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Zununjan Z, Turghan MA, Sattar M, Kasim N, Emin B, Abliz A. Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices. PLANT METHODS 2024; 20:97. [PMID: 38909230 PMCID: PMC11193302 DOI: 10.1186/s13007-024-01224-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 06/10/2024] [Indexed: 06/24/2024]
Abstract
Leaf water content (LWC) is a vital indicator of crop growth and development. While visible and near-infrared (VIS-NIR) spectroscopy makes it possible to estimate crop leaf moisture, spectral preprocessing and multiband spectral indices have important significance in the quantitative analysis of LWC. In this work, the fractional order derivative (FOD) was used for leaf spectral processing, and multiband spectral indices were constructed based on the band-optimization algorithm. Eventually, an integrated index, namely, the multiband spectral index (MBSI) and moisture index (MI), is proposed to estimate the LWC in spring wheat around Fu-Kang City, Xinjiang, China. The MBSIs for LWC were calculated from two types of spectral data: raw reflectance (RR) and the spectrum based on FOD. The LWC was estimated by combining machine learning (K-nearest neighbor, KNN; support vector machine, SVM; and artificial neural network, ANN). The results showed that the fractional derivative pretreatment of spectral data enhances the implied information of the spectrum (the maximum correlation coefficient appeared using a 0.8-order differential) and increases the number of sensitive bands, especially in the near-infrared bands (700-1100 nm). The correlations between LWC and the two-band index (RVI1156, 1628 nm), three-band indices (3BI-3(766, 478, 1042 nm), 3BI-4(1129, 1175, 471 nm), 3BI-5(814, 929, 525 nm), 3BI-6(1156, 1214, 802 nm), 3BI-7(929, 851, 446 nm)) based on FOD were higher than that of moisture indices and single-band spectrum, with r of - 0.71**, 0.74**, 0.73**, - 0.72**, 0.75** and - 0.76** for the correlation. The prediction accuracy of the two-band spectral indices (DVI(698, 1274 nm) DVI(698, 1274 nm) DVI(698, 1274 nm)) was higher than that of the moisture spectral index, with R2 of 0.81 and R2 of 0.79 for the calibration and validation, respectively. Due to a large amount of spectral indices, the correlation coefficient method was used to select the characteristic spectral index from full three-band indices. Among twenty seven models, the FWBI-3BI- 0.8 order model performed the best predictive ability (with an R2 of 0.86, RMSE of 2.11%, and RPD of 2.65). These findings confirm that combining spectral index optimization with machine learning is a highly effective method for inverting the leaf water content in spring wheat.
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Affiliation(s)
- Zinhar Zununjan
- School of Resources and Environment, Yili Normal University, Yining, 835000, China
| | - Mardan Aghabey Turghan
- State Key Laboratory of Oasis and Desert Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Mutallip Sattar
- College of Information Management, Xinjiang University of Finance and Economics, Urumqi, 830012, China
| | - Nijat Kasim
- School of Resources and Environment, Yili Normal University, Yining, 835000, China.
| | - Bilal Emin
- School of Resources and Environment, Yili Normal University, Yining, 835000, China
| | - Abdugheni Abliz
- College of Resources and Environmental Sciences, Xinjiang University, Urumqi, 830046, China
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Tang R, Wei S, Jianxun T, Aridas NK, Talip MSA. A method for durian precise fertilization based on improved radial basis neural network algorithm. FRONTIERS IN PLANT SCIENCE 2024; 15:1387977. [PMID: 38903447 PMCID: PMC11188315 DOI: 10.3389/fpls.2024.1387977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 05/08/2024] [Indexed: 06/22/2024]
Abstract
Introduction Durian is one of the tropical fruits that requires soil nutrients in its cultivation. It is important to understand the relationship between the content of critical nutrients, such as nitrogen (N), phosphorus (P), and potassium (K) in the soil and durian yield. How to optimize the fertilization plan is also important to the durian planting. Methods Thus, this study proposes an Improved Radial Basis Neural Network Algorithm (IM-RBNNA) in the durian precision fertilization. It uses the gray wolf algorithm to optimize the weights and thresholds of the RBNNA algorithm, which can improve the prediction accuracy of the RBNNA algorithm for the soil nutrient content and its relationship with the durian yield. It also collects the soil nutrients and historical yield data to build the IM-RBNNA model and compare with other similar algorithms. Results The results show that the IM-RBNNA algorithm is better than the other three algorithms in the average relative error, average absolute error, and coefficient of determination between the predicted and true values of soil N, K, and P fertilizer contents. It also predicts the relationship between soil nutrients and yield, which is closer to the true value. Discussion It shows that the IM-RBNNA algorithm can accurately predict the durian soil nutrient content and yield, which is benefited for farmers to make agronomic plans and management strategies. It uses soil nutrient resources efficiently, which reduces the environmental negative impacts. It also ensures that the durian tree can obtain the appropriate amount of nutrients, maximize its growth potential, reduce production costs, and increase yields.
