Precision Agriculture最新文献

{"title":"Integrative approaches in modern agriculture: IoT, ML and AI for disease forecasting amidst climate change","authors":"Payam Delfani, Vishnukiran Thuraga, Bikram Banerjee, Aakash Chawade","doi":"10.1007/s11119-024-10164-7","DOIUrl":"https://doi.org/10.1007/s11119-024-10164-7","url":null,"abstract":"<p>Plant disease forecasting models, driven by concurrent data and advanced technologies, are reliable tools for accurate prediction of disease outbreaks in achieving sustainable and productive agricultural systems. Optimal integration of Internet of Things (IoTs), machine learning (ML) techniques and artificial intelligence (AI), further augment the capabilities of these models in empowering farmers with proactive disease control measures towards modern agriculture manifested by efficient resource management, reduced diseases and higher crop yields. This article summarizes the role of disease forecasting models in crop management, emphasizing the advancements and applications of AI and ML in disease prediction, challenges and future directions in the field via (a) The technological foundations and need for validation testing of models, (b) The advancements in disease forecasting with the importance of high-quality publicly available data and (c) The challenges and future directions for the development of transparent and interpretable open-source AI models. Further improvement of these models needs investment in continuous innovative research with collaboration and data sharing among agricultural stakeholders.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"59 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues","authors":"Jyoti S. Jennewein, W. Hively, Brian T. Lamb, Craig S. T. Daughtry, Resham Thapa, Alison Thieme, Chris Reberg-Horton, Steven Mirsky","doi":"10.1007/s11119-024-10159-4","DOIUrl":"https://doi.org/10.1007/s11119-024-10159-4","url":null,"abstract":"&lt;h3 data-test=\"abstract-sub-heading\"&gt;Purpose&lt;/h3&gt;&lt;p&gt;Cover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy.&lt;/p&gt;&lt;h3 data-test=\"abstract-sub-heading\"&gt;Methods&lt;/h3&gt;&lt;p&gt;he objective of this study was to evaluate the efficacy of spectroscopy instruments to quantify crop residue biochemical traits in cover crop agriculture systems using partial least squares regression models and a combination of (1) the band equivalent reflectance (BER) of the &lt;i&gt;PRecursore IperSpettrale della Missione Applicativa&lt;/i&gt; (PRISMA) imaging spectroscopy sensor derived from laboratory collected Analytical Spectral Devices (ASD) spectra (&lt;i&gt;n&lt;/i&gt; = 296) of 11 cover crop species and three cash crop species, and (2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (&lt;i&gt;n&lt;/i&gt; = 65). Spectral range was constrained to 1200 to 2400 nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches.&lt;/p&gt;&lt;h3 data-test=\"abstract-sub-heading\"&gt;Results&lt;/h3&gt;&lt;p&gt;Models using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. &lt;i&gt;R&lt;/i&gt;&lt;sup&gt;&lt;i&gt;2&lt;/i&gt;&lt;/sup&gt; = 0.86 − 0.98; RMSE = 0.24 − 4.25%) and results indicate that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. &lt;i&gt;R&lt;/i&gt;&lt;sup&gt;&lt;i&gt;2&lt;/i&gt;&lt;/sup&gt; = 0.65 − 0.75; RMSE = 2.71 − 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. &lt;i&gt;R&lt;/i&gt;&lt;sup&gt;&lt;i&gt;2&lt;/i&gt;&lt;/sup&gt; = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38–1.85%). PRISMA imagery models were also influenced by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation.&lt;/p&gt;&lt;h3 data-test=\"abstract-sub-heading\"&gt;Conclusion&lt;/h3&gt;&lt;p&gt;As spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an eme","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"2015 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Promoting excellence or discouraging mediocrity – a policy framework assessment for precision agriculture technologies adoption","authors":"Georgios Kleftodimos, Leonidas Sotirios Kyrgiakos, Stelios Kartakis, Christina Kleisiari, Marios Vasileiou, Marios Dominikos Kremantzis, George Vlontzos","doi":"10.1007/s11119-024-10160-x","DOIUrl":"https://doi.org/10.1007/s11119-024-10160-x","url":null,"abstract":"<p>Precision Agriculture Technologies (PATs) are providing a great potential in alleviating adverse impacts arising from climate change. This study evaluates the decision-making process of farmers regarding the adoption and implementation of PATs in potato agricultural cooperative in Northern Greece. For this purpose, a bio-economic model utilizing mathematical programming techniques was designed and applied to three different farms producing Protected Geographical Indication (PGI) potato of Kato Nevrokopi. The proposed model aims to incorporate the existing management methods of farming systems and their associated characteristics. Its objective is to analyse the aspirations of farmers to adopt new practices, considering agronomic, environmental, and policy limitations. Special focus was paid to two distinct scenarios: (a) subsiding PATs adopters or (b) penalizing the non-adopters. Results indicated that subsidy provision 594–650€/ha would have a greater impact on PATs profitability. Lastly, based on the results, further explanations of incentives towards promoting the adoption of novel practices, ensuring the long-term viability of agricultural systems, are proposed.