Integrating Convolutional Neural Network with IoT for Weed Management

Improving agricultural weed control practices is essential for sustaining crop yields while decreasing negative effects on the environment. This paper presents a thorough method for detecting and eliminating weeds by integrating Convolutional Neural Network (CNN) with Internet of Things (IoT) technologies. Utilizing image analysis methods, our system effectively utilizes CNN to detect and categorize weeds in real-time. The CNN model achieves consistently high accuracy and reliability in weed identification by using a diversified dataset for training and validation. Further, the IoT allows sensors, actuators, and the CNN model to communicate and collaborate seamlessly, allowing for targeted weed eradication techniques that are both timely and efficient. Improved weed control methods and less dependence on chemical pesticides are two outcomes of this integrated framework’s efforts to foster sustainability. The technique has the potential to completely transform weed control in contemporary agriculture, as shown by the experimental findings, which attest to its practicality and effectiveness. A smart weed identification and sustainable eradication approach is proposed in this study, which might lead to increased agricultural yields while also protecting the environment.