Lodging Assessment Research Articles

Enhancing model performance in detecting lodging areas in wheat fields using UAV RGB Imagery: Considering spatial and temporal variations

Fast and accurate assessment of wheat lodging holds significant importance for disaster prevention and agricultural insurance claim settlement. However, lodging detection is often influenced by the spatial and temporal heterogeneity of fields, leading to inconsistent lodging features in images captured at different times and locations. This inconsistency poses challenges for evaluating model performance and obtaining consistent results. In this study, we proposed a comprehensive lodging area detection model performance evaluation method for multi-year and multi-phenological periods. Three evaluation indicators—accuracy, potential, and stability—were introduced to assess model performance from different perspectives. Additionally, a weighted method is employed to combine the results of multiple experiments. To evaluate the performance of lodging area segmentation in wheat fields, we selected three feature adaptive models (CBAM-unet, SE-unet, and Swin-transformer) along with the Unet model.The experimental results are as follows: (1) Swin-transformer achieved the highest weighted average accuracy (Acccp) of 83.53% among the four models evaluated. (2) Swin-transformer exhibited the highest upper limit of segmentation accuracy, reaching 97.98%. However, its average accuracy in the prediction experiment was 85% of the upper limit, suggesting potential for optimization. (3) CBAM-unet demonstrated the lowest overall weighted segmentation accuracy variance (35.92) compared to the other three models, indicating higher stability. Based on the experimental results it can be seen that our proposed method and model can address the challenge of evaluating lodging area detection models under spatial and temporal heterogeneity.

Comprehensive wheat lodging detection after initial lodging using UAV RGB images

Crop lodging in agricultural fields is one of the major factors that limit cereal crop yields. Wheat, the most popular cereal crop in most countries, is also affected by this phenomenon, which may result in a significant decrease in both yield and quality. Therefore, addressing wheat lodging is crucial for producers. This study aims to detect and identify wheat lodging through aerial images and classify its severity based on ratio, angle and location of the lodging. To achieve this goal, a multi-task approach was proposed involving three phases. First, automatic dataset generation methodology was conducted on orthomosaic imagery of three dates. Next, a comprehensive assessment of wheat lodging (ratio, angle and location) was performed, which has received little research attention. Third, applying and improving selected classification models for classifying image datasets was conducted. Combining convolutional neural networks and temporal sequences in a single model provided an opportunity to use spatiotemporal information extracted from the wheat image datasets. Time dependency and individual dates were both considered in the classification task. The limited number of data and imbalanced classes challenges, resulting from real field conditions data collection, were overcome by applying a new loss function to the classifier models. The overall accuracy of wheat lodging classification reached over 91% in these two states using the proposed approach. Based on this research, wheat lodging was detected more accurately by the proposed models despite the small and imbalanced dataset. The developed methodology paves the way for comprehensive and automatic wheat lodging detection, and the methodology can be adapted for similar crops that suffer lodging issues with suitable modifications.

Spatial-temporal distribution and hazard assessment of maize lodging in a synergistic disaster environment

Maize, a major food crop, holds significant edible and industrial value. Lodging poses a threat to maize yields, often resulting in reduced or even zero grain production due to the prevailing growth conditions. The precise evaluation of contributing factors on maize lodging, such as adverse environmental conditions, remains limited.This study employs the M-K test, Geodetector analysis, and Receiver Operator Characteristic (ROC) curve to examine the spatial-temporal distribution of maize lodging incidents and establishes a comprehensive assessment model for spring maize lodging hazard evaluation, considering environmental variables. The findings reveal the following: (1) High wind-type lodging events (≥ 10.8 m/s by universal definition, ≥ 10 m/s in this study) occurred frequently from the jointing-tasseling stage. Wind and rain-type lodging events occurred more frequently from the milk to maturity stages compared to other phases. (2) Maize lodging hazard during the jointing stage is prominent in western Jilin province and tends to expand towards central Jilin province as the growth stages progress. (3) Topographical, soil, and climatic factors all exhibited explanatory power regarding lodging incidents, with climatic factors demonstrating a substantial contribution when combined with the other two variables. (4) The accuracy test of the ROC curve for the comprehensive model surpasses the 0.05 significant confidence threshold, indicating a strong fit. The model developed in this study enables the evaluation of maize lodging hazards during the late growth stage, enhancing the reliability of lodging disaster risk assessments.

