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Agriculture
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Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring
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Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (10 July 2024) | Viewed by 11101

Special Issue Editors

Dr. Aichen Wang

E-Mail Website
Guest Editor
School of Agricultural Engineering, Jiangsu University, Zhenjiang 210013, China
Interests: hyperspectral image analysis; crop protection; phenotyping; applied artificial intelligence; image processing; remote sensing; advanced machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Minglu Tian

E-Mail Website
Guest Editor
Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Interests: plant phenotyping; hyperspectral imaging; remote sensing; GIS
Dr. Liyuan Zhang

E-Mail Website
Guest Editor
Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang 212013, China
Interests: crop stress monitoring; UAV; phenotyping; image processing; machine learning

Special Issue Information

Dear Colleagues,

As the global population proliferates, greater pressure is placed on modern agriculture to produce more food. However, crops are facing various threats from abiotic and biotic stress, including drought, salt, freezing, diseases, insects, and weeds, among others. Accurately monitoring the growing status of crops in a timely manner under various stresses is crucial to crop cultivation, protection, phenotyping, as well as seed breeding. Optical sensing technology has been explored extensively for crop monitoring, with multi-/hyper-spectral imaging technologies that can provide both spectral and imaging information playing a vital role.

This Special Issue focuses on the development and application of multi-/hyper-spectral imaging equipment/systems and advanced analyzing algorithms in crop monitoring in the field or in greenhouses. This Special Issue will fully embrace inter- and trans-disciplinary studies from multiple domains (e.g., agricultural sciences, agricultural engineering, optical engineering,) in the co-construction of knowledge for sustainable agriculture. All types of articles, such as original research and review papers, are welcome.

Dr. Aichen Wang
Dr. Minglu Tian
Dr. Liyuan Zhang
Guest Editors

Manuscript Submission Information

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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. Agriculture is an international peer-reviewed open access monthly 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 2600 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

  • multi-/hyper-spectral imaging
  • crop monitoring
  • phenotyping
  • optical sensing
  • stress monitoring
  • machine learning
  • remote sensing
  • UAV

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Related Special Issue

Published Papers (8 papers)

