Multispectral Research Articles

3D computational modeling of multispectral photoacoustic microscopy: Simulations aiding systematic optimization and data acquisition

Abstract Photoacoustic microscopy (PAM), a fruitful application modality for photoacoustic imaging, is capable of directly measuring optical absorption properties in skin tissue, providing high-contrast, high-resolution medical images as well as functional and pathologic information. However, optical attenuation and unknown optical and acoustic nonuniformities limit its imaging performance in deep tissue regions. New instrumentation, image reconstruction, and artificial intelligence methods are being investigated to overcome these limitations, and a reliable PAM simulation tool is required for the effective implementation of these methods. In this paper, we propose and validate a 3D quantitative computational multispectral PAM imaging model. It provides a theoretical basis for the application of quantitative PAM in skin diagnosis and aids in the development and optimization of PAM devices, as well as the acquisition of deep learning datasets.

Leaf Discoloration in Rhododendron Species Exposed to Phytophthora Cinnamomi Corresponds with Future Mortality

Abstract Phytophthora cinnamomi, which causes the disease root rot, is an oomycete pathogen that is damaging to woody plants, including many horticulturally important groups, such as Rhododendron. Infecting the root of plants, Phytophthora cinnamomi inhibits water uptake, leading to root damage, wilting, and increased rates of plant mortality. Some observations suggest that P. cinnamomi infection corresponds to changes in leaf coloration, though whether this indicates a plant stress response or plant damage is generally unknown. We used leaf color analysis to test for differences in leaf discoloration between plants inoculated with the pathogen and control plants. We demonstrate a significant link between leaf discoloration in Rhododendron species and Phytophthora cinnamomi inoculation. This method was most useful when mortality was not exceptionally high, and analyzers must consider mortality as well as leaf damage in quantifying effects of the pathogen. Plants with leaf discoloration were 3.3 times more likely to die 2 weeks from our leaf census than plants with no leaf discoloration (P =0.005). This method is particularly inexpensive to implement, making it a valuable alternative to multi-spectral or hyperspectral imaging, especially in contexts such as horticulture and citizen science, where the high speed and low-cost nature of this technique might prove valuable. Species used in this study: root rot disease pathogen (Phytophthora cinnamomi Rands); Rhododendron atlanticum (Ashe) Rehder; Rhododendron brachycarpum D.Don ex G.Don; Rhododendron kiusianum Makino; Rhododendron maximum L.; Rhododendron minus Michx.; Rhododendron calendulaceum (Michx.) Torr.; Rhododendron kaempferi Planch.; Rhododendron keiskei Miq. Chemicals used in this study: Fosal Select Aliette/aluminum phosphite.

Pickles, Hiccups, and an Upside-Down Gloss: Two Fragments of Judeo-Arabic Medical Treatises in the Stanford University Collections

Abstract: This article reconstructs the history of two fragments in the Stanford University Collections. Although they once belonged to different manuscripts, they share much in common: both were copied in the western Mediterranean sometime around the fourteenth century, both contain excerpts of Arabic medical treatises transliterated into Hebrew characters, both contain marginal glosses in other languages (Latin in the one case; Hebrew and Romance vernaculars in the other), and both were subsequently reused as book bindings. Thanks to the insights gleaned from tools both old and new (the latter ranging from multispectral imaging to Twitter), the fragments together attest to the circulation of knowledge from Iran to Iberia, the material frameworks that enabled exchanges across religious and linguistic divides, and the violent dismemberment of manuscripts and communities alike in the closing decades of the Middle Ages.

