- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR)
摘要： Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.
关键词： precision agriculture,LIBS,PLS regression,gaussian processes,soil,lasso,nutrients
更新于2025-09-16 10:30:52
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Coupling Waveguide-Based Micro-Sensors and Spectral Multivariate Analysis to Improve Spray Deposit Characterization in Agriculture
摘要： The leaf coverage surface is a key measurement of the spraying process to maximize spray efficiency. To determine leaf coverage surface, the development of optical micro-sensors that, coupled with a multivariate spectral analysis, will be able to measure the volume of the droplets deposited on their surface is proposed. Rib optical waveguides based on Ge-Se-Te chalcogenide films were manufactured and their light transmission was studied as a response to the deposition of demineralized water droplets on their surface. The measurements were performed using a dedicated spectrophotometric bench to record the transmission spectra at the output of the waveguides, before (reference) and after drop deposition, in the wavelength range between 1200 and 2000 nm. The presence of a hollow at 1450 nm in the relative transmission spectra has been recorded. This corresponds to the first overtone of the O–H stretching vibration in water. This result tends to show that the optical intensity decrease observed after droplet deposition is partly due to absorption by water of the light energy carried by the guided mode evanescent field. The probe based on Ge-Se-Te rib optical waveguides is thus sensitive throughout the whole range of volumes studied, i.e., from 0.1 to 2.5 μL. Principal Component Analysis and Partial Least Square as multivariate techniques then allowed the analysis of the statistics of the measurements and the predictive character of the transmission spectra. It confirmed the sensitivity of the measurement system to the water absorption, and the predictive model allowed the prediction of droplet volumes on an independent set of measurements, with a correlation of 66.5% and a precision of 0.39 μL.
关键词： principal component analysis (PCA),partial least squares (PLS),precision agriculture,droplet characterization,infrared spectroscopy,optical micro-sensors,crop protection
更新于2025-09-12 10:27:22
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Applications of Laser-Induced Breakdown Spectroscopy for Soil Analysis, Part I: Review of Fundamentals and Chemical and Physical Properties
摘要： Laser-induced breakdown spectroscopy (LIBS) has become a prominent analytical technique in recent years for real-time characterization of soil properties. However, only a few studies of soil chemical and physical properties have been reported using LIBS until recently. The aims of this article are to: (1) provide the basic principles of LIBS for soil analysis and (2) present the use of LIBS for soil pH, soil texture, and humification degree of soil organic matter (SOM). The second article will cover soil classification and soil elemental analysis, including plant nutrients, carbon (C), and toxic elements. LIBS is a multi-element analytical technique based on atomic spectroscopy that employs a high-energy laser pulse focused onto a sample surface to create a transient plasma. It is a spectroscopic analytical technique that requires very little or no sample preparation, examines each sample in seconds, and offers a flexible platform for the examination of a broad array of elements in the sample. LIBS also can be used to infer soil chemical and physical properties if a relationship exists between the chemical composition and the soil properties. With proper calibration, LIBS has a great potential for real-time in-field soil analysis and precision farming that could lead to improved soil management and agricultural production, and reduced agricultural environmental impacts.
关键词： humification degree of soil organic matter,soil texture,precision agriculture,soil sensing,soil analysis,soil pH
更新于2025-09-11 14:15:04
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Applications of Laser-Induced Breakdown Spectroscopy for Soil Characterization, Part II: Review of Elemental Analysis and Soil Classification
摘要： In-field soil health assessments, including plant nutrients and toxic elements, are needed and could improve the sustainability of agriculture production. Among the available analytical techniques for these analyses, laser-induced breakdown spectroscopy (LIBS) has become one of the most promising techniques for real-time soil analysis at low cost and without the need of reagents. The first part of this two-part review (Part I, Villas-Boas et al., 2019) in this issue focused on the fundamentals of LIBS for soil analysis and its use for soil chemical and physical characterization. Our objectives in this review article (Part II) are to review (i) the main applications of LIBS in the determination of soil carbon (C), nutrients and toxic elements, spatial elemental mapping, and (ii) its use in soil classification. Traditional and more recent techniques will be compared to LIBS, considering their advantages and disadvantages. LIBS is a promising, versatile technique for detecting many elements in soil samples, requires little or no sample preparation, takes only a few seconds per sample, and has a low cost per sample compared to other techniques. However, overcoming matrix effects is a challenge for LIBS applications in soil analysis, since most studies are conducted with limited changes in the matrix. In spite of the limitation of matrix effects, a typical LIBS system has a limit of detection of 0.3, 0.6, 4, 7, 10, 18, 46, and 89 mg kg-1 for Mo, Cu, Mg, Mn, Fe, Zn, K, and Ca, respectively. LIBS holds potential for real-time in-field spatial elemental analysis of soils and practical applications in precision farming with proper calibration. This could lead to immediate diagnoses of contaminated soil and inefficient nutrient supplies and facilitate well-informed soil management, increasing agricultural production while minimizing environmental impacts.