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Research

20 pages, 4757 KiB  
Article
Combining Transfer Learning and Ensemble Algorithms for Improved Citrus Leaf Disease Classification
by Hongyan Zhu, Dani Wang, Yuzhen Wei, Xuran Zhang and Lin Li
Agriculture 2024, 14(9), 1549; https://doi.org/10.3390/agriculture14091549 - 7 Sep 2024
Cited by 1 | Viewed by 789
Abstract
Accurate categorization and timely control of leaf diseases are crucial for citrus growth. We proposed the Multi-Models Fusion Network (MMFN) for citrus leaf diseases detection based on model fusion and transfer learning. Compared to traditional methods, the algorithm (integrating transfer learning Alexnet, VGG, [...] Read more.
Accurate categorization and timely control of leaf diseases are crucial for citrus growth. We proposed the Multi-Models Fusion Network (MMFN) for citrus leaf diseases detection based on model fusion and transfer learning. Compared to traditional methods, the algorithm (integrating transfer learning Alexnet, VGG, and Resnet) we proposed can address the issues of limited categories, slow processing speed, and low recognition accuracy. By constructing efficient deep learning models and training and optimizing them with a large dataset of citrus leaf images, we ensured the broad applicability and accuracy of citrus leaf disease detection, achieving high-precision classification. Herein, various deep learning algorithms, including original Alexnet, VGG, Resnet, and transfer learning versions Resnet34 (Pre_Resnet34) and Resnet50 (Pre_Resnet50) were also discussed and compared. The results demonstrated that the MMFN model achieved an average accuracy of 99.72% in distinguishing between diseased and healthy leaves. Additionally, the model attained an average accuracy of 98.68% in the classification of multiple diseases (citrus huanglongbing (HLB), greasy spot disease and citrus canker), insect pests (citrus leaf miner), and deficiency disease (zinc deficiency). These findings conclusively illustrate that deep learning model fusion networks combining transfer learning and integration algorithms can automatically extract image features, enhance the automation and accuracy of disease recognition, demonstrate the significant potential and application value in citrus leaf disease classification, and potentially drive the development of smart agriculture. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring)
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20 pages, 3140 KiB  
Article
Detection of Rice Leaf SPAD and Blast Disease Using Integrated Aerial and Ground Multiscale Canopy Reflectance Spectroscopy
by Aichen Wang, Zishan Song, Yuwen Xie, Jin Hu, Liyuan Zhang and Qingzhen Zhu
Agriculture 2024, 14(9), 1471; https://doi.org/10.3390/agriculture14091471 - 28 Aug 2024
Viewed by 1034
Abstract
Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in [...] Read more.
Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in scale. Spectral and imaging technologies in the visible and near-infrared (Vis/NIR) region have been widely investigated for crop disease detection. This work explored the potential of integrating canopy reflectance spectra acquired near the ground and aerial multispectral images captured with an unmanned aerial vehicle (UAV) for estimating Soil-Plant Analysis Development (SPAD) values and detecting rice leaf blast disease in the field. Canopy reflectance spectra were preprocessed, followed by effective band selection. Different vegetation indices (VIs) were calculated from multispectral images and selected for model establishment according to their correlation with SPAD values and disease severity. The full-wavelength canopy spectra (450–850 nm) were first used for establishing SPAD inversion and blast disease classification models, demonstrating the effectiveness of Vis/NIR spectroscopy for SPAD inversion and blast disease detection. Then, selected effective bands from the canopy spectra, UAV VIs, and the fusion of the two data sources were used for establishing corresponding models. The results showed that all SPAD inversion models and disease classification models established with the integrated data performed better than corresponding models established with the single of either of the aerial and ground data sources. For SPAD inversion models, the best model based on a single data source achieved a validation determination coefficient (Rcv2) of 0.5719 and a validation root mean square error (RMSECV) of 2.8794, while after ground and aerial data fusion, these two values improved to 0.6476 and 2.6207, respectively. For blast disease classification models, the best model based on a single data source achieved an overall test accuracy of 89.01% and a Kappa coefficient of 0.86, and after data fusion, the two values improved to 96.37% and 0.95, respectively. These results indicated the significant potential of integrating canopy reflectance spectra and UAV multispectral images for detecting rice diseases in large fields. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring)
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14 pages, 7401 KiB  
Article
Classification of Apple Color and Deformity Using Machine Vision Combined with CNN