Understanding the trade–off between lodging resistance and seed yield, and developing some non–destructive methods for predicting crop lodging risk in canola production

Efficient nitrogen (N) nutrient management is important for developing sustainable strategies to increase seed yield while reducing negative environmental impacts. Motivation to increase seed yield by appropriately increasing N application rates would make crop lodging a potential problem. Fewer studies have been conducted to elucidate the trade–off between yield gain and lodging susceptibility in canola ( Brassica napus L. ), and a non–destructive and high–throughput assessment of lodging is seriously lacking. In this regard, a field study consisting of two varieties and five combinations of rates and timing of N application was conducted to explore the strength of their trade–offs for better N fertilization recommendation and to determine the feasibility of non–destructive technique for diagnosing canola lodging susceptibility. Two non–destructive techniques including root electrical capacitance and normalized difference vegetative index (NDVI) were verified in this study. The susceptibility of stem and root lodging were quantified by “safety factor” method. The results showed that there was a trade–off between seed yield and lodging resistance under conditions of seed yield greater than 1.3 t ha –1 . Under high–yielding condition of 2021 cropping season, split–N treatment, 50 kg N ha –1 at preplant plus 50 kg N ha –1 topdressed at the 6–leaf stage, increased seed yield by 20% and lodging resistance by 38% for hybrid ‘Invigor L233P’, compared with the equivalent preplant–only N application. However, under low–yielding condition, split–N treatments did not always show advantages over the equivalent preplant–only N treatments in terms of lodging resistance and seed yield, whereas the highest seed yield was generally attained under the split–N application in all experimental years. Root capacitance and impedance were significantly correlated with root morphological traits, which in turn affected lodging resistance and seed yield, while NDVI was closely related to lodging resistance (P < 0.01). A split–N application strategy with moderate N rates (100–150 kg N ha –1 ) can be highly recommended for canola production in eastern Canada. Indirect prediction of lodging susceptibility through root electrical measurements and NDVI mapping have high applicability due to their low cost and non–destructive properties, and are expected to serve as high–throughput techniques for guiding N fertilizer management to improve seed yield, while reducing lodging risk. • The trade–off between canola yield and lodging susceptibility was elucidated. • Split–N application with moderate rates can be recommended for canola production. • NDVI and root capacitance were feasible for diagnosing lodging susceptibility risk.

Wheat lodging extraction using Improved_Unet network.

The accurate extraction of wheat lodging areas can provide important technical support for post-disaster yield loss assessment and lodging-resistant wheat breeding. At present, wheat lodging assessment is facing the contradiction between timeliness and accuracy, and there is also a lack of effective lodging extraction methods. This study aims to propose a wheat lodging assessment method applicable to multiple Unmanned Aerial Vehicle (UAV) flight heights. The quadrotor UAV was used to collect high-definition images of wheat canopy at the grain filling and maturity stages, and the Unet network was evaluated and improved by introducing the Involution operator and Dense block module. The performance of the Improved_Unet was determined using the data collected from different flight heights, and the robustness of the improved network was verified with data from different years in two different geographical locations. The results of analyses show that (1) the Improved_Unet network was better than other networks (Segnet, Unet and DeeplabV3+ networks) evaluated in terms of segmentation accuracy, with the average improvement of each indicator being 3% and the maximum average improvement being 6%. The Improved_Unet network was more effective in extracting wheat lodging areas at the maturity stage. The four evaluation indicators, Precision, Dice, Recall, and Accuracy, were all the highest, which were 0.907, 0.929, 0.884, and 0.933, respectively; (2) the Improved_Unet network had the strongest robustness, and its Precision, Dice, Recall, and Accuracy reached 0.851, 0.892, 0.844, and 0.885, respectively, at the verification stage of using lodging data from other wheat production areas; and (3) the flight height had an influence on the lodging segmentation accuracy. The results of verification show that the 20-m flight height performed the best among the flight heights of 20, 40, 80 and 120 m evaluated, and the segmentation accuracy decreased with the increase of the flight height. The Precision, Dice, Recall, and Accuracy of the Improved_Unet changed from 0.907 to 0.845, from 0.929 to 0.864, from 0.884 to 0.841, and from 0.933 to 0.881, respectively. The results demonstrate the improved ability of the Improved-Unet to extract wheat lodging features. The proposed deep learning network can effectively extract the areas of wheat lodging, and the different height fusion models developed from this study can provide a more comprehensive reference for the automatic extraction of wheat lodging.