Remote sensing of cover crop legacies on main crop N‐uptake dynamics

AbstractGrowing cover crops promotes soil health as they retain nutrients during autumn/winter and provide organic matter to the soil biota, which in turn supplies nutrients to the main crop upon mineralisation in spring. Different cover crops have varying impacts on soil biology and nutrient dynamics due to the quantity and quality of plant material returned to the soil. To understand these effects, high‐resolution data on crop responses is required. In this study, remote sensing was used to provide such data. The temporal dynamics of soil nitrogen (N) availability and N uptake in barley were studied in response to different cover crop monocultures and mixtures. This was achieved using high‐resolution multispectral images of the main crop acquired from an unmanned aerial vehicle. Alongside this, in‐situ collected plant and soil parameters were used in this 5‐year cover crop field experiment. The results showed that cover crop legacies significantly affected barley N uptake, biomass, and canopy N content. In early June, at peak canopy N, the highest values were observed in barley grown after vetch‐radish or oat‐radish mixtures (84 kg N/ha) and the lowest in barley grown after fallow (63 kg N) or oat (53 kg N/ha on 23rd of June). At the start of the barley growing season, soil microbial biomass was not significantly affected by the cover crop legacies. However, differential N mineralisation between cover crop legacies can be attributed to differences in microbial activity associated with cover crop quantity and quality. This research demonstrates the potential of remote sensing to monitor and understand temporal and spatial variation of crop canopy N in response to cover crop N mineralisation by the soil biota which is an important component of soil health. This approach can contribute to more efficient N use by enabling fine‐tuning of the type, quantity, timing, and location of fertilisation.

Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests

Efficient diagnosis of apple diseases and pests is crucial to the healthy development of the apple industry. However, the existing single-source image-based classification methods have limitations due to the constraints of single-source input image information, resulting in low classification accuracy and poor stability. Therefore, a classification method for apple disease and pest areas based on multi-source image fusion is proposed in this paper. Firstly, RGB images and multispectral images are obtained using drones to construct an apple diseases and pests canopy multi-source image dataset. Secondly, a vegetation index selection method based on saliency attention is proposed, which uses a multi-label ReliefF feature selection algorithm to obtain the importance scores of vegetation indices, enabling the automatic selection of vegetation indices. Finally, an apple disease and pest area multi-label classification model named AMMFNet is constructed, which effectively combines the advantages of RGB and multispectral multi-source images, performs data-level fusion of multi-source image data, and combines channel attention mechanisms to exploit the complementary aspects between multi-source data. The experimental results demonstrated that the proposed AMMFNet achieves a significant subset accuracy of 92.92%, a sample accuracy of 85.43%, and an F1 value of 86.21% on the apple disease and pest multi-source image dataset, representing improvements of 8.93% and 10.9% compared to prediction methods using only RGB or multispectral images. The experimental results also proved that the proposed method can provide technical support for the coarse-grained positioning of diseases and pests in apple orchards and has good application potential in the apple planting industry.

Assessing soil CO2 emission on eucalyptus species using UAV-based reflectance and vegetation indices

Eucalyptus species play an important role in the global carbon cycle, especially in reducing the greenhouse effect as well as storing atmospheric CO₂. Thus, assessing the amount of CO₂ released by the soil in forest areas can generate important information for environmental monitoring. This study aims to verify the relation between soil carbon dioxide (CO₂) flux (FCO₂), spectral bands, and vegetation indices (VIs) derived from a UAV-based multispectral camera over an area of eucalyptus species. Multispectral imageries (green, red-edge, and near-infrared) from the Parrot Sequoia sensor, derived vegetation indices, and the FCO₂ data from a LI-COR 8100 analyzer, combined with soil moisture and temperature data, were collected and related. The vegetation indices ATSAVI (Adjusted Transformed Soil-Adjusted VI), GSAVI (Green Soil Adjusted Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index), which use soil correction factors, exhibited a strong negative correlation with FCO₂ for the species E. camaldulensis, E. saligna, and E. urophylla species. A Multivariate Analysis of Variance showed significance (p < 0.01) for the species factor, which indicates that there are differences when considering all variables simultaneously. The results achieved in this study show a specific correlation between the data of soil CO₂ emission and the eucalypt species, providing a distinction of values between the species in the statistical data.

Improving rapid flood impact assessment: An enhanced multi-sensor approach including a new flood mapping method based on Sentinel-2 data

