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AgriEngineering
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Hyperspectral Imaging Technique in Agriculture
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Hyperspectral Imaging Technique in Agriculture

A special issue of AgriEngineering (ISSN 2624-7402).

Deadline for manuscript submissions: closed (1 January 2023) | Viewed by 20081

Special Issue Editor

Dr. Zongmei Gao

E-Mail Website
Guest Editor
Department of Neurology, Columbia University, New York, NY 10033, USA
Interests: data mining; machine learning; image processing; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, precision agriculture (PA) has been urgently needed for whole-farm management. Optical sensing, which is among the most useful PA tools, has been widely applied in detecting crop and food responses. Among these techniques, hyperspectral imaging has been adopted as a powerful method to achieve such targets. HSI acquires a three-dimensional dataset called a hypercube, with two spatial dimensions and one spectral dimension, from visible to near-infrared or even short infrared wavelengths. Spatially resolved spectral imaging obtained by HSI provides diagnostic information on tissue physiology, morphology, and composition. This Special Issue will focus on hyperspectral imaging techniques in agriculture—from the fundamentals of sensing systems to novel applications for agricultural purposes.

The large-scale farming of agricultural crops requires on-time detection management. Hyperspectral remote sensing data taken from low-altitude flights usually have high spectral and spatial resolutions, while the application of such a technique is affected by the precision and accuracy under field conditions. In addition to sensors, reducing the high dimensionality of hypercubes is also becoming important for agriculture. Hyperspectral imaging applied in labs or fields could contribute to improvements in data collection, data mining, and result validation.

This Special Issue aims to promote the development of hyperspectral imaging techniques in agriculture. We would like to invite the scientific community to submit their research related to hyperspectral imaging in agriculture. Contributions could include, but are not limited to, the following: precision agriculture, hyperspectral imaging, smart farming, remote sensing, platforms and sensors, machine vision, robotics in agriculture, the Internet of Things, machine learning, and deep learning.

Dr. Zongmei Gao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hyperspectral imaging
  • precision agriculture
  • remote sensing
  • platforms and sensors
  • machine vision
  • Internet of Things
  • machine learning
  • deep learning
  • artificial intelligence

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Published Papers (5 papers)