Risk Assessment of Different Maize (Zea mays L.) Lodging Types in the Northeast and the North China Plain Based on a Joint Probability Distribution Model

Mastering the lodging risk of planting environment is of great significance to the optimal layout of maize varieties and the breeding of lodging resistant varieties. However, the existing lodging risk models are still at the stage of single or multi-factors independent analysis, and lack of assessment for different lodging types. To address this issue, based on the mechanism of different lodging types, the Archimedean copula function was used to describe the joint probability distribution of wind speed and precipitation, and the lodging risk assessment model of maize was established. By comparing the goodness of fit, when the rank correlation coefficient of these two is positive and negative, the corresponding optimal joint probability distribution functions are the Gumbel copula and Frank copula. According to the spatial distribution of lodging risk, the area from Liaodong Bay northward to Tongyu, Jilin province in the Northeast and the North China Plain has a high frequency of lodging, in which the probability of stalk lodging is two to four times that of root lodging. Finally, we discussed how to apply the lodging risk distribution results to optimize the maize variety test sites to improve the efficiency and reliability of the existing test system. The method proposed in this paper comprehensively considers the synergistic effect of multiple factors and can provide technical support for other risk assessment.

Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping

Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high-performance end-to-end machine learning pipelines with minimal effort from the user. However, despite AutoML showing great promise for computer vision tasks, to the best of our knowledge, no study has used AutoML for image-based plant phenotyping. To address this gap in knowledge, we examined the application of AutoML for image-based plant phenotyping using wheat lodging assessment with unmanned aerial vehicle (UAV) imagery as an example. The performance of an open-source AutoML framework, AutoKeras, in image classification and regression tasks was compared to transfer learning using modern convolutional neural network (CNN) architectures. For image classification, which classified plot images as lodged or non-lodged, transfer learning with Xception and DenseNet-201 achieved the best classification accuracy of 93.2%, whereas AutoKeras had a 92.4% accuracy. For image regression, which predicted lodging scores from plot images, transfer learning with DenseNet-201 had the best performance (R2 = 0.8303, root mean-squared error (RMSE) = 9.55, mean absolute error (MAE) = 7.03, mean absolute percentage error (MAPE) = 12.54%), followed closely by AutoKeras (R2 = 0.8273, RMSE = 10.65, MAE = 8.24, MAPE = 13.87%). In both tasks, AutoKeras models had up to 40-fold faster inference times compared to the pretrained CNNs. AutoML has significant potential to enhance plant phenotyping capabilities applicable in crop breeding and precision agriculture.

Landscape-Scale Crop Lodging Assessment across Iowa and Illinois Using Synthetic Aperture Radar (SAR) Images

Crop lodging, the tilting of stems from their natural upright position, usually occurs after a heavy storm event. Since lodging of a crop seriously affects its yield, rapid assessment of crop lodging is valuable for farmers, policymakers, agronomists, insurance companies, and relief workers. Synthetic Aperture Radar (SAR) sensors have been recognized as valuable data sources for mapping lodging extent because of their good penetrating power and high-resolution remote sensing ability. Compared to other sources, SAR’s weather and illumination independence and large area coverage at fine spatial resolution (3 m to 20 m) support frequent and detailed observations. Because of these advantages, SAR has the potential in supporting near real-time monitoring of lodging in fields when combined with automated image processing. In this study, a method based on change detection using modified Hidden Markov Random Field (HMRF) and Sentinel-1A data were utilized to identify lodging and map its extent. Results obtained have shown that when lodging occurs, the VH polarization’s backscatter (σVH) increases between the pre-lodging event image and the post-lodging event image. The increase in σVH is due to the increase in volume scattering and vegetation-soil double bounce scattering resulting from the structural changes in the crop canopy. Using Sentinel-1A images and applying our proposed approach across several fields in Iowa and Illinois, we mapped the extent of the 2020 Derecho (wind storm) lodging disaster. In addition, we separated lodged regions into severely and moderately lodged areas. We estimated that approximately 2.56 million acres of corn and 1.27 million acres of soybean were lodged. Further analysis also showed the separation between un-lodged (healthy) fields and lodged fields. The observations in this study can guide future use of SAR-based information for operational crop lodging assessment.