Rapid flood impact assessment methods need complete and accurate flood maps to provide reliable information for disaster risk management, in particular for emergency response and recovery and reconstruction plans. With the aim of improving the rapid assessment of flood impacts, this work presents a new impact assessment method characterized by an enhanced satellite multi-sensor approach for flood mapping, which improves the characterization of the hazard. This includes a novel flood mapping method based on the new multi-temporal Modified Normalized Difference Water Index (MNDWI) that uses multi-temporal statistics computed on time-series of Sentinel-2 multi-spectral satellite images. The multi-temporal aspect of the MNDWI improves characterization of land cover over time and enhances the temporary flooded areas, which can be extracted through a thresholding technique, allowing the delineation of more precise and complete flood maps. The methodology, if implemented in cloud-based environments such as Google Earth Engine (GEE), is computationally light and robust, allowing the derivation of flood maps in matters of minutes, also for large areas.The flood mapping and impact assessment method has been applied to the seasonal flood occurred in South Sudan in 2020, using Sentinel-1, Sentinel-2 and PlanetScope satellite imagery. Flood impacts were assessed considering damages to buildings, roads, and cropland. The multi-sensor approach estimated an impact of 57.4 million USD (considering a middle-bound scenario), higher than what estimated by using Sentinel-1 data only, and Sentinel-2 data only (respectively 24% and 78% of the estimation resulting from the multi-sensor approach). This work highlights the effectiveness and importance of considering multi-source satellite data for flood mapping in a context of disaster risk management, to better inform disaster response, recovery and reconstruction plans.

Estimating Leaf Area Index in Apple Orchard by UAV Multispectral Images with Spectral and Texture Information

The Leaf Area Index (LAI) strongly influences vegetation evapotranspiration and photosynthesis rates. Timely and accurately estimating the LAI is crucial for monitoring vegetation growth. The unmanned aerial vehicle (UAV) multispectral digital camera platform has been proven to be an effective tool for this purpose. Currently, most remote sensing estimations of LAIs focus on cereal crops, with limited research on economic crops such as apples. In this study, a method for estimating the LAI of an apple orchard by extracting spectral and texture information from UAV multispectral images was proposed. Specifically, field measurements were conducted to collect LAI data for 108 sample points during the final flowering (FF), fruit setting (FS), and fruit expansion (FE) stages of apple growth in 2023. Concurrently, UAV multispectral images were obtained to extract spectral and texture information (Gabor transform). The Support Vector Regression Recursive Feature Elimination (SVR-REF) was employed to select optimal features as inputs for constructing models to estimate the LAI. Finally, the optimal model was used for LAI mapping. The results indicate that integrating spectral and texture information effectively enhances the accuracy of LAI estimation, with the relative prediction deviation (RPD) for all models being greater than 2. The Categorical Boosting (CatBoost) model established for FF exhibits the highest accuracy, with a validation set R2, root mean square error (RMSE), and RPD of 0.867, 0.203, and 2.482, respectively. UAV multispectral imagery proves to be valuable in estimating apple orchard LAIs, offering real-time monitoring of apple growth and providing a scientific basis for orchard management.

Detecting starch-adulterated turmeric using Vis-NIR spectroscopy and multispectral imaging with machine learning

Chemicals are often added to turmeric to increase profits, posing significant health risks to consumers. At the same time, traditional methods for detecting contaminants in turmeric are complicated and time-consuming. This study aimed to develop a more practical approach using visible-near infrared (Vis-NIR) and multispectral imaging (MSI) techniques to detect starch adulteration in turmeric. The turmeric powder was mixed with starch (0.1, 0.5, 1, 2.5, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, and 100 % (w/w)) to create spectral and MSI datasets within the 400–1050 nm wavelength range. Spectra were corrected using spectral preprocessing techniques such as Savitzky-Golay (SG), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV). Principal Component Analysis (PCA) applied to reduce the dimensions and various machine learning (ML) models for prediction. The Random Forest Regressor (RFR) achieved a coefficient of determination (R²) of 0.999, a root mean squared error (RMSE) of 0.391 mg (w/w), and a residual predictive deviation (RPD) of 92.3 % in regression analysis. For classification, the Random Forest Classifiers (RFC) achieved an F1 score of 96.0 % and a Matthews Correlation Coefficient (MCC) of 94.6 %. In MSI analysis, the DenseNet201 model obtained an F1 score of 92.9 % and an MCC of 91.9 %. Moreover, the robustness of these models was cross-validated using leave-one-out cross-validation (LOOCV) and K-fold methods. The significance of the study lies in several critical areas, such as public health, advancement in technology, etc. The study's findings reveal that Vis-NIR and MSI approaches are excellent in detecting starch adulteration in turmeric with reliability. It has important implications for public health and food safety by offering a reliable tool for verifying the purity of turmeric and other food items.

