Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

By Marko Arsenovic, Mirjana S. Karanović, Srdjan Sladojevic, Andras Anderla, Darko Stefanović. Symmetry, 2019

445 citations recorded by the source corpus.

Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/ec197781e263d2dc22f8dfb0a87ce1629c2aec48

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Plant Science

1. Jinzhu Lu
2. Johan Potgieter
3. Kemal Akyol
4. Emine Uçar
5. Muhammad Hammad Saleem
6. Jun Liu
7. Xuewei Wang
8. Li Tan

Citations in the corpus

• Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification (2021, background): “In the studies on plant disease classification, accuracy is the most common evaluation index [53,60,64,67,68].”
• Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, unknown): “The latest trend includes proposals that implement architectures explicitly designed for the identification and classification of plant diseases, with emphasis on models that use hyperspectral images, namely: GPDCNN (S1), 3D-CNN (S7), PDDNN (S11), M-bCNN-CKM (S12), Teacher / Student Architecture (S13)”
• Plant Disease Detection and Classification by Deep Learning (2019, methodology): “A state-of-the-art DL model was proposed in [90] named PlantdiseaseNet which was remarkably suitable for the complex environment of an agricultural field.”
• Plant leaf disease classification using EfficientNet deep learning model (2021, methodology): “The model they proposed achieved 93.67% accuracy in the Plant Disease dataset (Arsenovic et al., 2019).”
• Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network (2020, background): “Arsenovic et al. (2019) established a plant disease dataset with 79,265 pieces collected under different meteorological conditions.”