by Dekai Qiu, Tianhao Guo, Shengqi Yu, Wei Liu, Lin Li, Zhizhong Sun, Hehuan Peng and Dong Hu
Agriculture 2024, 14(7), 978; https://doi.org/10.3390/agriculture14070978 - 23 Jun 2024
Viewed by 1235
Abstract
Accurately classifying the quality of apples is crucial for maximizing their commercial value. Deep learning techniques are being widely adopted for apple quality classification tasks, achieving impressive results. While existing research excels at classifying apple variety, size, shape, and defects, color and deformity [...] Read more.
Accurately classifying the quality of apples is crucial for maximizing their commercial value. Deep learning techniques are being widely adopted for apple quality classification tasks, achieving impressive results. While existing research excels at classifying apple variety, size, shape, and defects, color and deformity analysis remain an under-explored area. Therefore, this study investigates the feasibility of utilizing convolutional neural networks (CNN) to classify the color and deformity of apples based on machine vision technology. Firstly, a custom-assembled machine vision system was constructed for collecting apple images. Then, image processing was performed to extract the largest fruit diameter from the 45 images taken for each apple, establishing an image dataset. Three classic CNN models (AlexNet, GoogLeNet, and VGG16) were employed with parameter optimization for a three-category classification task (non-deformed slice–red apple, non-deformed stripe–red apple, and deformed apple) based on apple features. VGG16 achieved the best results with an accuracy of 92.29%. AlexNet and GoogLeNet achieved 91.66% and 88.96% accuracy, respectively. Ablation experiments were performed on the VGG16 model, which found that each convolutional block contributed to the classification task. Finally, prediction using VGG16 was conducted with 150 apples and the prediction accuracy was 90.50%, which was comparable to or better than other existing models. This study provides insights into apple classification based on color and deformity using deep learning methods. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring)
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16 pages, 16614 KiB  
Article
An Improved YOLOv7-Tiny Method for the Segmentation of Images of Vegetable Fields
by Shouwei Wang, Lijian Yao, Lijun Xu, Dong Hu, Jiawei Zhou and Yexin Chen
Agriculture 2024, 14(6), 856; https://doi.org/10.3390/agriculture14060856 - 29 May 2024
Viewed by 890
Abstract
In response to the limitations of existing methods in differentiating between vegetables and all types of weeds in farmlands, a new image segmentation method is proposed based on the improved YOLOv7-tiny. Building on the original YOLOv7-tiny framework, we replace the CIoU loss function [...] Read more.
In response to the limitations of existing methods in differentiating between vegetables and all types of weeds in farmlands, a new image segmentation method is proposed based on the improved YOLOv7-tiny. Building on the original YOLOv7-tiny framework, we replace the CIoU loss function with the WIoU loss function, substitute the Leaky ReLU loss function with the SiLU activation function, introduce the SimAM attention mechanism in the neck network, and integrate the PConv convolution module into the backbone network. The improved YOLOv7-tiny is used for vegetable target detection, while the ExG index, in combination with the OTSU method, is utilized to obtain a foreground image that includes both vegetables and weeds. By integrating the vegetable detection results with the foreground image, a vegetable distribution map is generated. Subsequently, by excluding the vegetable targets from the foreground image using the vegetable distribution map, a single weed target is obtained, thereby achieving accurate segmentation between vegetables and weeds. The experimental results show that the improved YOLOv7-tiny achieves an average precision of 96.5% for vegetable detection, with a frame rate of 89.3 fps, Params of 8.2 M, and FLOPs of 10.9 G, surpassing the original YOLOv7-tiny in both detection accuracy and speed. The image segmentation algorithm achieves a mIoU of 84.8% and an mPA of 97.8%. This method can effectively segment vegetables and a variety of weeds, reduce the complexity of segmentation with good feasibility, and provide a reference for the development of intelligent plant protection robots. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring)
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17 pages, 13072 KiB  
Article
Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image
by Hui Deng, Wenjiang Zhang, Xiaoqian Zheng and Houxi Zhang
Agriculture 2024, 14(4), 548; https://doi.org/10.3390/agriculture14040548 - 29 Mar 2024
Cited by 3 | Viewed by 1323
Abstract
The accurate and timely identification of crops holds paramount significance for effective crop management and yield estimation. Unmanned aerial vehicle (UAV), with their superior spatial and temporal resolution compared to satellite-based remote sensing, offer a novel solution for precise crop identification. In this [...] Read more.