Quantification of Green Carbon Changes in Relation with LULC Changes using Remote Sensing - GIS Technology in Kuantan River Basin

Land use land cover changes (LULCC) is a process that is influenced by anthropogenic effect. The growth of population is increasing, which leads to a demand on metropolitan regions. However, these urbanization and development are significant contributors to the expansion of LULCC conflict at an alarming rate to fulfil the human needs. LULCC has derived huge effects towards the environment which is biomass and green carbon changes. LULCC has caused vegetation covers of the land being degraded which reducing the total biomass of the area and implemented to that, the carbon stock for the area is increasing causing climate change and global warming. Therefore, the estimation of total carbon is important to be conducted. The existing map of LULCC is not update, the inventories information about the total carbon in Kuantan is not well covered, people in this region are lack of awareness about the harmful of carbon in the air, and conventional method is hard to be applied for carbon estimation are the problems that have derived for this study to be conducted. This study is focused on quantification of total carbon in Kuantan district to raise the awareness towards the local communities about the danger of high amount of carbon. The objective of this study is to classify LULC types in Kuantan district using geospatial artificial intelligent (GeoAI) approach on satellite images, to relate soil types, vegetation index and satellite – based total biomass in Kuantan River basin and to map green carbon changes in Kuantan based on quantified biomass using remote sensing – GIS technology. Therefore, this study is focused on Kuantan district to quantify the total biomass and carbon in this area by using selected multispectral satellites images with high spatial resolution Landsat 8 OLI, and Landsat 5 TM.

Thinning relationships of woody encroachers in a US southwestern shrubland

Woody plant encroachment degrades the economic and environmental potential of drylands by altering processes such as nutrient fluxes, ecohydrology, and ecosystem services. Though past research has investigated the encroachment process, relatively little is known about post-encroachment shrub community ecology. To better quantify dynamics within post-woody encroachment shrub communities, we combined USGS lidar height data and multispectral imagery to estimate shrub density, shrub height, shrub cover, and shrub volume across the Jornada Basin Long Term Ecological Research (JRN-LTER) site in southern New Mexico, USA. Structural estimates were analyzed in search of telltale signs of community competition, specifically, the presence of thinning relationships. Results demonstrated density-dependent thinning relationships in shrub communities of creosote and mesquite, indicating a large role for competition in arid shrub communities even at relatively low shrub densities and cover (∼35% cover). In addition, shrub volume estimates better modeled the expected thinning dynamics of shrub communities than shrub canopy cover measurements. Overall, our results indicate the utility of lidar data in extending two-dimensional descriptions of woody vegetation structure (i.e., woody canopy cover) into the critical third dimension (i.e., woody plant volume), as well as the relative importance of competition and demographic bottlenecks to vegetation structure in drylands.

Enhancing agricultural health with AI: Drone-based machine learning for mango tree disease detection

In the agriculture sector, timely detection of diseases in fruit trees is a significant challenge, leading to substantial economic losses. Automated detection of diseases in fruit trees, particularly mango trees, is crucial to minimize these losses by enabling early intervention. This research explores the use of drone-captured multispectral images combined with deep learning and computer vision techniques to detect diseases in mango trees. The proposed system leverages various pre-trained Convolutional Neural Network (CNN) models, such as YOLOv5, Detectron2, and Faster R-CNN, to achieve optimal accuracy. Data augmentation techniques are employed to address data skewness and overfitting, while Generative Adversarial Networks (GANs) enhance image quality. The system aims to provide a scalable solution for early disease detection, thereby reducing economic losses and supporting the agricultural sector's growth.

Estimation of soil organic matter content by combining Zhuhai-1 hyperspectral and Sentinel-2A multispectral images

