Computers and Electronics in Agriculture最新文献

{"title":"Estimating canopy chlorophyll content of powdery mildew stressed winter wheat by different spatial resolutions of UAV-imagery","authors":"Yang Liu ,&nbsp;Mingjia Liu ,&nbsp;Guohui Liu ,&nbsp;Hong Sun ,&nbsp;Lulu An ,&nbsp;Ruomei Zhao ,&nbsp;Weijie Tang ,&nbsp;Fangkui Zhao ,&nbsp;Xiaojing Yan ,&nbsp;Yuntao Ma ,&nbsp;Minzan Li","doi":"10.1016/j.compag.2024.109621","DOIUrl":"10.1016/j.compag.2024.109621","url":null,"abstract":"<div><div>The wheat powdery mildew (WPM) always alters the pigment and structure of the leaf and canopy, disrupting crop growth. A challenge on the WPM monitoring is the limited capability of unmanned aerial vehicle (UAV)-based canopy images to directly indicate complex infection symptoms. However, the WPM infection markedly changed canopy chlorophyll content (CCC), which encompassed both leaf and canopy attributes, and this change was relatively easy to capture by UAV remote sensing. Thus, this study aimed to estimate CCC to indirectly explore WPM using different scales of UAV image features. UAV-based winter wheat canopy images were acquired continuously in the field during the early, middle, and late infection stages after being artificially inoculated with fungal pathogens at the Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Xinxiang, China, in 2022. The study evaluated the potential of spectral (Spe) and textural (Tex) features and their combination to estimate CCC and characterize WPM dynamic. Considering the impacts of spatial scales, the selected Spe and Tex textures were calculated from images with 1, 2, 5, 10, 15, and 20 cm spatial resolution. The changes in different types of features under WPM stress and their response to CCC were analyzed. Three regression methods, including extreme gradient boosting regression (XGBR), multilayer perceptron regression (MLPR), and partial least squares regression (PLSR) were used to estimate CCC based on the acquired sensitive features and track the infection status. Results showed that the image spatial resolution barely affected the Spe performance while notably affecting the Tex performance. The performance of estimating CCC under WPM stress was superior for Tex (ranging from 1 to 20 cm spatial resolution imagery) compared to Spe features. The best modeling result was the combination of Spe with Tex features from 1 and 10 cm (R² = 0.82, RMSE = 28.49 mg/L, NRMSE = 12.38 %), which could be related to information captured from different viewpoints. Although finer spatial resolution was advantageous for capturing the complex symptoms caused by WPM, it increased the burden on UAV missions. UAV multispectral imagery with the 10 cm spatial resolution using XGBR (R² = 0.74, RMSE = 33.48 mg/L, NRMSE = 14.55 %) might be used as an optimization scheme for estimating CCC and exploring WPM stress, as it decreased the cost associated with data processing and time in the actual operation. This study indirectly characterizes the condition of WPM infection by estimating CCC, which provides promising and valuable insights for disease management and control in the field.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109621"},"PeriodicalIF":7.7,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Yield prediction of root crops in field using remote sensing: A comprehensive review","authors":"Hanhui Jiang ,&nbsp;Liguo Jiang ,&nbsp;Leilei He ,&nbsp;Bryan Gilbert Murengami ,&nbsp;Xudong Jing ,&nbsp;Paula A. Misiewicz ,&nbsp;Fernando Auat Cheein ,&nbsp;Longsheng Fu","doi":"10.1016/j.compag.2024.109600","DOIUrl":"10.1016/j.compag.2024.109600","url":null,"abstract":"<div><div>Yield information of root crops guides precision agriculture efforts and optimizes resource allocation. Predicting root crops prior to harvest is crucial to crop management and planning and requires obtaining root crop yield without damaging them. Non-destructive access to yield of root crops is challenging because of the edible portion of the crops being located underground, which impacts precision agriculture technology application. Remote sensing provides a possible way to solve this problem. There are no review reports on yield prediction for root crops using remote sensing, though root crops share the same growth characteristic of producing edible parts underground, which makes their yield prediction techniques similar. In this work, a total of 49 sources on the use of remote sensing techniques for yield prediction of root crops in field were collected, analyzed and discussed from the aspects of remote sensing platforms, input features and modelling methods. In terms of usage counts of remote sensing platforms, ground penetrating radars that are directly exposed to edible parts of root crops have the potential to be applied to