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"54 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial and temporal patterns of cotton profitability in management zones based on soil properties and topography","authors":"Jasmine Neupane, Chenggang Wang, Glen L. Ritchie, Fangyuan Zhang, Sanjit K. Deb, Wenxuan Guo","doi":"10.1007/s11119-024-10158-5","DOIUrl":"https://doi.org/10.1007/s11119-024-10158-5","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Understanding spatial and temporal variability of absolute and relative profit within fields provides a basis for site-specific management of limited agricultural inputs such as water. The objectives of this study were to evaluate the pattern of spatial and temporal variation of cotton profitability and to assess the stability of profit in management zones (MZs) created based on soil properties and topography.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This study analyzed profitability patterns in eight commercially managed fields in the Southern High Plains from 2000 to 2003. Each field was divided into 30 m grids and soil physical properties, topography, and lint yield were collected for each grid. Based on the input cost and output prices, profit was also calculated for each grid. Clusters or MZs based on soil and topographic properties were created for each field using the partitioning around medoids (PAM) clustering algorithm. ANOVA and Least Significant Difference tests were conducted to determine the difference in profit among the clusters over multiple years.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>In four of the eight fields, the spatial pattern of profit was consistent across multiple years, indicating the potential of using MZs for site-specific input management. For the rest of the fields, the profit pattern in clusters was inconsistent across multiple years, indicating the need for within-season dynamic MZs.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The variability in soil and topographic properties influenced the profitability of management zones within a field across multiple years. Hence, this study indicates that understanding the variability in profit patterns in management zones can help to determine the best strategy for field-specific and year-specific precision input management. </p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"57 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Are Indonesian rice farmers ready to adopt precision agricultural technologies?","authors":"Agung B. Santoso, Evawaty S. Ulina, Siti F. Batubara, Novia Chairuman, Sudarmaji, Siti D. Indrasari, Arlyna B. Pustika, Nana Sutrisna, Yanto Surdianto, Rahmini, Vivi Aryati, Erpina D. Manurung, Hendri F. P. Purba, Wasis Senoaji, Noldy R. E. Kotta, Dorkas Parhusip, Widihastuty, Ani Mugiasih, Jeannette M. Lumban Tobing","doi":"10.1007/s11119-024-10156-7","DOIUrl":"https://doi.org/10.1007/s11119-024-10156-7","url":null,"abstract":"<p>Precision agriculture technologies (PATs) are believed to be able to ensure the sustainability of rice production. However, the adoption of PATs in developing countries is much lower than in developed countries. The basic question of our research is how Indonesian rice farmers are ready to adopt precision agriculture since they are smallholder farmers. Data was collected from 521 rice farmers in five Indonesian provinces, i.e. North Sumatra, West Java, Yogyakarta, South Sulawesi, and East Nusa Tenggara, in 2023. Farmers were interviewed face to face using structured questionnaires. The data were analysed using Partial Least Squares-Structural Equation Modelling (PLS-SEM) through the Python software. The results showed that Indonesian rice farmers have a moderate level of readiness. The mean value of the capabilities and opportunities indicators were 2.54 to 3.8, while the range for the opportunity’s indicator is 3.23 to 4.11, larger than the capabilities indicators. The level of precision agriculture implementation on Indonesian rice farmers was significant influenced by management (β = 0.42, t = 7.11, <i>p</i> &lt; 0.05), environment (β = 0.17, t = 3.63, <i>p</i> &lt; 0.05), readiness (β = 0.14, t = 2.51, <i>p</i> &lt; 0.05), and technology (β = 0.10, t = 2.12, <i>p</i> &lt; 0.05), economy (β = 0.09, t = 3.63, <i>p</i> &lt; 0.05), and technology² (β = -0.072, t = 3.5, <i>p</i> &lt; 0.05). Meanwhile, farmer readiness was significantly influenced by opportunity (β = 0.39, t = 6.64, <i>p</i> &lt; 0.05) and capabilities (β = 0.43, t = 6.82, <i>p</i> &lt; 0.05). This research provides information on the status of human resource capacity in exploiting opportunities for implementing precision agriculture and technical policy advice. The Indonesian government should improve farmers’ skills in information technology, Global Positioning Systems (GPS), and sensor technology in agricultural sectors, and facilitate access to technology and resources in order to increase rice farmers’ readiness to adopt PATs. For opportunity indicators, however, further research is needed to determine which components require immediate attention for construction or development.