologies to boost productivity and make farming smarter. This project introduces an Automated Vegetable Picker Machine that uses IoT and AI to detect and harvest ripe vegetables with better efficiency. The system combines an ESP32-CAM module for image capture, a color sensor for ripeness detection and a 4-DOF robotic arm to carefully pick the vegetables without causing damage. A lightweight AI model, trained using platforms like Google Teachable Machine or TensorFlow, is deployed on the ESP32-CAM to classify vegetables. Ripeness in real time. Once a ripe vegetable is identified, the Node MCU processes the data and sends commands to the robotic arm to carry out the picking action with precision. The entire setup is connected to a mobile application via IoT, allowing farmers to receive live updates and notifications on harvesting progress. This level of automation reduces dependence on manual labor, speeds up th...

  ABSTRACT A Wireless Sensor Network (WSN) for Smart Farm Management integrates multiple wireless sensor nodes across agricultural fields to monitor and manage environmental conditions in real time. These nodes measure essential parameters such as soil moisture, temperature, humidity and light intensity, transmitting data wirelessly to a central control unit or cloud server. The collected data are analyzed to provide actionable insights that support decision-making for irrigation, fertilization and crop protection. Using communication technologies such as ZigBee, LoRa or Wi-Fi the system ensures reliable long-range data transmission with low power consumption. The integration of Internet of Things (IoT) features enables farmers to remotely access data and control operations using mobile or web interfaces. This system enhances productivity, reduces human intervention and conserves vital resources like water and energy. Furthermore, automated control mechanisms-such as smart irrigati...