Plant Infirmity Detection Using Vgg -16 Convolutional Neural Network

This research paper aims to leverage state-of-the-art deep learning techniques to detect plant diseases in agriculture with greater accuracy and timeliness, with a particular emphasis on the Indian economy. Agriculture is a critical aspect of the Indian economy, and plant diseases caused by climatic factors, pests, and weather conditions can significantly affect the economy. CNNs mimic the structure and function of the visual cortex in the brain and learn spatial hierarchies of features automatically and adaptively impact productivity and yield. Early and precise identification of plant diseases is crucial to estimating agricultural production and maximizing resources while minimizing the use of pesticides. The VGG-16 convolutional neural network architecture which uses Rectified Linear Units (ReLU) activation function is used and it has been shown to be highly effective and accurate in detecting and diagnosing plant diseases. Additionally, we have integrated a Graphical User Interface (GUI) that can provide an intuitive platform for users to input plant images and obtain detailed diagnostic information along with an efficient solution to the plant disease. Using these techniques, the research aims to increase productivity and yield by detecting plant diseases early and accurately. The paper also discusses potential avenues for further research and the challenges that still exist in identifying plant diseases using available technology.