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"182 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing visual autonomous navigation in row-based crops with effective synthetic data generation","authors":"Mauro Martini, Marco Ambrosio, Alessandro Navone, Brenno Tuberga, Marcello Chiaberge","doi":"10.1007/s11119-024-10157-6","DOIUrl":"https://doi.org/10.1007/s11119-024-10157-6","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Introduction</h3><p>Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and in-field validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments and deep visual perception are spreading as successful tools to speed up the development of robust navigation with low-cost RGB-D cameras.</p><h3 data-test=\"abstract-sub-heading\">Materials and methods</h3><p>In this context, the contribution of this work resides in a complete framework to fully exploit synthetic data for a robust visual control of mobile robots. A wide realistic multi-crops dataset is accurately generated to train deep semantic segmentation networks and enabling robust performance in challenging real-world conditions. An automatic parametric approach enables an easy customization of virtual field geometry and features for a fast reliable evaluation of navigation algorithms.</p><h3 data-test=\"abstract-sub-heading\">Results and conclusion</h3><p>The high quality of the generated synthetic dataset is demonstrated by an extensive experimentation with real crops images and benchmarking the resulting robot navigation both in virtual and real fields with relevant metrics.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"71 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141309051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unmanned aerial system plant protection products spraying performance evaluation on a vineyard","authors":"Alberto Sassu, Vasilis Psiroukis, Francesco Bettucci, Luca Ghiani, Spyros Fountas, Filippo Gambella","doi":"10.1007/s11119-024-10155-8","DOIUrl":"https://doi.org/10.1007/s11119-024-10155-8","url":null,"abstract":"<p>In the context of increasing global food demand and the urgent need for production processes optimization, plant protection products play a key role in safeguarding crops from insects, pests, and fungi, responsible of plant diseases proliferation and yield losses. Despite the inaccurate distribution of conventional aerial spraying performed by airplanes and helicopters, Unmanned Aerial Spraying Systems (UASSs) offer low health risks and operational cost solutions, preserving crops and soil from physical damage. This study explores the impact of UASS flight height (2 m and 2.5 m above ground level), speed (1 m s⁻¹ and 1.5 m s⁻¹), and position (over the canopy and the inter-row) on vineyard aerial spraying efficiency by analysing Water Sensitive Papers droplet coverage, density, and Number Median Diameter using a MATLAB script. Flight position factor, more than others, influenced the application results. The specific configuration of 2 m altitude, 1.5 m s⁻¹ cruising speed, and inter-row positioning yielded the best results in terms of canopy coverage, minimizing off-target and ground dispersion, and represented the best setting to facilitate droplets penetration, reaching the lowest parts generally more affected from disease. Further research is needed to assess UASS aerial PPP distribution effectiveness and environmental impact in agriculture, crucial for technology implementation, especially in countries where aerial treatments are not yet permitted.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"70 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interviews with farmers from the US corn belt highlight opportunity for improved decision support systems and continued structural barriers to farmland diversification","authors":"Matthew Nowatzke, Lijing Gao, Michael C. Dorneich, Emily A. Heaton, Andy VanLoocke","doi":"10.1007/s11119-024-10154-9","DOIUrl":"https://doi.org/10.1007/s11119-024-10154-9","url":null,"abstract":"<p>Diversifying high-input, monocropped landscapes like the US Corn Belt would provide both economic and ecosystem service benefits to the agricultural landscape. Decision support systems (DSS) and digital agriculture could help farmers decide if diversification is suitable for their operation. However, adoption of DSS by farmers remains low, likely due to lack of farmer engagement before and during the DSS development process. This study aimed to better understand the tasks, tools, and people involved in implementing farmland diversification with the goal to inform design of agricultural DSS. Semi-structured interviews were conducted with 11 farmers who had diversified their corn/soybean cropland with government-supported conservation programs (e.g., CRP, wetlands) and