Hyperspectral satellite imagery has significant advantages in rapidly monitoring soil organic matter (SOM) content over a large area. However, limitations in the timely acquisition of site-specific data may affect its effectiveness due to weather influences and revisit cycles. This paper proposes a novel method for estimating SOM content that combines the high temporal resolution of the Zhuhai-1 hyperspectral satellite image and Sentinel-2A multispectral satellite image to broaden the spectral range of Zhuhai-1 images. Multisource features, including spectral bands, topographic features, textural features, and spectral indexes, are extracted from the combined image and digital elevation model. An improved genetic algorithm (IGA) is proposed to optimize feature selection and extreme gradient boosting (XGBoost) is then applied to estimate SOM content. The proposed method was validated using 197 topsoil samples and satellite images collected from a demonstration area in Lishu County, Jilin Province, China. The results indicate that the estimation accuracy using the combined image was greater than using one single image. Compared with only using the Sentinel-2A and Zhuhai-1 images, the coefficient of determination (R2) values improved from 0.61 and 0.73 to 0.82, the ratio of the prediction to the deviation (RPD) values improved from 1.62 and 1.93 to 2.35, and the ratio of performance to the interquartile distance (RPIQ) values improved from 2.16 and 2.58 to 3.15, respectively. Furthermore, the XGBoost algorithm outperformed the random forest algorithm in terms of model accuracy and mapping reliability. The use of multisource features improved the R2 value from 0.45 to 0.82 compared to only using 31 spectral bands of Zhuhai-1 and Sentinel-2A image, with contribution rates in descending order of spectral indexes (48.2%), topographic features (24.7%), spectral bands (19.9%), and textural features (7.2%). This paper thus presents a promising method for efficient periodic mapping of the SOM content by combining data from a hyperspectral satellite constellation and a multispectral satellite image.

Using multispectral images and field inclinometer data to analyze topographic changes related to and the reactivation mechanism of a large-scale landslide at Caoling in Taiwan

Using multispectral images and field inclinometer data to analyze topographic changes related to and the reactivation mechanism of a large-scale landslide at Caoling in Taiwan

Oued Lakhdar watershed (Morocco), monitoring land use/cover changes: remote sensing and GIS approach

ABSTRACT Landsat is a series of Earth-observation satellites jointly managed by NASA and the United States Geological Survey (USGS) that capture multispectral images of the Earth’s surface dating back to the 1970s, providing a valuable tool for tracking changes in land cover over time. In this study, Landsat satellite data were combined with remote sensing and Geographic Information System (GIS) technologies to monitor land cover changes in the Oued Lakhdar watershed, which covers an area of 1,638 km2. Landsat satellite data for the years 1987, 1994, 2004, 2014, and 2021 were used, and the maximum likelihood method was applied in ArcGIS software version 10.4.1 to classify land components. The classification was validated using total accuracy, the kappa index, Google Earth, and terrain knowledge. The results show remarkable changes in land cover, with an increase in bare soil and irrigated crops between 1987 and 2021, especially in the last six years, totaling 0.672 km2/yr and 15.028 km2/yr of the study area, respectively, and a decrease in water bodies and dense forests, totaling −0.941 km2/yr and −30.046 km2/yr, respectively. Water retention coefficient analysis indicates that the watershed is anthropized and has a low retention capacity. This study provides valuable information for land use planning and management in the Oued Lakhdar watershed (Morocco).

Detection of Rice Leaf SPAD and Blast Disease Using Integrated Aerial and Ground Multiscale Canopy Reflectance Spectroscopy

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.

Large-Scale Mapping of Complex Forest Typologies Using Multispectral Imagery and Low-Density Airborne LiDAR: A Case Study in Pinsapo Fir Forests

Climate change increases the vulnerability of relict forests. To address this problem, regional Forest Services require silvicultural and conservation actions to designate specific forest management alternatives. In this context, the main objective of this study was to develop a methodology to map complex Abies pinsapo forest typologies using multispectral and low-density airborne LiDAR data and machine learning. Stand density, species composition and cover were used to identify seven forest typologies. Random forest resulted as the more accurate model (OA = 0.62; Kappa = 0.43) to classify those types based on multispectral and LiDAR data, although showing a moderate model performance. Classification performance showed great differences between forest types with better results for the uneven-aged stands compared to the even-aged and two-aged stands. The developed typology was applied to supply local forest managers with more accurate forest maps that can be used to improve forest management plans. The typology proposed is easy to apply in forest management practices since it only uses as input the diameter at breast height, tree density and specific composition. The study demonstrated the potential of low-density LiDAR data combined with spectral information from high-resolution orthophotos to predict the structural characteristics of complex forest typologies.

