Spinach fungi guard: A deep learning-based software solution for swift detection and remediation of fungal diseases in spinach leaves

Abstract

We have developed a software solution aimed at assisting farmers in quickly detecting and finding a cure for fungal diseases in spinach leaves. This solution utilizes Deep Learning techniques, specifically a Convolutional Neural Network (CNN), to effectively detect diseases caused by fungi in spinach leaves and suggest suitable remedies to the user. To train our model; we gathered a spinach fungi disease image dataset consisting of over 700 images categorized into six classes, including five diseases and one healthy category. Prior to model training, we resized the images and applied various image augmentation techniques to improve the robustness of the model. Using Keras, we constructed a sequential CNN model for disease classification. The model was trained on the dataset and evaluated on the validation set, achieving an impressive accuracy of 89.86 %. To provide an intuitive interface for end users, we implemented a PySide2-based GUI application that leverages the trained model to classify disease in spinach leaf images provided as input.Our software not only accurately classifies the disease but also suggests appropriate remedies or medications for the specific disease. Furthermore, it provides links to relevant products on various e-commerce sites, enabling users to conveniently purchase the required medications. This comprehensive solution empowers end users to analyze infected spinach leaf images, accurately classify diseases, and take necessary actions by applying appropriate remedies and acquiring the right medications. By swiftly detecting diseases and offering prompt remedies, our software aids in preserving the production of spinach and ensures farmers can effectively combat fungal diseases, ultimately benefiting the food, medicine, and skincare industries.