AI-Powered Maize Analysis by Exploring InceptionV3 CNN Applications

There is a significant risk to world food security from plant diseases, and it is difficult and time-consuming to diagnose these illnesses in a timely manner. It is usually best to rely on the knowledge and experience of trained experts when trying to assess a plant's health and identify individual illnesses. Deep Learning's introduction of incredibly effective methods for picture analysis and classification has revolutionised computer vision. The purpose of this research is to visually depict and classify maize photographs using the MobileNet50 Convolutional Neural Network (CNN) model. Because there are so many different kinds of maize and so many different stages of its life cycle, identifying this extensively grown crop may be somewhat challenging. With the use of the cutting-edge MobileNet50 CNN architecture, this study intends to improve the efficacy and accuracy of maize categorization. Impressively, the algorithm was able to discriminate between different varieties of maize plants and their health conditions with an accuracy of 97%. This work uses MobileNet50 to help identify maize illnesses quickly and accurately, which advances computer vision applications in agriculture. By utilising a deep learning technique, the need on human knowledge is reduced, making large-scale agriculture surveillance more accessible and efficient. The use of MobileNet50 in maize categorization has the potential to greatly improve plant disease identification and aid in the fight for food security on a worldwide scale.