Plants Disease Identification and Classification Through Leaf Images: A Survey

By Sukhvir Kaur, Shreelekha Pandey, Shivani Goel. Archives of Computational Methods in Engineering, 2018

311 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/57fed257317eaaddd4698481c0465af063954740

How researchers see this paper

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

In Analytical Chemistry

1. Srdjan Sladojevic
2. Andras Anderla
3. Marko Arsenovic
4. Darko Stefanović

In Ecology

1. Marcin Wozniak
2. Geeta Rani
3. Sangeeta Vaibhav Meena
4. Sanjeev Jain
5. Srdjan Sladojevic
6. Andras Anderla
7. Uday Pratap Singh
8. Marko Arsenovic

In Plant Science

1. Marcin Wozniak
2. Geeta Rani
3. Sangeeta Vaibhav Meena
4. Sanjeev Jain
5. Srdjan Sladojevic
6. Andras Anderla
7. Uday Pratap Singh
8. Marko Arsenovic

Citations in the corpus

• Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease (2019, background): “in [16] have presented a study of the com-”
• A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, background): “Biotic stress is infectious, transmissible, and more dangerous than abiotic stress [12].”
• Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection (2019, background): “One of the crucial drawbacks of the present study in the area of plant disease detection is a major drop in classification performance [25,34,35] of the models on real images taken in fields compared to the images from a controlled environment.”