alternative crops (e.g., small grains, pasture) in the past four years. Interview data was transcribed and then analyzed using affinity diagramming. Results show farmers needed DSS to layer multiple sources of data and observations over several years to identify field productivity trends and drivers; spatial orientation of practices to fit management and field constraints; matching operation goals to alternative practices; financial planning and market exploration; and information on promising emerging practices like subsidized pollinator habitat. However, the interviews also highlighted structural barriers to diversification that DSS cannot or can only partially address. These included social pressures; market access; crop insurance policy; and quality of relationships with governmental agencies. Results indicate better DSS design can empower individual farmers to diversify cropland, but structural interventions will be needed to successfully diversify the agricultural landscape and support economic and ecosystem health.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"15 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping varieties of farmers’ experience in the digital transformation: a new perspective on transformative dynamics","authors":"Valentin Knitsch, Lea Daniel, Juliane Welz","doi":"10.1007/s11119-024-10148-7","DOIUrl":"https://doi.org/10.1007/s11119-024-10148-7","url":null,"abstract":"<p>The COVID-19 pandemic has highlighted the vulnerabilities of the global food system, underscoring the need for a sustainable transformation of the food system. With the advent of new digital technologies emerging as critical tools for achieving the agricultural shift, it is important to understand farmers’ adoption decisions better. This study aims to systematically uncover and delineate the varied forms of experiences farmers have with new digital technologies and investigate how these experiences impact the organizational adoption decisions on the farm. In this study, twenty interviews with apple growers, wine makers, and intermediaries from a German region encompassing Saxony, Thuringia, and Saxony–Anhalt were conducted and analyzed. Through the lens of the modified adaptive capacity wheel and alongside the interview data, five relevant types of experiences were identified. These types of experiences are closely related to farmers’ adaptation motivation (AM) and adaptation belief (AB), potentially influencing their future decisions about the adoption of digital technologies. This study highlights the importance of creating meaningful experiences with technologies to strengthen farmers’ AM and AB.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"42 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141246366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leaf area index estimation in maize and soybean using UAV LiDAR data","authors":"Shezhou Luo, Weiwei Liu, Qian Ren, Hanquan Wei, Cheng Wang, Xiaohuan Xi, Sheng Nie, Dong Li, Dan Ma, Guoqing Zhou","doi":"10.1007/s11119-024-10146-9","DOIUrl":"https://doi.org/10.1007/s11119-024-10146-9","url":null,"abstract":"<p>Leaf area index (LAI) is a vital input variable for crop growth and yield prediction models. Therefore, rapid and accurate crop LAI estimates can offer important information for monitoring and managing the quantity and quality of food production. Here, LAI values of maize and soybean were predicted applying height metrics and intensity metrics calculated through unmanned aerial vehicle (UAV) LiDAR data. Moreover, we compared the prediction performance of physical model with that of empirical model for estimating crop LAI. The physical model based on Beer–Lambert law yielded reliable estimation results using LiDAR height data (maize: R² = 0.815, RMSE = 0.385; soybean: R² = 0.627, RMSE = 0.515) and LiDAR intensity data (maize: R² = 0.719, RMSE = 0.474; soybean: R² = 0.548, RMSE = 0.567). However, the linear regression model obtained a higher estimation accuracy. The single linear regression model derived from LiDAR height data had an R² value of 0.837 (RMSE = 0.361) for maize and 0.658 (RMSE = 0.493) for soybean, and derived from LiDAR intensity data had an R² value of 0.749 (RMSE = 0.448) for maize and 0.460 (RMSE = 0.619) for soybean, respectively. We found that the random forest (RF) regression model yielded the lowest estimation accuracy in this study. Moreover, the RF regression model in our study was not able to reliably estimate soybean LAI whether using LiDAR height metrics (R² = 0.294) or intensity metrics (R² = 0.180). Our results show that both LiDAR intensity and height metrics are capable of reliably predicting maize and soybean LAIs, although LiDAR intensity data yielded lower estimation accuracy than LiDAR height data. In conclusion, the results presented in this study demonstrate that using UAV-LiDAR technology to predict crop LAI is a flexible, practical, and cost-effective method.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"44 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141156722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}