A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications

Khan, Atiya; Vibhute, Amol D.; Mali, S. M.; Patil, C. H.

doi:10.1016/j.ecoinf.2022.101678

Cited by 177 publications

(60 citation statements)

References 47 publications

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“…The original spectral data collected by the hyperspectral system consists of a large number of wavebands with multiple collinearity ( Khan et al., 2022 ). XGBoost, LightGBM, CatBoost and GBDT rank the importance of 235 wavelength respectively, and the number of effective wavelengths they select is 61, 32, 46 and 66.…”

Section: Resultsmentioning

confidence: 99%

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Zou

et al. 2022

Front. Plant Sci.

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Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.

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Section: Resultsmentioning

confidence: 99%

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Zou

et al. 2022

Front. Plant Sci.

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“…In this sense, the upper as well as lower limits were confirmed for all the individuals restricted from the group of novel places P t+1 , as displayed in Eq. (10).…”

Section: Hoa Based Parameter Tuningmentioning

confidence: 99%

“…≤ y max y min + rand y max − y min otherwise (10) whereas rand offers arbitrary numbers with normal distribution amongst zero and one. Some individuals in the group of candidate solutions P t+1 is chosen as the novel hurricane eye if, and only if, the value of their objective function was superior to the present hurricane eye P t HE .…”

Section: Hoa Based Parameter Tuningmentioning

confidence: 99%

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Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Duhayyim¹,

Alsolai²,

Hassine³

et al. 2023

Computers, Materials &Amp; Continua

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Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of 3168 CMC, 2023, vol.74, no.2 the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

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“…In order to capture morphological data from agricultural product samples, HSI represents a significant technological advancement [16]. Many researchers have employed the HSI techniques with or without the combination of Machine Learning (ML)/ Deep Learning (DL) for various agricultural applications [17]- [20]. The most prominent techniques adopted for analyzing images using ML are K-means, convolutional neural network (CNN), artificial neural networks (ANN), support vector machines (SVM)), linear polarization, vegetation indices (e.g.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

et al. 2022

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for a project on "Application of IoT in Agriculture Sector" through the ICPS division under grant ID DST-319."ABSTRACT Agricultural productivity is the asset on which the world's economy thoroughly relies. This is one of the major causes that disease identification in fruits and plants occupies a salient role in farming space, as having disease disorders in them is obvious. There is a need to carry genuine supervision to avoid crucial consequences in vegetation; otherwise, corresponding vegetation standards, quantity, and productiveness gets affected. At present, a recognition system is required in the food handling industries to uplift the effectiveness of productivity to cope with demand in the community. The study has been carried out to perform a systematic literature review of research papers that deployed machine learning (ML) techniques in agriculture, applicable to the banana plant and fruit production. Thus; it could help upcoming researchers in their endeavors to identify the level and kind of research done so far. The authors investigated the problems related to banana crops such as disease classification, chilling injuries detection, ripeness, moisture content, etc. Moreover, the authors have also reviewed the deployed frameworks based on ML, sources of data collection, and the comprehensive results achieved for each study. Furthermore, ML architectures/techniques were evaluated using a range of performance measures. It has been observed that some studies used the PlantVillage dataset, a few have used Godliver and Scotnelson dataset, and the rest were based on either real-field image acquisition or on limited private datasets. Hence, more datasets are needed to be acquired to enhance the disease identification process and to handle the other kind of problems (e.g. chilling injuries detection, ripeness, etc.) present in the crops. Furthermore, the authors have also carried out a comparison of popular ML techniques like support vector machines, convolutional neural networks, regression, etc. to make differences in their performance. In this study, several research gaps are addressed, allowing for increased transparency in identifying different diseases even before symptoms arise and also for monitoring the abovementioned problems related to crops.

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A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications

Cited by 177 publications

References 47 publications

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

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