A review of neural networks in plant disease detection using hyperspectral data

By K. Golhani, Siva K. Balasundram, G. Vadamalai, Biswajeet Pradhan. Information Processing in Agriculture, 2018

377 citations recorded by the source corpus.

Abstract This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a review on NN mechanism, types, models, and classifiers that use different algorithms to process hyperspectral data. Then we highlight the current state of imaging and non-imaging hyperspectral data for early disease detection. The hybridization of NN-hyperspectral approach has emerged as a powerful tool for disease detection and diagnosis. Spectral Disease Index (SDI) is the ratio of different spectral bands of pure disease spectra. Subsequently, we introduce NN techniques for rapid development of SDI. We also highlight current challenges and future trends of hyperspectral data.

Subfields: Analytical Chemistry, Media Technology, Plant Science

View Paper: https://www.semanticscholar.org/paper/be28092478faa4df11063fecaef53370de6a29e7

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. Johan Potgieter
2. José García-Nieto
3. Muhammad Hammad Saleem
4. Rafia Mumtaz
5. Khalid Mahmood Arif

In Media Technology

1. Johan Potgieter
2. Muhammad Hammad Saleem
3. Khalid Mahmood Arif

In Plant Science

1. Johan Potgieter
2. Muhammad Hammad Saleem
3. Sanjeev Jain
4. Uday Pratap Singh
5. Khalid Mahmood Arif
6. Siddharth Singh Chouhan
7. Sukirty Jain

Citations in the corpus

• Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease (2019, methodology): “in [2] has present various studies of neural network approaches used for the identification and classification of the disease from the leave images of the plant.”
• Precision Agriculture Techniques and Practices: From Considerations to Applications (2019, background): “Using hyper-spectral images, a system was presented [51] to identify disease or pest attack in crops.”
• Plant Disease Detection and Classification by Deep Learning (2019, background): “…of plant disease detection, this is still a fertile area of research and should lead to improvements for better detection of plants’ diseases [115] in di ff erent situations, like various conditions of illumination, considering real background, etc. wrong classification by RF model [114].”