Abstract: In this research work, a prototype model based on machine learning is presented, which is implemented as a Jupyter Notebook to predict harmful deviations from normal structures in plants or their leaves. The model employs hybrid ensemble learning techniques in Python. The proposed system integrates data obtained from 2,000 photos and applies machine learning algorithms to predict the crops that are best adapted to the prevailing environmental conditions. This system aims to provide farmers with a variety of crop options that are well-suited to their conditions and are presented in an informative and accessible manner. Machine learning is essential for building computer programs that can learn and improve on their own by analyzing data. Several models have been proposed for plant disease prediction, addressing some of the most challenging issues in precision agriculture. Plant diseases are influenced by various factors, including fertilizer usage, weather, soil variety, etc. This complexity requires working with multiple datasets. A prototype was created using hybrid machine learning and data analytics with an algorithm in Python, designed to assist farmers in managing plant health more effectively.

Keywords: Plant pathology, leaf disease, machine learning, CNN, feature extraction, disease classification, precision agriculture

Pages: 136-141