A UAV-based framework for crop lodging assessment

Crop lodging assessment needs to be carried out timely and accurately to ensure valuable information about the location and area where lodging occurs. Many applications have been explored and tested for unmanned aerial vehicle (UAV) visible imagery in agricultural management due to the ability of providing high-space-resolution information. However, there still face many challenges in extracting lodging information using UAV visible imagery, and lacks consensus on an appropriate way to assess crop lodging. The main purpose of this study was to proposed an efficient framework to identify crop lodging at the field scale using UAV visible imagery. This framework contained a two-phase procedure. Meanwhile, three methods were evaluated by providing the appropriate feature subset for objected-based classification to determine the best feature selection method. The results showed that the proposed framework provided high accuracy (94.0 %) for identification of sugarcane lodging. Furthermore, the Boruta algorithm yielded the best feature subset compared with statistical indicators and the RFE algorithm. Thus, the proposed framework based on UAV visible imagery is promising to identify crop lodging precisely, and has great application potential in precision agriculture.

Identifying sunflower lodging based on image fusion and deep semantic segmentation with UAV remote sensing imaging

Sunflower lodging is a common agricultural disorder taking place in the middle and late sunflower growth periods. This disorder reduces the sunflower seed yield, damages the seed quality, and hence usually causes great losses in both crop quantity and quality. Sunflower lodging is mainly caused by extreme and destructive weather events, which have been recently occurring more frequently. This is why it is highly crucial to develop methods for fast and accurate identification of sunflower lodging. In this work, an efficient method for sunflower lodging identification is proposed based on image fusion and deep semantic segmentation of remote sensing images obtained from an unmanned aerial vehicle (UAV). First, the resolution of low-resolution multispectral images was enhanced through matching their features with those of high-resolution visible-range images. Then, for effective lodging assessment, high-quality multispectral images with rich spectral information and high spatial resolution were obtained through fusing the visible-range images and the enhanced multispectral ones. Subsequently, in order to refine the identification outcomes, a variant of the segmentation network (SegNet) deep architecture was developed for semantic segmentation. This variant has skip connections, separable convolution, and a conditional random field. Experimental evaluation shows that the fusion-based approaches clearly outperform the no-fusion ones in terms of the lodging identification accuracy for all compared architectures including support vector machine (SVM), fully convolutional network (FCN), SegNet, and the proposed SegNet variant. Meanwhile, the deep semantic segmentation methods consistently outperform the classical SVM one with hand-crafted features. As well, the improved SegNet method outperformed all of the compared methods and achieved the best accuracies of 84.4% and 89.8% without and with image fusion, respectively, on one test. The corresponding accuracies on another test set were 76.6% and 83.3%, respectively. Moreover, the proposed method can also identify the sunflower lodging and non-lodging patterns and separate them from the background. These capabilities are highly beneficial for lodging hazard assessment and sunflower harvest survey. Overall, the proposed method effectively exploited UAV remote sensing image data with fusion and deep semantic segmentation modules in order to provide a useful reference for sunflower lodging assessment and mapping.

Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet

Rice is a globally important crop that will continue to play an essential role in feeding our world as we grapple with climate change and population growth. Lodging is a primary threat to rice production, decreasing rice yield, and quality. Lodging assessment is a tedious task and requires heavy labor and a long duration due to the vast land areas involved. Newly developed autonomous crop scouting techniques have shown promise in mapping crop fields without any human interaction. By combining autonomous scouting and lodged rice detection with edge computing, it is possible to estimate rice lodging faster and at a much lower cost than previous methods. This study presents an adaptive crop scouting mechanism for Autonomous Unmanned Aerial Vehicles (UAV). We simulate UAV crop scouting of rice fields at multiple levels using deep neural networks and real UAV energy profiles, focusing on areas with high lodging. Using the proposed method, we can scout rice fields 36% faster than conventional scouting methods at 99.25% accuracy.

