Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring
Agricultural production management is facing a new era of intelligence and automation. With developments in sensor technologies, the temporal, spectral, and spatial resolution from ground/air/space platforms have been notably improved. Optical sensors play an essential role in agriculture production...
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| Format: | Online |
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| Idioma: | anglès |
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MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Accés en línia: | ONIX_20240108_9783036597980_103 |
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| _version_ | 1869525506208038912 |
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| collection | Directory of Open Access Books |
| description | Agricultural production management is facing a new era of intelligence and automation. With developments in sensor technologies, the temporal, spectral, and spatial resolution from ground/air/space platforms have been notably improved. Optical sensors play an essential role in agriculture production management. Specifically, monitoring plant health, growth conditions, and insect infestation has traditionally involved extensive fieldwork. We believe that sensors, artificial intelligence, and machine learning are not simply scientific experiments but opportunities to make our agricultural production management more efficient and cost-effective, further contributing to the healthy development of natural–human systems. This reprint compiles the latest research on optical sensors and machine learning in agricultural monitoring, including related topics: Machine learning approaches for crop health, growth, and yield monitoring; Combined multisource/multi-sensor data to improve the crop parameters mapping; Crop-related growth models, artificial intelligence models, algorithms, and precision management; Farmland environmental monitoring and management; Ground, air, and space platforms application in precision agriculture; Development and application of field robotics; High-throughput field information survey; Phenological monitoring. |
| format | Online |
| id | doab-20.500.12854ir-132444 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1324442024-04-09T23:16:13Z Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring Yue, Jibo Zhou, Chengquan Feng, Haikuan Yang, Yanjun Zhang, Ning soil moisture content spectral processing technology hyperspectral principal component analysis feature parameters extraction yield estimation rice unmanned aerial vehicle (UAV) tasseled cap transformation precision agriculture weed identification YOLOv4-Tiny attention mechanism multiscale detection angle normalization vegetation canopy reflectance geostationary satellite path length correction Minnaert model GOCI winter wheat LSTM LAI deep learning land use land cover classification random forest Sentinel data SRTM feature selection accuracy validation unmanned aerial vehicle soybean convolutional neural network multispectral imagery fusarium head blight texture indices machine learning cropland multi-seasonal fractal feature feature extraction accuracy evaluation black soil UAV chlorophyll fractional vegetation cover maturity monitoring anomaly detection smart agriculture detection of apple leaf diseases YOLOv5 transformer CBAM crop type classification multi-temporal remote sensing dairy cows body condition score 3D TOF sensor non-contact evaluation recognize area of interest sugarcane clones canopy cover light interception biomass cane yield peanut southern blight reflection spectrum spectral index continuous wavelet transform VGNet corn diseases leaf detection lightweight transfer learning agriculture n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Agricultural production management is facing a new era of intelligence and automation. With developments in sensor technologies, the temporal, spectral, and spatial resolution from ground/air/space platforms have been notably improved. Optical sensors play an essential role in agriculture production management. Specifically, monitoring plant health, growth conditions, and insect infestation has traditionally involved extensive fieldwork. We believe that sensors, artificial intelligence, and machine learning are not simply scientific experiments but opportunities to make our agricultural production management more efficient and cost-effective, further contributing to the healthy development of natural–human systems. This reprint compiles the latest research on optical sensors and machine learning in agricultural monitoring, including related topics: Machine learning approaches for crop health, growth, and yield monitoring; Combined multisource/multi-sensor data to improve the crop parameters mapping; Crop-related growth models, artificial intelligence models, algorithms, and precision management; Farmland environmental monitoring and management; Ground, air, and space platforms application in precision agriculture; Development and application of field robotics; High-throughput field information survey; Phenological monitoring. 2024-01-08T14:52:56Z 2024-01-08T14:52:56Z 2023 book ONIX_20240108_9783036597980_103 9783036597980 9783036597997 https://directory.doabooks.org/handle/20.500.12854/132444 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/8482 https://mdpi.com/books/pdfview/book/8482 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-9799-7 10.3390/books978-3-0365-9799-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036597980 9783036597997 310 Basel open access |
