DEVELOPING AN APPLICATION TO IDENTIFY PHYTOPATHOLOGICAL DISEASES IN LEAVES USING A CONVOLUTION NEURAL NETWORK (CNN)

Abstract

In the realm of precision agriculture, the accurate identification of phytopathological conditions in 
plant foliage is crucial for effective disease management and crop yield optimization. This paper 
delineates the development of an advanced application designed to identify and classify 
phytopathological conditions in foliage through the implementation of a Convolutional Neural Network 
(CNN) architecture. 
The proposed system is embedded within a comprehensive project framework that integrates various 
components to enhance its functionality and efficiency. The CNN architecture, selected for its efficacy 
in image analysis tasks, is meticulously engineered to process and analyze high-resolution images of 
plant leaves. This architecture is trained on a diverse dataset comprising annotated images of different 
plant diseases, enabling it to discern subtle variations and patterns indicative of specific pathological 
conditions. 
The application encompasses several critical phases: preprocessing of input images to standardize and 
enhance quality, extraction of relevant features using deep convolutional layers, and classification 
through fully connected layers to accurately identify the disease. Furthermore, the system includes a 
user interface that facilitates seamless interaction, allowing users to upload images, view diagnostic 
results, and access recommendations for disease management. 
In addition to its technical aspects, the application is designed to be integrated into existing agricultural 
practices, providing real-time disease detection and actionable insights. The integration of CNN-based 
analysis within this application signifies a significant advancement in agricultural technology, aiming 
to improve disease management strategies and promote sustainable agricultural practices. This 
intricate project framework underscores the transformative potential of machine learning in modern 
agriculture, offering a robust solution for early disease detection and enhanced crop health 
management