Plant Disease Detection from Images

TL;DR

This paper presents a deep learning model using transfer learning with ResNet architectures to accurately detect plant diseases from leaf images, aiming to make disease diagnosis accessible and reduce reliance on experts.

Contribution

The study develops a transfer learning-based CNN model that achieves state-of-the-art results in plant disease detection from images, demonstrating the effectiveness of ResNet architectures.

Findings

ResNet 50 outperforms ResNet 34 in accuracy

Achieved state-of-the-art results on the dataset

Model reduces need for professional diagnosis

Abstract

Plant disease detection is a huge problem and often require professional help to detect the disease. This research focuses on creating a deep learning model that detects the type of disease that affected the plant from the images of the leaves of the plants. The deep learning is done with the help of Convolutional Neural Network by performing transfer learning. The model is created using transfer learning and is experimented with both resnet 34 and resnet 50 to demonstrate that discriminative learning gives better results. This method achieved state of art results for the dataset used. The main goal is to lower the professional help to detect the plant diseases and make this model accessible to as many people as possible.