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Affiliation(s)
- Ruipeng Tang
- Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Sun Wei
- Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Tang Jianxun
- Faculty of Electronics and Electrical Engineering, Zhaoqing University, Zhaoqing, Guangdong, China
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Opara IK, Opara UL, Okolie JA, Fawole OA. Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review. PLANTS (BASEL, SWITZERLAND) 2024; 13:1200. [PMID: 38732414 PMCID: PMC11085577 DOI: 10.3390/plants13091200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024]
Abstract
The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.
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Affiliation(s)
- Ikechukwu Kingsley Opara
- SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa; (I.K.O.); (U.L.O.)
- Department of Food Science, Stellenbosch University, Stellenbosch 7600, South Africa
| | - Umezuruike Linus Opara
- SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa; (I.K.O.); (U.L.O.)
- UNESCO International Centre for Biotechnology, Nsukka 410001, Enugu State, Nigeria
| | - Jude A. Okolie
- Gallogly College of Engineering, University of Oklahoma, Norman, OK 73019, USA;
| | - Olaniyi Amos Fawole
- Postharvest and Agroprocessing Research Centre, Department of Botany and Plant Biotechnology, University of Johannesburg, Johannesburg 2006, South Africa
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Strzępek K, Salach M, Trybus B, Siwiec K, Pawłowicz B, Paszkiewicz A. Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:9251. [PMID: 38005637 PMCID: PMC10675671 DOI: 10.3390/s23229251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/06/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023]
Abstract
This article presents an integrated system that uses the capabilities of unmanned aerial vehicles (UAVs) to perform a comprehensive crop analysis, combining qualitative and quantitative evaluations for efficient agricultural management. A convolutional neural network-based model, Detectron2, serves as the foundation for detecting and segmenting objects of interest in acquired aerial images. This model was trained on a dataset prepared using the COCO format, which features a variety of annotated objects. The system architecture comprises a frontend and a backend component. The frontend facilitates user interaction and annotation of objects on multispectral images. The backend involves image loading, project management, polygon handling, and multispectral image processing. For qualitative analysis, users can delineate regions of interest using polygons, which are then subjected to analysis using the Normalized Difference Vegetation Index (NDVI) or Optimized Soil Adjusted Vegetation Index (OSAVI). For quantitative analysis, the system deploys a pre-trained model capable of object detection, allowing for the counting and localization of specific objects, with a focus on young lettuce crops. The prediction quality of the model has been calculated using the AP (Average Precision) metric. The trained neural network exhibited robust performance in detecting objects, even within small images.
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Affiliation(s)
- Krzysztof Strzępek
- The Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland
| | - Mateusz Salach
- Department of Complex Systems, Rzeszow University of Technology, 35-959 Rzeszow, Poland
| | - Bartosz Trybus
- Department of Computer and Control Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland
| | - Karol Siwiec
- The Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland
| | - Bartosz Pawłowicz
- Department of Electronic and Telecommunications Systems, Rzeszow University of Technology, 35-959 Rzeszow, Poland
| | - Andrzej Paszkiewicz
- Department of Complex Systems, Rzeszow University of Technology, 35-959 Rzeszow, Poland
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Zhou H, Yang J, Lou W, Sheng L, Li D, Hu H. Improving grain yield prediction through fusion of multi-temporal spectral features and agronomic trait parameters derived from UAV imagery. FRONTIERS IN PLANT SCIENCE 2023; 14:1217448. [PMID: 37908835 PMCID: PMC10613988 DOI: 10.3389/fpls.2023.1217448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 09/27/2023] [Indexed: 11/02/2023]
Abstract
Rapid and accurate prediction of crop yield is particularly important for ensuring national and regional food security and guiding the formulation of agricultural and rural development plans. Due to unmanned aerial vehicles' ultra-high spatial resolution, low cost, and flexibility, they are widely used in field-scale crop yield prediction. Most current studies used the spectral features of crops, especially vegetation or color indices, to predict crop yield. Agronomic trait parameters have gradually attracted the attention of researchers for use in the yield prediction in recent years. In this study, the advantages of multispectral and RGB images were comprehensively used and combined with crop spectral features and agronomic trait parameters (i.e., canopy height, coverage, and volume) to predict the crop yield, and the effects of agronomic trait parameters on yield prediction were investigated. The results showed that compared with the yield prediction using spectral features, the addition of agronomic trait parameters effectively improved the yield prediction accuracy. The best feature combination was the canopy height (CH), fractional vegetation cover (FVC), normalized difference red-edge index (NDVI_RE), and enhanced vegetation index (EVI). The yield prediction error was 8.34%, with an R2 of 0.95. The prediction accuracies were notably greater in the stages of jointing, booting, heading, and early grain-filling compared to later stages of growth, with the heading stage displaying the highest accuracy in yield prediction. The prediction results based on the features of multiple growth stages were better than those based on a single stage. The yield prediction across different cultivars was weaker than that of the same cultivar. Nevertheless, the combination of agronomic trait parameters and spectral indices improved the prediction among cultivars to some extent.