Culm Morphological Traits Contributing to Lodging Resistance in First Generation NERICA Cultivars Under Foliar Application of Orthosilicic Acid Fertilizer

Lodging resistance in upland rice requires adequate knowledge of the mechanical basis of culm morphological traits (CMTs) with potential for improved structural integrity under orthosilicic acid (OSA) fertilizer. However, assessment of lodging in NERICAs has been based solely on visual scoring which is inadequate to establish resistance to lodging. Ten upland rice cultivars, comprising NERICAs 1 to 8, Moroberekan, and Ofada were grown on loamy-sand soil under four rates (0, 250, 500 and 750 ml ha− 1) of OSA. At 20 days after heading, CMTs related to lodging were assessed among the rice cultivars. Application of 500 and 750 ml ha− 1 of OSA gave the highest CMTs with reduced lumen diameter. Lodging resistance index (LRI) varied significantly among the cultivars (0.34 in NERICA 1 to 0.70 in Ofada). Moroberekan had the highest CMTs with average LRI (0.47). Using Genotype and Genotype × Environment (GGE) comparisons bi-plot, NERICAs 4,6,7,8 and Moroberekan compared well with NERICA 1 for LRI. Bending stress had the highest but negative significant correlation (-0.74**) with LRI. Assessment of CMTs in NERICAs under OSA fertilizer can reveal variations in lodging resistance obscured by visual scoring. OSA fertilizer can improve lodging resistance through increased culm wall thickness and reduced lumen diameter in resistant cultivars such as Moroberekan and NERICA 4. NERICA cultivars with lower LRI will be more desirable to minimize lodging at moderate rate(s) of foliar OSA fertilizer. The need to characterize the CMTs of other NERICAs to establish LRI that confers susceptibility, moderate and high resistance to lodging was emphasized.

UNDERSTANDING OF CROP LODGING INDUCED CHANGES IN SCATTERING MECHANISMS USING RADARSAT-2 AND SENTINEL-1 DERIVED METRICS

Abstract. Crop lodging – the bending of crop stems from the vertical – is a major yield-reducing factor in cereal crops and causes deterioration in grain quality. Accurate assessment of crop lodging is important for improving estimates of crop yield losses, informing insurance loss adjusters and influencing management decisions for subsequent seasons. The role of remote sensing data, particularly synthetic aperture radar (SAR) data has been emphasized in the recent literature for crop lodging assessment. However, the effect of lodging on SAR scattering mechanisms is still unknown. Therefore, this research aims to understand the possible change in scattering mechanisms due to lodging by investigating SAR image pairs before and after lodging. We conducted the study in 26 wheat fields in the Bonifiche Ferraresi farm, located in Jolanda di Savoia, Ferrara, Italy. We measured temporal crop biophysical (e.g. crop angle) parameters and acquired multi-incidence angle RADARSAT-2 (R-2 FQ8-27° and R-2 FQ21-41°) and Sentinel-1 (S-1 40°) images corresponding to the time of field observations. We extracted metrics of SAR scattering mechanisms from RADARSAT-2 and Sentinel-1 image pairs in different zones using the unsupervised H/α decomposition algorithm and Wishart classifier. Contrasting results were obtained at different incidence angles. Bragg surface scattering increased in the case of S-1 (6.8%), R-2 FQ8 (1.8%) while at R-2 FQ21, it decreased (8%) after lodging. The change in double bounce scattering was more prominent at low incidence angle. These observations can guide future use of SAR-based information for operational crop lodging assessment in particular, and sustainable agriculture in general.

Understanding wheat lodging using multi-temporal Sentinel-1 and Sentinel-2 data

Crop lodging assessment is essential for evaluating yield damage and informing crop management decisions for sustainable agricultural production. While a few studies have demonstrated the potential of optical and SAR data for crop lodging assessment, large-scale crop lodging assessment has been hampered by the unavailability of dense satellite time series data. The unprecedented availability of free Sentinel-1 and Sentinel-2 data may provide a basis for operational detection and monitoring of crop lodging. In this context, this study aims to understand the effect of lodging on backscatter/coherence and spectral reflectance derived from Sentinel-1 and Sentinel-2 data and to detect lodging incidence in wheat using time-series analysis. Crop biophysical parameters were measured in the field for both healthy and lodged plots from March to June 2018 in a study site in Ferrara, Italy, and the corresponding Sentinel images were downloaded and processed. The lodged plots were further categorised into different lodging severity classes (moderate, severe and very severe). Temporal profiles of backscatter, coherence, reflectance and continuum removed spectra were studied for healthy and lodging severity classes throughout the stem elongation to ripening growth stages. The Kruskal Wallis and posthoc Tukey tests were used to test for significant differences between different classes. Our results for Sentinel-2 showed that red edge (740 nm) and NIR (865 nm) bands could best distinguish healthy from lodged wheat (particularly healthy and very severe). For Sentinel-1, the analysis revealed the potential of VH backscatter and the complementarity of VV and VH/VV backscatter in distinguishing a maximum number of classes. Our findings demonstrate the potential of Sentinel data for near real-time detection of the incidence and severity of lodging in wheat. To the best of our knowledge, there is no study that has contributed to this application.