The accurate and timely identification of crops holds paramount significance for effective crop management and yield estimation. Unmanned aerial vehicle (UAV), with their superior spatial and temporal resolution compared to satellite-based remote sensing, offer a novel solution for precise crop identification. In this study, we evaluated a methodology that integrates object-oriented method and random forest (RF) algorithm for crop identification using multispectral UAV images. The process involved a multiscale segmentation algorithm, utilizing the optimal segmentation scale determined by Estimation of Scale Parameter 2 (ESP2). Eight classification schemes (S1–S8) were then developed by incorporating index (INDE), textural (GLCM), and geometric (GEOM) features based on the spectrum (SPEC) features of segmented objects. The best-trained RF model was established through three steps: feature selection, parameter tuning, and model training. Subsequently, we determined the feature importance for different classification schemes and generated a prediction map of vegetation for the entire study area based on the best-trained RF model. Our results revealed that S5 (SPEC + GLCM + INDE) outperformed others, achieving an impressive overall accuracy (OA) and kappa coefficient of 92.76% and 0.92, respectively, whereas S4 (SPEC + GEOM) exhibited the lowest performance. Notably, geometric features negatively impacted classification accuracy, while the other three feature types positively contributed. The accuracy of ginger, luffa, and sweet potato was consistently lower across most schemes, likely due to their unique colors and shapes, posing challenges for effective discrimination based solely on spectrum, index, and texture features. Furthermore, our findings highlighted that the most crucial feature was the INDE feature, followed by SPEC and GLCM, with GEOM being the least significant. For the optimal scheme (S5), the top 20 most important features comprised 10 SPEC, 7 INDE, and 3 GLCM features. In summary, our proposed method, combining object-oriented and RF algorithms based on multispectral UAV images, demonstrated high classification accuracy for crops. This research provides valuable insights for the accurate identification of various crops, serving as a reference for future advancements in agricultural technology and crop management strategies. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring)
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22 pages, 5073 KiB  
Article
Combinations of Feature Selection and Machine Learning Models for Object-Oriented “Staple-Crop-Shifting” Monitoring Based on Gaofen-6 Imagery
by Yujuan Cao, Jianguo Dai, Guoshun Zhang, Minghui Xia and Zhitan Jiang
Agriculture 2024, 14(3), 500; https://doi.org/10.3390/agriculture14030500 - 20 Mar 2024
Cited by 2 | Viewed by 1172
Abstract
This paper combines feature selection with machine learning algorithms to achieve object-oriented classification of crops in Gaofen-6 remote sensing images. The study provides technical support and methodological references for research on regional monitoring of food crops and precision agriculture management. “Staple-food-shifting” refers to [...] Read more.
This paper combines feature selection with machine learning algorithms to achieve object-oriented classification of crops in Gaofen-6 remote sensing images. The study provides technical support and methodological references for research on regional monitoring of food crops and precision agriculture management. “Staple-food-shifting” refers to the planting of other cash crops on cultivated land that should have been planted with staple crops such as wheat, rice, and maize, resulting in a change in the type of arable land cultivated. An accurate grasp of the spatial and temporal patterns of “staple-food-shifting” on arable land is an important basis for rationalizing land use and protecting food security. In this study, the Shihezi Reclamation Area in Xinjiang is selected as the study area, and Gaofen-6 satellite images are used to study the changes in the cultivated area of staple food crops and their regional distribution. Firstly, the images are segmented at multiple scales and four types of features are extracted, totaling sixty-five feature variables. Secondly, six feature selection algorithms are used to optimize the feature variables, and a total of nine feature combinations are designed. Finally, k-Nearest Neighbor (KNN), Random Forest (RF), and Decision Tree (DT) are used as the basic models of image classification to explore the best combination of feature selection method and machine learning model suitable for wheat, maize, and cotton classification. The results show that our proposed optimal feature selection method (OFSM) can significantly improve the classification accuracy by up to 15.02% compared to the Random Forest Feature Importance Selection (RF-FI), Random Forest Recursive Feature Elimination (RF-RFE), and XGBoost Feature Importance Selection (XGBoost-FI) methods. Among them, the OF-RF-RFE model constructed based on KNN performs the best, with the overall accuracy, average user accuracy, average producer accuracy, and kappa coefficient reaching 90.68%, 87.86%, 86.68%, and 0.84, respectively. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring)