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Affiliation(s)
- Hongkui Zhou
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Jianhua Yang
- Academy of Eco-civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin, China
| | - Weidong Lou
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Li Sheng
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Dong Li
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Hao Hu
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
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Namazi F, Ezoji M, Parmehr EG. Paddy Rice mapping in fragmented lands by improved phenology curve and correlation measurements on Sentinel-2 imagery in Google earth engine. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1220. [PMID: 37718323 DOI: 10.1007/s10661-023-11808-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 08/30/2023] [Indexed: 09/19/2023]
Abstract
Accurate and timely rice crop mapping is important to address the challenges of food security, water management, disease transmission, and land use change. However, accurate rice crop mapping is difficult due to the presence of mixed pixels in small and fragmented rice fields as well as cloud cover. In this paper, a phenology-based method using Sentinel-2 time series images is presented to solve these problems. First, the improved rice phenology curve is extracted based on Normalized Difference Vegetation Index and Land Surface Water Index time series data of rice fields. Then, correlation was taken between rice phenology curve and time series data of each pixel. The correlation result of each pixel shows the similarity of its time series behavior with the proposed rice phenology curve. In the next step, the maximum correlation value and its occurrence time are used as the feature vectors of each pixel to classification. Since correlation measurement provides data with better separability than its input data, training the classifier can be done with fewer samples and the classification is more accurate. The implementation of the proposed correlation-based algorithm can be done in a parallel computing. All the processes were performed on the Google Earth Engine cloud platform on the time series images of the Sentinel 2. The implementations show the high accuracy of this method.
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Affiliation(s)
- Fateme Namazi
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Mehdi Ezoji
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.
| | - Ebadat Ghanbari Parmehr
- Department of Geomatics, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran
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Kavaliauskas A, Žydelis R, Castaldi F, Auškalnienė O, Povilaitis V. Predicting Maize Theoretical Methane Yield in Combination with Ground and UAV Remote Data Using Machine Learning. PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12091823. [PMID: 37176880 PMCID: PMC10181051 DOI: 10.3390/plants12091823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023]
Abstract
The accurate, timely, and non-destructive estimation of maize total-above ground biomass (TAB) and theoretical biochemical methane potential (TBMP) under different phenological stages is a substantial part of agricultural remote sensing. The assimilation of UAV and machine learning (ML) data may be successfully applied in predicting maize TAB and TBMP; however, in the Nordic-Baltic region, these technologies are not fully exploited. Therefore, in this study, during the maize growing period, we tracked unmanned aerial vehicle (UAV) based multispectral bands (blue, red, green, red edge, and infrared) at the main phenological stages. In the next step, we calculated UAV-based vegetation indices, which were combined with field measurements and different ML models, including generalized linear, random forest, as well as support vector machines. The results showed that the best ML predictions were obtained during the maize blister (R2)-Dough (R4) growth period when the prediction models managed to explain 88-95% of TAB and 88-97% TBMP variation. However, for the practical usage of farmers, the earliest suitable timing for adequate TAB and TBMP prediction in the Nordic-Baltic area is stage V7-V10. We conclude that UAV techniques in combination with ML models were successfully applied for maize TAB and TBMP estimation, but similar research should be continued for further improvements.