| spellingShingle | soil moisture content spectral processing technology hyperspectral principal component analysis feature parameters extraction yield estimation rice unmanned aerial vehicle (UAV) tasseled cap transformation precision agriculture weed identification YOLOv4-Tiny attention mechanism multiscale detection angle normalization vegetation canopy reflectance geostationary satellite path length correction Minnaert model GOCI winter wheat LSTM LAI deep learning land use land cover classification random forest Sentinel data SRTM feature selection accuracy validation unmanned aerial vehicle soybean convolutional neural network multispectral imagery fusarium head blight texture indices machine learning cropland multi-seasonal fractal feature feature extraction accuracy evaluation black soil UAV chlorophyll fractional vegetation cover maturity monitoring anomaly detection smart agriculture detection of apple leaf diseases YOLOv5 transformer CBAM crop type classification multi-temporal remote sensing dairy cows body condition score 3D TOF sensor non-contact evaluation recognize area of interest sugarcane clones canopy cover light interception biomass cane yield peanut southern blight reflection spectrum spectral index continuous wavelet transform VGNet corn diseases leaf detection lightweight transfer learning agriculture n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring |
| title | Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring |
| title_full | Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring |
| title_fullStr | Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring |
| title_full_unstemmed | Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring |
| title_short | Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring |
| title_sort | novel applications of optical sensors and machine learning in agricultural monitoring |
| topic | soil moisture content spectral processing technology hyperspectral principal component analysis feature parameters extraction yield estimation rice unmanned aerial vehicle (UAV) tasseled cap transformation precision agriculture weed identification YOLOv4-Tiny attention mechanism multiscale detection angle normalization vegetation canopy reflectance geostationary satellite path length correction Minnaert model GOCI winter wheat LSTM LAI deep learning land use land cover classification random forest Sentinel data SRTM feature selection accuracy validation unmanned aerial vehicle soybean convolutional neural network multispectral imagery fusarium head blight texture indices machine learning cropland multi-seasonal fractal feature feature extraction accuracy evaluation black soil UAV chlorophyll fractional vegetation cover maturity monitoring anomaly detection smart agriculture detection of apple leaf diseases YOLOv5 transformer CBAM crop type classification multi-temporal remote sensing dairy cows body condition score 3D TOF sensor non-contact evaluation recognize area of interest sugarcane clones canopy cover light interception biomass cane yield peanut southern blight reflection spectrum spectral index continuous wavelet transform VGNet corn diseases leaf detection lightweight transfer learning agriculture n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | soil moisture content spectral processing technology hyperspectral principal component analysis feature parameters extraction yield estimation rice unmanned aerial vehicle (UAV) tasseled cap transformation precision agriculture weed identification YOLOv4-Tiny attention mechanism multiscale detection angle normalization vegetation canopy reflectance geostationary satellite path length correction Minnaert model GOCI winter wheat LSTM LAI deep learning land use land cover classification random forest Sentinel data SRTM feature selection accuracy validation unmanned aerial vehicle soybean convolutional neural network multispectral imagery fusarium head blight texture indices machine learning cropland multi-seasonal fractal feature feature extraction accuracy evaluation black soil UAV chlorophyll fractional vegetation cover maturity monitoring anomaly detection smart agriculture detection of apple leaf diseases YOLOv5 transformer CBAM crop type classification multi-temporal remote sensing dairy cows body condition score 3D TOF sensor non-contact evaluation recognize area of interest sugarcane clones canopy cover light interception biomass cane yield peanut southern blight reflection spectrum spectral index continuous wavelet transform VGNet corn diseases leaf detection lightweight transfer learning agriculture n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20240108_9783036597980_103 |