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8 pages, 2194 KiB  
Article
Detection Method of Straw Mulching Unevenness with RGB-D Sensors
by Yuanyuan Shao, Xianlu Guan, Guantao Xuan, Xiaoteng Li, Fengwei Gu, Junteng Ma, Feng Wu and Zhichao Hu
AgriEngineering 2023, 5(1), 12-19; https://doi.org/10.3390/agriengineering5010002 - 21 Dec 2022
Cited by 1 | Viewed by 2271
Abstract
Returning straw to the field is very important of for the conservation tillage to increase land fertility. It is vital to detect the unevenness of the straw covering to evaluate the performance of no-tillage planter, especially for the ones with returning full amount [...] Read more.
Returning straw to the field is very important of for the conservation tillage to increase land fertility. It is vital to detect the unevenness of the straw covering to evaluate the performance of no-tillage planter, especially for the ones with returning full amount of straw. In this study, two kinds of RGB-D(Red, Green, Blue-Depth) sensors (RealSense D435i and Kinect v2) were applied to estimate the straw mulching unevenness by detecting the depth of straw coverage. Firstly, the overall structure and working principle of no-tillage planter with returning the full amount of straw was introduced. Secondly, field images were captured with the two kinds of RGB-D sensors after no tillage planter operation. Thirdly, straw covering unevenness computing was carried on a system developed by Matlab. Finally, the correlation analysis was conducted to test for the relationship between the straw covering unevenness by manual and deep sensors, with R (correlation coefficient) of 0.93, RMSE(Root Mean Square Error) of 4.59% and MAPE(Mean of Absolute Percentage Error) of 3.86% with D435i sensor, and with R of 0.915, RMSE of 6.53% and MAPE of 13.85% with Kinect V2, which showed both kinds of RGB-D sensors can acquire the unevenness of straw covering efficiently. The finding can provide a potential way to detect the unevenness of straw coverage and data support for operation evaluation and improvement of no-tillage planter. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Technique in Agriculture)
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14 pages, 4759 KiB  
Article
Prediction of Potassium in Peach Leaves Using Hyperspectral Imaging and Multivariate Analysis
by Megan Io Ariadne Abenina, Joe Mari Maja, Matthew Cutulle, Juan Carlos Melgar and Haibo Liu
AgriEngineering 2022, 4(2), 400-413; https://doi.org/10.3390/agriengineering4020027 - 21 Apr 2022
Cited by 15 | Viewed by 3325
Abstract
Hyperspectral imaging (HSI) is an emerging technology being utilized in agriculture. This system could be used to monitor the overall health of plants or in pest/disease detection. As sensing technology advancement expands, measuring nutrient levels and disease detection also progresses. This study aimed [...] Read more.
Hyperspectral imaging (HSI) is an emerging technology being utilized in agriculture. This system could be used to monitor the overall health of plants or in pest/disease detection. As sensing technology advancement expands, measuring nutrient levels and disease detection also progresses. This study aimed to predict three different levels of potassium (K) concentration in peach leaves using principal component analysis (PCA) and develop models for predicting the K concentration of a peach leaf using a hyperspectral imaging technique. Hyperspectral images were acquired from a randomly selected fresh peach leaf from multiple trees over the spectral region between 500 and 900 nm. Leaves were collected from trees with varying potassium levels of high (2.7~3.2%), medium (2.0~2.6%), and low (1.3~1.9%). Four pretreatment methods (multiplicative scatter effect (MSC), Savitzky–Golay first derivative, Savitzky–Golay second derivative, and standard normal variate (SNV)) were applied to the raw data and partial least square (PLS) was used to develop a model for each of the pretreatments. The R2 values for each pretreatment method were 0.8099, 0.6723, 0.5586, and 0.8446, respectively. The SNV prediction model has the highest accuracy and was used to predict the K nutrient using the validation data. The result showed a slightly lower R2 = 0.8101 compared with the training. This study showed that HSI could measure K concentration in peach tree cultivars. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Technique in Agriculture)
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13 pages, 24260 KiB  
Article
Oil Palm Yield Estimation Based on Vegetation and Humidity Indices Generated from Satellite Images and Machine Learning Techniques
by Fernando Watson-Hernández, Natalia Gómez-Calderón and Rouverson Pereira da Silva
AgriEngineering 2022, 4(1), 279-291; https://doi.org/10.3390/agriengineering4010019 - 3 Mar 2022
Cited by 16 | Viewed by 5828
Abstract
Palm oil has become one of the most consumed vegetable oils in the world, and it is a key element in profitable global value chains. In Costa Rica, oil palm cultivation is one of the three crops with the largest occupied agricultural area. [...] Read more.