关键词： soil contamination,soil fertility,rhizosphere,toxic elements,spatial elemental mapping,SOM,precision agriculture,Plant nutrients,soil carbon
更新于2025-09-11 14:15:04
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Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers
摘要： The presented approach demonstrates an automated way of crop disease identification on various leaf sample images corresponding to different crop species employing Local Binary Patterns (LBPs) for feature extraction and One Class Classification for classification. The proposed methodology uses a dedicated One Class Classifier for each plant health condition including, healthy, downy mildew, powdery mildew and black rot. The algorithms trained on vine leaves have been tested in a variety of crops achieving a very high generalization behavior when tested in other crops. An original algorithm proposing conflict resolution between One Class Classifiers provides the correct identification when ambivalent data examples possibly belong to one or more conditions. A total success rate of 95% is achieved for the total for the 46 plant-condition combinations tested.
关键词： Computer vision,Machine learning,Local descriptors,Crop health status,Precision agriculture
更新于2025-09-09 09:28:46
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Separating Crop Species in Northeastern Ontario Using Hyperspectral Data
摘要： The purpose of this study was to examine the capability of hyperspectral narrow wavebands within the 400–900 nm range for distinguishing five cash crops commonly grown in Northeastern Ontario, Canada. Data were collected from ten different fields in the West Nipissing agricultural zone (46°24'N lat., 80°07'W long.) and included two of each of the following crop types; soybean (Glycine max), canola (Brassica napus L.), wheat (Triticum spp.), oat (Avena sativa), and barley (Hordeum vulgare). Stepwise discriminant analysis was used to assess the spectral separability of the various crop types under two scenarios; Scenario 1 involved testing separability of crops based on number of days after planting and Scenario 2 involved testing crop separability at specific dates across the growing season. The results indicate that select hyperspectral bands in the visual and near infrared (NIR) regions (400–900 nm) can be used to effectively distinguish the five crop species under investigation. These bands, which were used in a variety of combinations include B465, B485, B495, B515, B525, B535, B545, B625, B645, B665, B675, B695, B705, B715, B725, B735, B745, B755, B765, B815, B825, B885, and B895. In addition, although species classification could be achieved at any point during the growing season, the optimal time for satellite image acquisition was determined to be in late July or approximately 75–79 days after planting with the optimal wavebands located in the red-edge, green, and NIR regions of the spectrum.
关键词： soybean,wheat,barley,canola,oat,crop separability,hyperspectral remote sensing,optimal timing,precision agriculture
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Crop Lodging Analysis from Uas Orthophoto Mosaic, Sentinel-2 Image and Crop Yield Monitor Data
摘要： Crop lodging is surveyed from different image sources and from the crop yield map. Crop lodging has effect on remotely sensed and field level measurements when indirectly measuring, for example, soil variation, nutrients, or crop condition. The interpretation of results may be incorrect especially in the case when it is not possible to detect lodging from the analyzed dataset. Such situation may arise, for example, when analyzing Sentinel-2 (S2) images or crop yield monitor data. In this study crop lodging is inspected from UAS-based orthophotomosaic taken 50 meters above the ground level, S2 image and crop yield monitor connected to a combine harvester.
关键词： orthophoto,Sentinel-2,UAS,crop yield monitor,precision agriculture,lodging
更新于2025-09-04 15:30:14
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[IEEE 2018 37th Chinese Control Conference (CCC) - Wuhan (2018.7.25-2018.7.27)] 2018 37th Chinese Control Conference (CCC) - A Lidar-Based Tree Canopy Detection System Development
摘要： In this paper, a LiDAR-based interactive target detection system was developed to characterized tree canopy structures under various laser sensor travel speed and detection distances. The system composed of a sliding motion control system and a Lidar-based target detection unit. The target detection unit used a 2700 range laser scanning sensor to measure target object surface distances based on the time-of-flight principle. The laser sensor travel speed and travel distance was controlled via the control system by specifying a position and speed as a set point. A real-time data acquisition and data post-processing programs were developed based on C++ and MATLAB programming languages respectively. The entire system was tested in the laboratory for a wide range of parameters and operating conditions. The test result showed that the system could detect and characterize tree canopy structure at very low travel speed (0.3 m/s) and high travel speed (5.0 m/s), respectively with acceptable accuracy.
关键词： Tree canopy,three-dimensional image reconstruction,Precision agriculture,LiDAR,Servo system
更新于2025-09-04 15:30:14