Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

Abstract

In agricultural engineering, the main challenge is on methodologies used for disease detection. The manual methods depend on the experience of the personal. Due to large variation in environmental condition, disease diagnosis and classification becomes a challenging task. Apart from the disease, the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background. In Cucurbita gourd family, the disease severity examination of leaf samples through computer vision, and deep learning methodologies have gained popularity in recent years. In this paper, a hybrid method based on Convolutional Neural Network (CNN) is proposed for automatic pumpkin leaf image classification. The Proposed Denoising and deep Convolutional Neural Network (CNN) method enhances the Pumpkin Leaf Preprocessing and diagnosis. Real time data base was used for training and testing of the proposed work. Investigation on existing pre-trained network Alexnet and googlenet was investigated is done to evaluate the performance of the proposed method. The system and computer simulations were performed using Matlab tool.

Robust Deep Learning Model for Black Fungus Detection Based on Gabor Filter and Transfer Learning

“…The key points in developing an effective ensemble model are the current methods for aggregating all models. Recently, ensemble learning has been used in several medical domains [4], for example. Mahmoud et al [5] propose a stacking ensemble model for predicting mortality inside the ICU.…”