root crop yield predictions, while spaceborne platforms are the current trend, accounting for 51 %. Feature combination from environment and crop itself is beneficial to crop yield prediction models, particularly the processed-based crop models. It is recommended to collect data time after ensuring specific root data types. Additionally, full-cycle data is suggested to be used to increase robustness of root crop yield prediction models. The result showed that plant-by-plant detection was only applied to radar-based platforms while spectral-based platforms are still in plot level, which further investigated that improving accuracy of root crop yield prediction through individual above ground phenotypic traits. The review is intended to summarize the development of root crop yield prediction using remote sensing and put forward further for further improvement.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109600"},"PeriodicalIF":7.7,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A precise maize seeding parameter monitoring system at the end of seed tube: Improving monitoring accuracy using near-infrared diffusion emission-diffuse reflectance (NIRDE-DR)","authors":"Chengkun Zhai ,&nbsp;Caiyun Lu ,&nbsp;Hongwen Li ,&nbsp;Jin He ,&nbsp;Qingjie Wang ,&nbsp;Fangle Chang ,&nbsp;Jinshuo Bi ,&nbsp;Zhengyang Wu","doi":"10.1016/j.compag.2024.109626","DOIUrl":"10.1016/j.compag.2024.109626","url":null,"abstract":"<div><div>In the context of precision agriculture, real-time monitoring of maize seeding parameters is of great significance for evaluating seeding situations and ensuring seeding quality. At present, seeding monitoring mainly uses the through beam photoelectric (TBP) method, which is susceptible to dust and can only be used at the upper part of the seed tube, affecting monitoring accuracy. For this purpose, this study developed a maize seeding parameter monitoring system based on near-infrared diffusion emission-diffuse reflectance (NIRDE-DR), which utilizes the diffusion emission effect of NIR rays to form a three-dimensional monitoring area for maize seeds without missed monitoring. When maize seeds with uneven surfaces enter the monitoring area, the diffuse reflectance effect of the seeds on NIR rays is utilized to change the electrical signal of the monitoring system, and the recognition of falling seeds is achieved by processing the electrical signal. NIRDE-DR takes advantage of the small size of dust particles, which are difficult to form a reflective area, effectively avoiding dust interference. Therefore, it can perform high-precision monitoring at the end of the seed tube. The NIR spectrum of coated maize seeds was measured, and the NIR wavenumber with the lowest absorbance and strongest reflection ability of maize seeds was determined as the target wavenumber of the monitoring system. The impact of the horizontal distance from the monitoring surface to the inner wall of the seed tube (HD) on seeding monitoring was clarified. The value of HD in the developed seeding parameter monitoring system was determined, so that when the NIR rays are emitted into the seed tube, they can cover the entire cross-section of the end of the seed tube without being reflected by dust, avoiding missed monitoring and false monitoring. A signal shielding filtering algorithm based on sawtooth wave shielding was proposed. In regard to the characteristic of high-frequency sawtooth wave in the signal generated by seeds passing through the monitoring area, the first rising edge of the signal is used as the seed recognition signal. By analyzing the duration of high-frequency sawtooth wave and the interval between adjacent seeds, the shielding time of the interference signal is determined to achieve effective noise reduction. Performance evaluation test in the bench results showed that NIRDE-DR has a better recognition effect on maize seeds than TBP. Performance evaluation test in the field showed that at a seeding speed of 6–14 km/h, the maximum monitoring error of the developed system for seeding quantity was 7.98 %, and the maximum monitoring error for seeding qualified rate was 7.69 %. The developed seeding parameter monitoring system has good performance, providing a reference for the advancement of seeding parameter monitoring technology at the end of the seed tube.