Deep learning techniques for hyperspectral image analysis in agriculture:A review

Mohamed Fadhlallah Guerri, Cosimo Distante, Paolo Spagnolo, Fares Bougourzi,
Abdelmalik Taleb-Ahmed

ISPRS Open Journal of Photogrammetry and Remote Sensing 12 (2024) 100062

A B S T R A C T
In recent years, there has been a growing emphasis on assessing and ensuring the quality of horticultural and
agricultural produce. Traditional methods involving field measurements, investigations, and statistical analyses
are labour-intensive, time-consuming, and costly. As a solution, Hyperspectral Imaging (HSI) has emerged as a
non-destructive and environmentally friendly technology. HSI has gained significant popularity as a new technology,
particularly for its promising applications in remote sensing, notably in agriculture. However, classifying
HSI data is highly complex because it involves several challenges, such as the excessive redundancy of spectral
bands, scarcity of training samples, and the intricate non-linear relationship between spatial positions and
spectral bands. Notably, Deep Learning (DL) techniques have demonstrated remarkable efficacy in various HSI
analysis tasks, including those within agriculture. As interest continues to surge in leveraging HSI data for
agricultural applications through DL approaches, a pressing need exists for a comprehensive survey that can
effectively navigate researchers through the significant strides achieved and the future promising research directions
in this domain. This literature review diligently compiles, analyzes, and discusses recent endeavours
employing DL methodologies. These methodologies encompass a spectrum of approaches, ranging from
Autoencoders (AE) to Convolutional Neural Networks (CNN) (in 1D, 2D, and 3D configurations), Recurrent
Neural Networks (RNN), Deep Belief Networks (DBN), Generative Adversarial Networks (GAN), Transfer
Learning (TL), Semi-Supervised Learning (SSL), Few-Shot Learning (FSL) and Active Learning (AL). These approaches
are tailored to address the unique challenges posed by agricultural HSI analysis. This review evaluates
and discusses the performance exhibited by these diverse approaches. To this end, the efficiency of these approaches
has been rigorously analyzed and discussed based on the results of the state-of-the-art papers on widely
recognized land cover datasets.

Keywords:
Hyperspectral imaging
, HSI,
Deep learning, Agriculture,
CNN,
RNN,
GAN