Palm oil has become one of the most consumed vegetable oils in the world, and it is a key element in profitable global value chains. In Costa Rica, oil palm cultivation is one of the three crops with the largest occupied agricultural area. The objective of this study was to explain and predict yield in safe time lags for production management by using free-access satellite images. To this end, machine learning methods were performed to a 20-year data set of an oil palm plantation located in the Central Pacific Region of Costa Rica and the corresponding vegetation indices obtained from LANDSAT satellite images. Since the best correlations corresponded to a one-year time lag, the predictive models Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Recursive Partitioning and Regression Trees (RPART), and Neural Network (NN) were built for a Time-lag 1. These models were applied to all genetic material and to the predominant variety (AVROS) separately. While NN showed the best performance for multispecies information (r2 = 0.8139, NSE = 0.8131, RMSE = 0.3437, MAE = 0.2605), RF showed a better fit for AVROS (r2 = 0.8214, NSE = 0.8020, RMSE = 0.3452, MAE = 0.2669). The most relevant vegetation indices (NDMI, MSI) are related to water in the plant. The study also determined that data distribution must be considered for the prediction and evaluation of the oil palm yield in the area under study. The estimation methods of this study provide information on the identification of important variables (NDMI) to characterize palm oil yield. Additionally, it generates a scenario with acceptable uncertainties on the yield forecast one year in advance. This information is of direct interest to the oil palm industry. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Technique in Agriculture)
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24 pages, 11839 KiB  
Article
An Improved Method of an Image Mosaic of a Tea Garden and Tea Tree Target Extraction
by Jinzhu Lu, Yishan Xu and Zongmei Gao
AgriEngineering 2022, 4(1), 231-254; https://doi.org/10.3390/agriengineering4010017 - 25 Feb 2022
Cited by 3 | Viewed by 2552
Abstract
UAV may be limited by its flight height and camera resolution when aerial photography of a tea garden is carried out. The images of the tea garden contain trees and weeds whose vegetation information is similar to tea tree, which will affect tea [...] Read more.
UAV may be limited by its flight height and camera resolution when aerial photography of a tea garden is carried out. The images of the tea garden contain trees and weeds whose vegetation information is similar to tea tree, which will affect tea tree extraction for further agricultural analysis. In order to obtain a high-definition large field-of-view tea garden image that contains tea tree targets, this paper (1) searches for the suture line based on the graph cut method in the image stitching technology; (2) improves the energy function to realize the image stitching of the tea garden; and (3) builds a feature vector to accurately extract tea tree vegetation information and remove unnecessary variables, such as trees and weeds. By comparing this with the manual extraction, the algorithm in this paper can effectively distinguish and eliminate most of the interference information. The IOU in a single mosaic image was more than 80% and the omissions account was 10%. The extraction results in accuracies that range from 84.91% to 93.82% at the different height levels (30 m, 60 m and 100 m height) of single images. Tea tree extraction accuracy rates in the mosaic images are 84.96% at a height of 30 m, and 79.94% at a height of 60 m. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Technique in Agriculture)
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18 pages, 2280 KiB  
Commentary
The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat
by Yiting Xie, Darren Plett and Huajian Liu
AgriEngineering 2021, 3(4), 924-941; https://doi.org/10.3390/agriengineering3040058 - 25 Nov 2021
Cited by 12 | Viewed by 4424
Abstract
Crown rot disease is caused by Fusarium pseudograminearum and is one of the major stubble-soil fungal diseases threatening the cereal industry globally. It causes failure of grain establishment, which brings significant yield loss. Screening crops affected by crown rot is one of the [...] Read more.
Crown rot disease is caused by Fusarium pseudograminearum and is one of the major stubble-soil fungal diseases threatening the cereal industry globally. It causes failure of grain establishment, which brings significant yield loss. Screening crops affected by crown rot is one of the key tools to manage crown rot, because it is necessary to understand disease infection conditions, identify the severity of infection, and discover potential resistant varieties. However, screening crown rot is challenging as there are no clear visible symptoms on leaves at early growth stages. Hyperspectral imaging (HSI) technologies have been successfully used to better understand plant health and disease incidence, including light absorption rate, water and nutrient distribution, and disease classification. This suggests HSI imaging technologies may be used to detect crown rot at early growing stages, however, related studies are limited. This paper briefly describes the symptoms of crown rot disease and traditional screening methods with their limitations. It, then, reviews state-of-art imaging technologies for disease detection, from color imaging to hyperspectral imaging. In particular, this paper highlights the suitability of hyperspectral-based screening methods for crown rot disease. A hypothesis is presented that HSI can detect crown-rot-infected plants before clearly visible symptoms on leaves by sensing the changes of photosynthesis, water, and nutrients contents of plants. In addition, it describes our initial experiment to support the hypothesis and further research directions are described. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Technique in Agriculture)
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