Autonomous Multi-Crop Weeding and Nutrient Management
Abstract
Agriculture faces major challenges such as weed growth, labor shortages, excessive fertilizer usage, and lack of real-time monitoring. This paper proposes an Autonomous Multi-Crop Weeding and Nutrient Management Machine using Artificial Intelligence (AI), Internet of Things (IoT), and embedded systems. The system uses AI-based image processing for weed detection and activates a mechanical cutter to remove weeds automatically. Soil pH and NPK sensors monitor nutrient levels and enable precision nutrient spraying only when required. An ESP32 microcontroller controls navigation, sensor interfacing, motor operation, and IoT communication. GPS, ultrasonic, and IMU sensors enable autonomous navigation and obstacle detection. Real-time data is transmitted to farmers through Wi-Fi or Bluetooth. The proposed system reduces labor dependency, minimizes chemical usage, improves crop productivity, and supports sustainable precision agriculture. The proposed machine is designed to provide an integrated approach to precision agriculture by combining intelligent weed management, soil monitoring, nutrient management, and autonomous field operation in a single system. The camera-based detection mechanism continuously observes the crop area and identifies weeds, after which the mechanical weeding unit is activated for selective removal. Simultaneously, soil pH and NPK sensors provide continuous information about soil conditions, allowing the system to determine nutrient requirements and operate the spraying mechanism accordingly. The integration of GPS, ultrasonic, and IMU sensors supports autonomous movement, obstacle detection, and directional stability during field operations. The ESP32 coordinates the different subsystems and enables communication of important field and machine parameters through Wi-Fi or Bluetooth. This integrated operation reduces unnecessary human intervention and promotes efficient utilization of agricultural resources. By combining AI-based decision-making with embedded control, sensing, robotics, and IoT connectivity, the system provides a practical approach for reducing labor requirements, minimizing input wastage, improving crop management, and supporting sustainable and data-driven agricultural practices. Keywords— Autonomous agriculture, ESP32, AI in farming, IoT, weed detection, precision farming, nutrient monitoring.