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109626"},"PeriodicalIF":7.7,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge informed hybrid machine learning in agricultural yield prediction","authors":"Malte von Bloh ,&nbsp;David Lobell ,&nbsp;Senthold Asseng","doi":"10.1016/j.compag.2024.109606","DOIUrl":"10.1016/j.compag.2024.109606","url":null,"abstract":"<div><div>Research on yield predictions is dominated by two approaches: machine learning and process-based models. Machine learning has shown impressive results in capturing complex relationships but is often limited by data availability in agriculture. Conversely, process-based models, with over 60 years of research history, simulate crop growth processes using biophysical equations. Here, we present a method to transfer domain knowledge from the Decision Support System for Agrotechnology Transfer framework (DSSAT) using the Nwheat crop simulation process-model into neural networks and random forest for predicting wheat yield at field scale. Expanding the feature and distribution space involved simulating crop parameters and synthetic samples through the utilization of observed and historical weather recordings, as well as future climate projections. We demonstrated that neural networks can learn both general crop growth and yield processes and then effectively adapt to regional, field-specific growth patterns using synthetic and high-resolution field data. This approach boosts overall performance and reduces model error by 8 % compared to a purely data-centric model without process-knowledge transfer and solely trained on observed field data and features. Synthetic samples generated from warmer conditions were the greatest driver for improvements and we showed that the climate scenario for data generation is more important than the actual synthetic data set size. The proposed method shows the potential of combining process-based and machine-learning models, highlighting the potential to leverage the strengths of both methods in a collaborative manner.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109606"},"PeriodicalIF":7.7,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound technology supplements zinc in soybean seeds and increases the photosynthetic efficiency of seedlings","authors":"Érica Souza Gomes ,&nbsp;Gustavo Roberto Fonseca de Oliveira ,&nbsp;Arthur Almeida Rodrigues ,&nbsp;Camila Graziela Corrêa ,&nbsp;Eduardo de Almeida ,&nbsp;Hudson Wallace Pereira de Carvalho ,&nbsp;Valter Arthur ,&nbsp;Edvaldo Aparecido Amaral da Silva ,&nbsp;Arthur I. Novikov ,&nbsp;Clíssia Barboza Mastrangelo","doi":"10.1016/j.compag.2024.109619","DOIUrl":"10.1016/j.compag.2024.109619","url":null,"abstract":"<div><div>Strategies to increase the concentration of essential micronutrients for the plant cycle have made a remarkable contribution to agriculture. Ultrasonic waves have the potential to increase cell wall permeability and enhance the chemical composition of seed tissues. In this context, the aim of this study was to verify if it is possible to increase the zinc (Zn) supplementation of soybean seeds through their controlled exposure to ultrasonic waves with improvements in the photosynthetic efficiency (Fv/Fm) of the resulting seedlings. Initially, we investigated the impact of ultrasonic waves on the physical, physiological and spectral parameters of soybean seeds. Next, the seeds were treated with Zn and analyzed by X-ray fluorescence spectroscopy to better understand the kinetics of Zn uptake. Finally, we evaluated the germination, vigor, pigments and photosynthetic performance of seedlings. The main results showed that ultrasound modifies the structure of the seed coat without interfering with the dynamics of water absorption and the germination capacity of the seeds. The changes promoted by the technology favor Zn supplementation of more than 100 % in the seeds. In addition, the resulting seedlings show Fv/Fm values 92.7 % higher than the control, and an increase in chlorophyll fluorescence, initial fluorescence, and anthocyanin. We show that ultrasonic wave technology combined with Zn treatment improves the performance of soybean seeds, producing seedlings with superior photosynthetic efficiency.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109619"},"PeriodicalIF":7.7,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive models of air temperatures inside a naturally ventilated vehicle transporting weaner pigs","authors":"Guoxing Chen,&nbsp;Guoqiang Zhang,&nbsp;Li Rong","doi":"10.1016/j.compag.2024.109591","DOIUrl":"10.1016/j.compag.2024.109591","url":null,"abstract":"<div><div>Maintaining proper interior thermal condition during transportation is vital for animal welfare and sustainability of livestock supply chain. This study investigated the air temperatures inside a multi-deck naturally ventilated vehicle when transporting weaner pigs under warmer weather condition by using computational fluid dynamics (CFD). Predictive models of interior air temperatures were developed by using response surface methodology (RSM) and gradient boosting machine (GBM) with the inputs of exterior air temperature, vehicle speed, wind speed, incident wind angle and opening height of shutter based on the