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20 pages, 11990 KiB  
Article
Mapping Paddy Rice in Rice–Wetland Coexistence Zone by Integrating Sentinel-1 and Sentinel-2 Data
by Duan Huang, Lijie Xu, Shilin Zou, Bo Liu, Hengkai Li, Luoman Pu and Hong Chi
Agriculture 2024, 14(3), 345; https://doi.org/10.3390/agriculture14030345 - 21 Feb 2024
Viewed by 1575
Abstract
Accurate mapping of vegetation in the coexisting area of paddy fields and wetlands plays a key role in the sustainable development of agriculture and ecology, which is critical for national food security and ecosystem balance. The phenology-based rice mapping algorithm uses unique flooding [...] Read more.
Accurate mapping of vegetation in the coexisting area of paddy fields and wetlands plays a key role in the sustainable development of agriculture and ecology, which is critical for national food security and ecosystem balance. The phenology-based rice mapping algorithm uses unique flooding stages of paddy rice, and it has been widely used for rice mapping. However, wetlands with similar flooding signatures make rice extraction in rice–wetland coexistence challenging. In this study, we analyzed phenology differences between rice and wetlands based on the Sentinel-1/2 data and used the random forest algorithm to map vegetation in the Poyang Lake Basin, which is a typical rice–wetland coexistence zone in the south of China. The rice maps were validated with reference data, and the highest overall accuracy and Kappa coefficient was 0.94 and 0.93, respectively. First, monthly median composited and J-M distance methods were used to analyze radar and spectral data in key phenological periods, and it was found that the combination of the two approaches can effectively improve the confused signal between paddy rice and wetlands. Second, the VV and VH polarization characteristics of Sentinel-1 data enable better identification of wetlands and rice. Third, from 2018 to 2022, paddy rice in the Poyang Lake Basin showed the characteristics of planting structure around the Poyang Lake and its tributaries. The mudflats were mostly found in the middle and northeast of Poyang Lake, and the wetland vegetation was found surrounding the mudflats, forming a nibbling shape from the lake’s periphery to its center. Our study demonstrates the potential of mapping paddy rice in the rice–wetland coexistence zone using the combination of Sentinel-1 and Sentinel-2 imagery, which would be beneficial for balancing the changes between paddy rice and wetlands and improving the vulnerability of the local ecological environment. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring)
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16 pages, 4028 KiB  
Article
Segmentation of Wheat Lodging Areas from UAV Imagery Using an Ultra-Lightweight Network
by Guoqing Feng, Cheng Wang, Aichen Wang, Yuanyuan Gao, Yanan Zhou, Shuo Huang and Bin Luo
Agriculture 2024, 14(2), 244; https://doi.org/10.3390/agriculture14020244 - 1 Feb 2024
Cited by 3 | Viewed by 1260
Abstract
Crop lodging is an important cause of direct economic losses and secondary disease transmission in agricultural production. Most existing methods for segmenting wheat lodging areas use a large-volume network, which poses great difficulties for annotation and crop monitoring in real time. Therefore, an [...] Read more.
Crop lodging is an important cause of direct economic losses and secondary disease transmission in agricultural production. Most existing methods for segmenting wheat lodging areas use a large-volume network, which poses great difficulties for annotation and crop monitoring in real time. Therefore, an ultra-lightweight model, Lodging-U2NetP (L-U2NetP), based on a novel annotation strategy which crops the images before annotating them (Crop-annotation), was proposed and applied to RGB images of wheat captured with an unmanned aerial vehicle (UAV) at a height of 30 m during the maturity stage. In the L-U2NetP, the Dual Cross-Attention (DCA) module was firstly introduced into each small U-structure effectively to address semantic gaps. Then, Crisscross Attention (CCA) was used to replace several bulky modules for a stronger feature extraction ability. Finally, the model was compared with several classic networks. The results showed that the L-U2NetP yielded an accuracy, F1 score, and IoU (Intersection over Union) for segmenting of 95.45%, 93.11%, 89.15% and 89.72%, 79.95%, 70.24% on the simple and difficult sub-sets of the dataset (CA set) obtained using the Crop-annotation strategy, respectively. Additionally, the L-U2NetP also demonstrated strong robustness in the real-time detection simulations and the dataset (AC set) obtained using the mainstream annotation strategy, which annotates images before cropping (Annotation-crop). The results indicated that L-U2NetP could effectively extract wheat lodging and the Crop-annotation strategy provided a reliable performance which is comparable with that of the mainstream one. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring)
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