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Affiliation(s)
- Ardas Kavaliauskas
- Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, Instituto Ave. 1, 58344 Akademija, Lithuania
| | - Renaldas Žydelis
- Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, Instituto Ave. 1, 58344 Akademija, Lithuania
| | - Fabio Castaldi
- Institute of BioEconomy, National Research Council of Italy (CNR), Via Giovanni Caproni 8, 50145 Firenze, Italy
| | - Ona Auškalnienė
- Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, Instituto Ave. 1, 58344 Akademija, Lithuania
| | - Virmantas Povilaitis
- Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, Instituto Ave. 1, 58344 Akademija, Lithuania
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Hassan SI, Alam MM, Illahi U, Mohd Suud M. A new deep learning-based technique for rice pest detection using remote sensing. PeerJ Comput Sci 2023; 9:e1167. [PMID: 37346729 PMCID: PMC10280224 DOI: 10.7717/peerj-cs.1167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 12/01/2022] [Indexed: 06/23/2023]
Abstract
Background Agriculture plays a vital role in the country's economy and human society. Rice production is mainly focused on financial improvements as it is demanding worldwide. Protecting the rice field from pests during seedling and after production is becoming a challenging research problem. Identifying the pest at the right time is crucial so that the measures to prevent rice crops from pests can be taken by considering its stage. In this article, a new deep learning-based pest detection model is proposed. The proposed system can detect two types of rice pests (stem borer and Hispa) using an unmanned aerial vehicle (UAV). Methodology The image is captured in real time by a camera mounted on the UAV and then processed by filtering, labeling, and segmentation-based technique of color thresholding to convert the image into greyscale for extracting the region of interest. This article provides a rice pests dataset and a comparative analysis of existing pre-trained models. The proposed approach YO-CNN recommended in this study considers the results of the previous model because a smaller network was regarded to be better than a bigger one. Using additional layers has the advantage of preventing memorization, and it provides more precise results than existing techniques. Results The main contribution of the research is implementing a new modified deep learning model named Yolo-convolution neural network (YO-CNN) to obtain a precise output of up to 0.980 accuracies. It can be used to reduce rice wastage during production by monitoring the pests regularly. This technique can be used further for target spraying that saves applicators (fertilizer water and pesticide) and reduces the adverse effect of improper use of applicators on the environment and human beings.
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Affiliation(s)
- Syeda Iqra Hassan
- Universiti Kuala Lumpur British Malaysian Institute, Kuala Lumpur, Malaysia
- Department of Electrical Engineering, Ziauddin University, Karachi, Pakistan
| | - Muhammad Mansoor Alam
- Faculty of Computing, Riphah International University, Islamabad, Pakistan
- Malaysian Institute of Information Technology, University of Kuala Lumpur, Kuala Lumpur, Malaysia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
- Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia
| | - Usman Illahi
- Electrical Engineering Department, Faculty of Engineering and Technology, Gomal University Dera Ismail Khan, Dera Ismail Khan, Pakistan
| | - Mazliham Mohd Suud
- Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia
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Assa BG, Bhowmick A, Cholo BE. Modeling Nitrogen Balance for Pre-Assessment of Surface and Groundwater Nitrate (NO3-−N) Contamination from N–Fertilizer Application Loss: a Case of the Bilate Downstream Watershed Cropland. WATER, AIR, & SOIL POLLUTION 2023; 234:105. [DOI: https:/doi.org/10.1007/s11270-023-06114-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 01/18/2023] [Indexed: 03/09/2024]
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Xiao Q, Wu N, Tang W, Zhang C, Feng L, Zhou L, Shen J, Zhang Z, Gao P, He Y. Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves. FRONTIERS IN PLANT SCIENCE 2022; 13:1080745. [PMID: 36643292 PMCID: PMC9834998 DOI: 10.3389/fpls.2022.1080745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Leaf nitrogen concentration (LNC) is a critical indicator of crop nutrient status. In this study, the feasibility of using visible and near-infrared spectroscopy combined with deep learning to estimate LNC in cotton leaves was explored. The samples were collected from cotton's whole growth cycle, and the spectra were from different measurement environments. The random frog (RF), weighted partial least squares regression (WPLS), and saliency map were used for characteristic wavelength selection. Qualitative models (partial least squares discriminant analysis (PLS-DA), support vector machine for classification (SVC), convolutional neural network classification (CNNC) and quantitative models (partial least squares regression (PLSR), support vector machine for regression (SVR), convolutional neural network regression (CNNR)) were established based on the full spectra and characteristic wavelengths. Satisfactory results were obtained by models based on CNN. The classification accuracy of leaves in three different LNC ranges was up to 83.34%, and the root mean square error of prediction (RMSEP) of quantitative prediction models of cotton leaves was as low as 3.36. In addition, the identification of cotton leaves based on the predicted LNC also achieved good results. These results indicated that the nitrogen content of cotton leaves could be effectively detected by deep learning and visible and near-infrared spectroscopy, which has great potential for real-world application.
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Affiliation(s)
- Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Na Wu
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Huzhou, China
| | - Wentan Tang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Lei Zhou
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China
| | | | - Ze Zhang
- Key Laboratory of Oasis Eco-Agriculture, College of Agriculture, Shihezi University, Shihezi, China
| | - Pan Gao
- College of Information Science and Technology, Shihezi University, Shihezi, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
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Abstract
In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.
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