dataset generated from CFD simulations and validated as well. The results showed that predictive models developed by RSM were sufficient for predicting the interior air temperatures of moving naturally ventilated livestock vehicle, and GMB could improve the prediction accuracy moderately. RSM models indicated that the interior temperatures increased linearly with the increase in exterior air temperature, opening height and wind speed while insensitive to vehicle speed. GMB model indicated that the plane-average air temperature of front compartments was 2.2 °C higher than those of the other two compartments at the same deck, and the air temperature increased slightly from the bottom to the upper deck. High spatial variations in air temperature were observed inside the moving livestock vehicle, which poses a challenge on monitoring interior air temperatures. The developed models are expected to predict the interior air temperatures and provide suggestion on regulating ventilation systems in advance. Further study could be conducted to investigate the optimum control of opening for improving the natural ventilation potential.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109591"},"PeriodicalIF":7.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A meta transfer learning-driven few-shot fault diagnosis method for combine harvester gearboxes","authors":"Daoming She ,&nbsp;Zhichao Yang ,&nbsp;Yudan Duan ,&nbsp;Michael G. Pecht","doi":"10.1016/j.compag.2024.109605","DOIUrl":"10.1016/j.compag.2024.109605","url":null,"abstract":"<div><div>Combine harvester gearboxes operate for extended periods under variable operating conditions, making it costly to gather sufficient fault data. A meta transfer learning-driven fault diagnosis method for combine harvester gearboxes is proposed to solve the complex operating conditions and scarce fault samples. The meta learning is employed to train the model so that the performance of the proposed method is not contingent upon the quantity of training data. The multi-step loss optimization (MSL) method is introduced to improve the inner loop and address the unstable update gradients in training. The enhanced method uses each task to refine the model updating strategy, thus circumventing the gradient explosion and decay. The proposed method employs conditional domain adversarial network to extract deep discriminative features from both domains. The batch feature constraint (BFC) is proposed to balance the features’ transferability and class discriminability. A weight-balancing strategy is employed to reconstruct the training loss function, enabling gearbox fault diagnosis under variable operating conditions with few-shot data. The effectiveness of the proposed method is validated through data collected from the combined harvester gearbox’s fault diagnosis experimental rig.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109605"},"PeriodicalIF":7.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of the front-mounted fertilizer pipe strip rotary tillage device by modeling the wide-seedbed characteristics and power consumption","authors":"Pengfei Zhao ,&nbsp;Xiaojun Gao ,&nbsp;Xiaoteng Ju ,&nbsp;Pengkun Yang ,&nbsp;Qingbin Song ,&nbsp;Yuxiang Huang ,&nbsp;Zhiqi Zheng","doi":"10.1016/j.compag.2024.109624","DOIUrl":"10.1016/j.compag.2024.109624","url":null,"abstract":"<div><div>Conventional wheat wide-seedbed strip rotary tillage devices face several disadvantages, including low straw cleaning efficiency, inadequate soil pulverization, inconsistent sowing depth, and high-power consumption. Therefore, this study introduces a novel front-mounted fertilizer pipe wide-seedbed strip rotary tillage device. The fertilizer pipe is strategically positioned within the gap between the rotary tillage blade groups, enabling an integrated operation with the rotary tillage blade assembly. To minimize trenching resistance, the design combines the fertilizer pipe with a sliding knife. Through theoretical analysis, this study analyzes the operating principles of the front-mounted fertilizer pipe wide-seedbed strip rotary tillage device, explores the structural characteristics of the Standard strip rotary tillage blade Group (SG) and Trapezoidal straight blade Group (TG), and examines the sources of power consumption during operation. A corresponding discrete element simulation model is constructed, and its validity is confirmed through soil bin experiments. These experiments underscore the model’s effectiveness. Subsequently, the study compares the effects of the SG and TG on the wide-seedbed strip rotary tillage device based on simulation experiments. Additionally, a regression orthogonal rotation combination experimental design is employed to investigate how the rotation speed of the strip rotary