Recognition of Weed Detection in Smart Agricultural Farm using Image Processing with IoT

The agriculture sector is indispensable for safeguarding sustainability and food safety. Though the presence of weeds in agricultural pitches can seriously reduce crop output and agricultural production. Customary weed management techniques include a huge amount of manual labor which takes a long time, and frequent use of chemical herbicides that may be harmful to both the environment and human health. This work suggests a unique method for weed detection in smart agricultural farms by combining Internet of Things (IoT) and Deep Learning (DL) methods with image processing. The suggested method takes benefit of recent developments in computer vision, IoT, and DL to quickly and accurately identify and categorize weeds. To take pictures and gather environmental data, IoT devices like high-resolution cameras and sensors are being positioned in agricultural areas. The pipeline for processing images consists of various steps, counting feature extraction, cataloguing, feature extraction from input images, and picture acquisition. The captured images are initially pre-processed to improve their quality and eliminate noise. The pre-processed photos are further subjected to refined image-processing algorithms to extract pertinent features from them. To classify weeds, the collected characteristics are then used as inputs to a deep learning prototypical, such as a convolutional neural network (CNN). The suggested approach has several advantages, such as precise and prompt weed detection, less dependency on chemical herbicides, and increased agricultural productivity. Farmers can remotely monitor their crops using IoT technology and get alerts when weeds are found. This lowers expenses and diminishes a contrary effect on the atmosphere while providing actual and targeted weed treatment.