Using Deep Learning for Image-Based Plant Disease Detection

Abstract

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

“…The experimental results showed that the proposed CNN-based model can reach a good recognition performance, and obtained an average accuracy of 96.3%. In [19], Mohanty et al developed a CNN-based model to detect 26 diseases and 14 crop species. Using a public dataset of 54,306 images of diseased and healthy plant leaves, the proposed model was trained and achieved an accuracy of 99.35%.…”

“…Inspired by the breakthrough of the convolutional neutral network in image-based recognition, the use of convolutional neural networks to identify early disease images has become a new research hotspot in agricultural informatization. In [13][14][15][16][17][18][19][20], convolutional neural networks (CNNs) are widely studied and used in the field of crop disease recognition. These studies show that convolutional neural networks have not only reduced the demand of image preprocessing, but also improved the recognition accuracy.…”

Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition

“…Grinblat et al (2016) employed a 3-layer CNN for assessing the classification performance on three different legume species and they emphasised the relevance of vein patterns. The works of Mohanty et al (2016) and Sladojevic et al (2016) used the deep CNN architectures to work on plant disease detection by focusing on leaf image classification. Mohanty et al (2016) compared the performance of two CNN architectures: AlexNet and GoogleNet, with different sizes of training and test sets.…”

Managing Biological Invasions: The Impact of Exotic Diseases on Plant Communities in Australia

“…Emerging technologies with potential to improve the efficiency of biosecurity activities identified by stakeholders covered: autonomous and drone surveillance; robotics and artificial intelligence (Mohanty et al 2016); 'Big data' and analytics; 'point of need' field testing; alternative treatment methods (for example, as a replacement for methyl bromide); and, innovations from various fields of science (for example, as next-generation sequencing, antimicrobial resistance, and new biological controls).…”