tillage blade group, the forward spacing between the fertilizer pipe and blade shaft, and the types of blades affect straw cleaning and soil crushing. Moreover, response surface methodology is employed to clarify the influence of these factors on the experimental outcomes. Optimization results indicate that under a rotation speed of 270 rpm for the strip rotary tillage blade group, a forward spacing of 30 mm, and a combination of SG and TG, the device performs optimally. Under these conditions, it achieves a theoretical straw cleaning rate of 55.38 %, a soil crushing rate of 79.56 %, and a total power consumption of 3.26 kW. These findings support the development and optimization of wheat wide seedling belt sowing devices.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109624"},"PeriodicalIF":7.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and optimization of a high-speed maize seed guiding device based on DEM-CFD coupling method","authors":"Hongsheng Li,&nbsp;Li Yang,&nbsp;Dongxing Zhang,&nbsp;Cui Tao,&nbsp;Xiantao He,&nbsp;Chunji Xie,&nbsp;Chuan Li,&nbsp;Zhaohui Du,&nbsp;Tianpu Xiao,&nbsp;Zhimin Li,&nbsp;Haoyu Wang","doi":"10.1016/j.compag.2024.109604","DOIUrl":"10.1016/j.compag.2024.109604","url":null,"abstract":"<div><div>This study designs a pneumatic seed delivery system for a high-speed corn planter based on the Venturi effect, aimed at improving seeding uniformity and efficiency. By utilizing an external blower to generate airflow, the seeds are accelerated within the seed tube, reducing collisions between seeds and achieving stable seed transport. The research adopts a gas–solid two-phase method to explore the effects of airflow rate and pressure on seed acceleration and delivery, revealing the principles of gas dynamics in seed transportation. DEM-CFD simulation technology, which integrates Discrete Element Method and Computational Fluid Dynamics, is employed to more accurately simulate the physical processes within the granular-fluid system, ensuring rapid acceleration and stable transport of seeds. Through response surface methodology (RSM), the structural parameters of the seed tube were optimized, identifying the main factors and optimal levels influencing seed delivery performance. Experimental results demonstrate that the newly designed seed tube significantly enhances seed movement speed and seeding uniformity under high-speed seeding conditions, confirming its potential application in high-precision planting.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109604"},"PeriodicalIF":7.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-throughput phenotypic traits estimation of faba bean based on machine learning and drone-based multimodal data","authors":"Yishan Ji ,&nbsp;Zehao Liu ,&nbsp;Rong Liu ,&nbsp;Zhirui Wang ,&nbsp;Xuxiao Zong ,&nbsp;Tao Yang","doi":"10.1016/j.compag.2024.109584","DOIUrl":"10.1016/j.compag.2024.109584","url":null,"abstract":"<div><div>Faba bean is a global food legume crop, and it is essential to accurately and timely determine its plant height, above-ground biomass (fresh and dry weight) and yield for enhancing cultivation practices and planning the next planting season. Traditional ground sampling is a time-consuming and labor-intensive approach. However, the utilization of an unmanned aerial vehicle (UAV) as a high-throughput technique offers a promising alternative strategy for estimating crop phenotypic traits. In this study, a two-year experiment was conducted from 2020 to 2022, where UAV-based multimodal data were collected using red–green–blue, multispectral and thermal infrared sensors. The variables derived from these three sensors and their combinations were used to estimate the fresh weight, dry weight and yield of faba bean based on extreme gradient boosting (XGBoost), random forest, multiple linear regression and k-nearest neighbor algorithms. The following findings were obtained: (1) The use of the maximum percentile crop surface model resulted in the highest estimation accuracy for faba bean plant height. (2) Fusion data from multiple sensors increased the estimation accuracy of faba bean fresh weight, dry weight and yield, the coefficient of determination (<em>R</em>²) improved by 14.22%, 1.45%, and 18.76%, respectively, compared with the best estimation accuracy of a single sensor. (3) The XGBoost algorithm outperformed the other algorithms in estimating fresh weight, dry weight and yield of faba bean. These results demonstrate that multiple sensors and appropriate algorithms can be used to effectively estimate faba bean phenotypic traits and provide valuable insights for agricultural remote sensing research.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109584"},"PeriodicalIF":7.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}