Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

By Srdjan Sladojevic, Marko Arsenovic, Andras Anderla, Dubravko Ćulibrk, Darko Stefanović. 2016

1,835 citations recorded by the source corpus.

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

Subfields: Plant Science

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “[83], the authors developed a plant disease recognition system based on the classification of leave images.”
Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach (2021, positive): “[26] designed a DL architecture to identify 13 different plant diseases.”
An In-field Automatic Wheat Disease Diagnosis System (2017, methodology): “In addition, a small amount of researches focused on plant disease detection using DCNN. (Sladojevic et al., 2016) collected 3,000 original leaf images from Internet and performed data augmentation process on this database, then built a DCNN to automatically classify and detect 13 di ﬀ erent types o”
Applications of Remote Sensing in Precision Agriculture: A Review (2020, background): “Furthermore, some diseases do not show any visible symptoms, or the effect may not be noticeable until it is too late to act [247].”
How deep learning extracts and learns leaf features for plant classification (2017, methodology): “Instead of relying on either whole leaf data [31, 45, 46, 47] or solely venation [30, 31] for species classification, we propose to combine information from two CNN networks, one global network trained upon the whole leaf data and another local network trained upon its corresponding leaf patches.”
An advanced deep learning models-based plant disease detection: A review of recent research (2023, methodology): “DL models, such as CNNs and DBNs, have been applied plant lesion identification based on image analysis and classification, providing better accuracy and robustness compared to traditional image processing methods (Sladojevic et al., 2016; Alzubaidi et al., 2021; Dhaka et al., 2021).”
Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild (2019, background): “Object detection approaches ( Uijlings et al. (2013), Girshick et al.”
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition (2017, methodology): “For instance, [49] developed a CNN-based system to identify 13 types of diseases out of healthy ones in five crops using images downloaded from the internet.”
Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks (2017, methodology): “Appropriate datasets are required at all stages of object recognition research, from training the CNN-based models to evaluating the performance of the recognition algorithms [14].”
Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis (2021, methodology): “Machine learning (ML) is a kind of technologies using computer to perform repetitive and well-deﬁned tasks (3–5).”