Seed Yield and Lodging Assessment in Red Fescue (Festuca rubra L.) Sprayed with Trinexapac-Ethyl

Red fescue (Festuca rubra) is used in seed mixtures for lawns and pastures. It is prone to lodge at flowering, and plant growth regulators (PGRs) are used to prevent lodging, ensuring sufficient pollination. Seed yield and lodging were studied over three years in a red fescue field established with four seeding rates (2, 4, 6 and 8 kg ha−1) and sprayed each year with three doses of the PGR trinexapac-ethyl (250 g L−1) (0, 0.3, 0.6 and 1.2 L ha−1). Half of each plot was sprayed with the PGR and the other half was left unsprayed as control. The degree of lodging was assessed by analysing drone images in the second year of the experiment and using a 10-point scale for scoring lodging at the ground. Generally, application of PGR increased the seed yield but the effect varied between years. There was no interaction between the PGR dosage and seeding rate. We found a positive correlation between the blue intensity of the images and lodging. PGR dosage significantly affected lodging evaluated by visual ranking and the blue intensity of the images, while the seeding rates did not affect lodging. Lodging affected seed yield negatively.

Low Orbiting Satellite and Small UAS-Based High-Resolution Imagery Data to Quantify Crop Lodging: A Case Study in Irrigated Spearmint

Conventional methods for crop lodging assessments need accurate ground observations and tend to be laborious. Lodging assessment methods and accuracy can thus be improved using remote sensing data from small unmanned aerial systems (UASs) and low orbiting satellites (LOSs). With such aim, imagery to assess spearmint crop lodging was acquired using a small UAS at two ground sample distances (GSDs) of 0.01 and 0.03 m. Crop surface model (CSM) and six image color features were extracted from small UAS-based data. These features were then classified into not lodged (NL), partially lodged (PL), and lodged (L) groups. Mean and majority feature classes were obtained for 50 regions of interest (ROI) of size 1 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> each. Features were compared with visual crop lodging ratings using Pearson correlation (r) and Cohen's kappa (CK) coefficients. CSM showed higher assessment accuracy with r ≈ 0.85 and CK > 0.60. Mean percentage red (%R) was observed to have the strongest correlation with visual ratings (r = 0.75 and CK = 0.40-0.59) followed by mean percentage blue (%B), both at 0.01-m GSD. The percentage of lodging calculated from %R and %B maps was also contrasted with similar estimates from LOS-based imagery at 3.00-m GSD with no statistical differences found at 5% level.

Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging.

Rice lodging severely affects harvest yield. Traditional evaluation methods and manual on-site measurement are found to be time-consuming, labor-intensive, and cost-intensive. In this study, a new method for rice lodging assessment based on a deep learning UNet (U-shaped Network) architecture was proposed. The UAV (unmanned aerial vehicle) equipped with a high-resolution digital camera and a three-band multispectral camera synchronously was used to collect lodged and non-lodged rice images at an altitude of 100 m. After splicing and cropping the original images, the datasets with the lodged and non-lodged rice image samples were established by augmenting for building a UNet model. The research results showed that the dice coefficients in RGB (Red, Green and Blue) image and multispectral image test set were 0.9442 and 0.9284, respectively. The rice lodging recognition effect using the RGB images without feature extraction is better than that of multispectral images. The findings of this study are useful for rice lodging investigations by different optical sensors, which can provide an important method for large-area, high-efficiency, and low-cost rice lodging monitoring research.

