Big Data and Machine Learning With Hyperspectral Information in Agriculture

Abstract

Hyperspectral and multispectral information processing systems and technologies have demonstrated its usefulness for the improvement of agricultural productivity and practices by providing useful information to farmers and crop managers on the factors affecting crop status and growth. These technologies are widely used in a range of agriculture applications such as crop management, crop yield forecasting, crop disease detection, and the monitoring of agriculture land usage, water, and soil conditions. Hyperspectral information sensing can acquire several hundred spectral bands that cover the electromagnetic spectrum of an observational scene in a single acquisition. The resulting hyperspectral data cube contains a large volume of spatial and spectral information. The hyperspectral sequence of images or video further increases the data generation velocity and volume which lead to the Big data challenges particularly in agricultural remote sensing applications. This paper is structured to first give a comprehensive review of representative studies to provide insights into significant research efforts in agriculture using Big data, machine learning and deep learning with the focus on frameworks or architectures, information processing and analytics with hyperspectral and multispectral data. The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising. The paper then further explores the potential of using ensemble machine learning and scalable parallel discriminant analysis which takes into consideration the spatial and spectral components for Big data in agriculture. To the best of our knowledge, no similar review study on agriculture with Big data, machine learning and deep learning for hyperspectral and multispectral information processing has been reported. Furthermore, the potential of ensemble machine learning and scalable parallel discriminant analysis has not been explored in agriculture information processing. Experiments and data analytics have been performed on hyperspectral data from agriculture for validation. The results have shown the good performance of our approach.

Machine Learning in Agriculture: A Comprehensive Updated Review

“…Furthermore, other studies were concerned with the implementation of ML methods regarding the main grain crops by investigating different aspects including quality and disease detection [ 29 ]. Finally, focus has been paid on big data analysis using ML, aiming at finding out real-life problems that originated from smart farming [ 30 ], or dealing with methods to analyze hyperspectral and multispectral data [ 31 ].…”

“…Consequently, more practical datasets coming from fields are required [ 18 , 20 ]. Moreover, the need for more efficient ML algorithms and scalable computational architectures has been pointed out, which can lead to rapid information processing [ 18 , 22 , 23 , 31 ]. The challenging background, when it comes to obtaining images, video, or audio recordings, has also been mentioned owing to changes in lighting [ 16 , 29 ], blind spots of cameras, environmental noise, and simultaneous vocalizations [ 25 ].…”

“…For this reason, more user-friendly systems should be developed. In particular, simple systems, being easy to understand and operate, would be valuable, as for example a visualization tool with a user-friendly interface for the correct presentation and manipulation of data [ 25 , 30 , 31 ]. Taking into account that farmers are getting more and more familiar with smartphones, specific smartphone applications have been proposed as a possible solution to address the above challenge [ 15 , 16 , 21 ].…”

Fast Identification of Soybean Seed Varieties Using Laser-Induced Breakdown Spectroscopy Combined With Convolutional Neural Network

“…On account of kernel trick and structural risk minimization principles, SVM usually presents a better performance in classification and regression (Hesami et al, 2020). It has been applied in various fields in agriculture (Ang and Seng, 2021), such as plant breeding (Yoosefzadeh-Najafabadi et al, 2021), pest detection (Ebrahimi et al, 2017), and soil condition prediction (Morellos et al, 2016). However, SVM usually takes a long time to search for optimal parameters.…”

Locally Homogeneous Covariance Matrix Representation for Hyperspectral Image Classification

“…Different from ordinary RGB images, hyperspectral images (HSIs) usually contain hundreds of spectral channels from ultraviolet to infrared, which provides valuable information for detailed material analysis [1], [2]. Therefore, HSIs have been widely applied in many fields, such as environmental monitoring, agriculture [3], medical diagnosis, and target detection [1], [4], [5]. In the last few decades, HSI classification technology, which assigns a unique class label to each pixel, has attracted great attention in the field of remote sensing [6].…”