Phenotyping for Effects of Drought Levels in Quinoa Using Remote Sensing Tools

Drought is a principal limiting factor in the production of agricultural crops; however, quinoa possesses certain adaptive and tolerance factors that make it a potentially valuable crop under drought-stress conditions. Within this context, the objective of the present study was to evaluate morphological and physiological changes in ten quinoa genotypes under three irrigation treatments: normal irrigation, drought-stress followed by recovery irrigation, and terminal drought stress. The experiments were conducted at the UNSA Experimental Farm in Majes, Arequipa, Peru. A series of morphological, physiological, and remote measurements were taken, including plant height, dry biomass, leaf area, stomatal density, relative water content, selection indices, chlorophyll content via SPAD, multispectral imaging, and reflectance measurements via spectroradiometry. The results indicated that there were numerous changes under the conditions of terminal drought stress; the yield variables of total dry biomass, leaf area, and plant height were reduced by 69.86%, 62.69%, and 27.16%, respectively; however, under drought stress with recovery irrigation, these changes were less pronounced with a reduction of 21.10%, 27.43%, and 17.87%, respectively, indicating that some genotypes are adapted or tolerant of both water-limiting conditions (Accession 50, Salcedo INIA and Accession 49). Remote sensing tools such as drones and spectroradiometry generated reliable, rapid, and precise data for monitoring stress and phenotyping quinoa and the optimum timing for collecting these data and predicting yield impacts was from 79–89 days after sowing (NDRE and CREDG r Pearson 0.85).

Smart Ship Draft Reading by Dual-Flow Deep Learning Architecture and Multispectral Information.

In maritime transportation, a ship's draft survey serves as a primary method for weighing bulk cargo. The accuracy of the ship's draft reading determines the fairness of bulk cargo transactions. Human visual-based draft reading methods face issues such as safety concerns, high labor costs, and subjective interpretation. Therefore, some image processing methods are utilized to achieve automatic draft reading. However, due to the limitations in the spectral characteristics of RGB images, existing image processing methods are susceptible to water surface environmental interference, such as reflections. To solve this issue, we obtained and annotated 524 multispectral images of a ship's draft as the research dataset, marking the first application of integrating NIR information and RGB images for automatic draft reading tasks. Additionally, a dual-branch backbone named BIF is proposed to extract and combine spectral information from RGB and NIR images. The backbone network can be combined with the existing segmentation head and detection head to perform waterline segmentation and draft detection. By replacing the original ResNet-50 backbone of YOLOv8, we reached a mAP of 99.2% in the draft detection task. Similarly, combining UPerNet with our dual-branch backbone, the mIoU of the waterline segmentation task was improved from 98.9% to 99.3%. The inaccuracy of the draft reading is less than ±0.01 m, confirming the efficacy of our method for automatic draft reading tasks.

Rapid prediction and visualization of safe moisture content in alfalfa seeds based on multispectral imaging technology

Moisture significantly impacts seed sales, storage, and processing. Traditional moisture testing methods are often slow, labor-intensive, and inadequate for the rapid detection demands of modern agriculture, particularly for non-destructive testing of individual seeds. This study applied multispectral imaging to obtain morphological and spectral data from alfalfa seeds at six moisture levels (4 %, 8 %, 12 %, 16 %, 25 %, and 41 %). By integrating algorithms such as Support Vector Machines (SVM), Random Forests (RF), Linear Discriminant Analysis (LDA), Back Propagation Neural Network (BPNN), and normalized typical discriminant analysis (nCDA) algorithms, classification models were developed to distinguish between safe and unsafe moisture levels. The Results indicated that spectral data alone significantly improved model accuracy and prediction. nCDA visualizations effectively illustrated spatial moisture distribution, highlighting stark color differences between seeds in the safe moisture range (4 %, 8 %, 12 %) and those in the unsafe range (16 %, 25 %, 41 %). BPNN exhibited high model precision, achieving a recognition accuracy rate of 90.1 % for safe and unsafe moisture content. Key wavelengths identified by the Permutation method included 970, 880, 570, and 490 nm. Pearson correlation analysis showed a significant positive correlation between germination indicators and spectral data, which strengthened with longer seed storage. These findings confirm the potential of multispectral imaging for assessing the safe moisture content of alfalfa seeds, supporting the development of detection systems for evaluating moisture content in individual seeds. This advancement enables the rapid removal of high-moisture seeds, preventing deterioration during storage.