WHEAT LODGING ASSESSMENT USING MULTISPECTRAL UAV DATA

Abstract. Lodging is a major yield-reducing factors in wheat, causing reductions up to 80%. Timely detection of lodging can reduce its impacts and support proper decisions regarding expected yield, crop price or its insurance. Since the incidence of lodging is heterogeneous within a field, very high-resolution remote sensing data can be viable for accurate and prompt spatio-temporal assessment of lodging severity. As such unmanned aerial vehicles (UAVs) provide a versatile and cost-effective solution to monitor crops on a small scale with sub-centimetre spatial resolution. In this study, we analysed the spectral variability between different grades of lodging severity (non-lodged (NL), moderate (ML), severe (SL) and very severe (VSL)) and classified them using high-resolution UAV data. Multispectral orthomosaic UAV images with 5cm resolution and nine bands (covering the VIS-NIR spectrum with Sentinel-2 filters) were acquired in May 2018 for two wheat fields in Bonifiche Ferraresi farm, Jolanda di Savoia, Italy. Concurrent to the time of image acquisition, a field campaign was carried out in which crop characteristics and lodging related parameters were collected. The results showed that reflectance magnitude increased with lodging severity and demonstrated that the red-edge and NIR bands can be used to clearly discriminate between NL and lodged (all grades) wheat and to some extent between different lodging classes (ML, SL and VSL). The nearest neighbourhood classification performed using an object-based segmentation yielded optimal results with an overall accuracy of 90%, thus demonstrating the use of multispectral UAV data as a promising tool for wheat lodging assessment.

High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat.

Novel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits from images and sensors but is limited in advancing our understanding of crop development and architecture. Lodging is a complex trait that significantly impacts yield and quality in many crops including wheat. Conventional visual assessment methods for lodging are time-consuming, relatively low-throughput, and subjective, limiting phenotyping accuracy and population sizes in breeding and genetics studies. Here, we demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging, which significantly impacts yield and quality in many crops including wheat. We tested and validated quantitative assessment of lodging on 2,640 wheat breeding plots over the course of 2 years using differential digital elevation models from UAS. High correlations of digital measures of lodging to visual estimates and equivalent broad-sense heritability demonstrate this approach is amenable for reproducible assessment of lodging in large breeding nurseries. Using these high-throughput measures to assess the underlying genetic architecture of lodging in wheat, we applied genome-wide association analysis and identified a key genomic region on chromosome 2A, consistent across digital and visual scores of lodging. However, these associations accounted for a very minor portion of the total phenotypic variance. We therefore investigated whole genome prediction models and found high prediction accuracies across populations and environments. This adequately accounted for the highly polygenic genetic architecture of numerous small effect loci, consistent with the previously described complex genetic architecture of lodging in wheat. Our study provides a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to uncover the genetic factors and increase the rate of gain for complex traits in crop breeding.

Remote sensing-based crop lodging assessment: Current status and perspectives

Rapid and quantitative assessment of crop lodging is important for understanding the causes of the phenomena, improving crop management, making better production and supporting loss estimates in general. Accurate information on the location and timing of crop lodging is valuable for farmers, agronomists, insurance loss adjusters, and policymakers. Lodging studies can be performed to assess the impact of lodging events or to model the risk of occurrence, both of which rely on information that can be acquired by field observations, from meteorological data and from remote sensing (RS). While studies applying RS data to assess crop lodging dates back three decades, there has been no comprehensive review of the status, potential, current approaches, and challenges in this domain. In this position paper, we review the trends in field/lab-based and RS-based studies for crop lodging assessment and discuss the strengths and weaknesses of current approaches. Theoretical background on crop lodging is presented, and the scope of RS in assessing plant characteristics associated with lodging is reviewed and discussed. The review focuses on RS-based studies, grouping them according to the platform deployed (i.e., ground-based, airborne and spaceborne), with an emphasis on analyzing the pros and cons of the technology. Finally, the challenges, research gaps, perspectives for future research, and an outlook on new sensors and platforms are presented to provide state-of-the-art and future scenarios of RS in lodging assessment. Our review reveals that the use of RS techniques in crop lodging assessment is still in an experimental stage. However, there is increasing interest within the RS scientific community (based on the increased rate of publications over time) to investigate its use for crop lodging detection and risk mapping. The existing satellite-based lodging assessment studies are very few, and the operational application of the current approaches over large spatial extents seems to be the biggest challenge. We identify opportunities for future studies that can develop quantitative models for estimating lodging severity and mapping lodging risk using RS data.