Precision Agriculture最新文献

{"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}

{"title":"Laser and optical radiation weed control: a critical review","authors":"Hongbo Zhang, Deng Cao, Wenjing Zhou, Ken Currie","doi":"10.1007/s11119-024-10152-x","DOIUrl":"https://doi.org/10.1007/s11119-024-10152-x","url":null,"abstract":"<p>The success of weed control is critical for our food security. Non-chemical weed control is a promising technique in sustainable agriculture to ensure the food security. In this review, multiple directed energy weed control methods are reviewed with a specific focus on laser and optical radiation weed control. The mechanisms of the weed control in terms of adverse ablation, radiation thermal effects, and molecular-level damages are systematically reviewed. In particular, the underlying mathematical models determining the dose and response relationship of the weed control are also analyzed for a rigorous study of the physical law of the control process. Challenges of applying the techniques into practice are also illustrated to guide practical weed control applications.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"57 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141156701","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":"Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought","authors":"Yi Luo, Huijing Wang, Junjun Cao, Jinxiao Li, Qun Tian, Guoyong Leng, Dev Niyogi","doi":"10.1007/s11119-024-10149-6","DOIUrl":"https://doi.org/10.1007/s11119-024-10149-6","url":null,"abstract":"<p>Effective yield forecasting is a key strategy for adaptation when facing food loss to climate variability. Currently, solar-induced chlorophyll fluorescence (SIF) is an emerging remote-sensing index owing to its high relevance to plant photosynthesis, and sensitivity to drought. Despite many studies have focused on drought monitoring and production assessment by SIF, little puts it into practice for in-season yield prediction. In this study, we combined multi-source satellite and meteorological data, especially coupling with subseasonal-to-seasonal (S2S) dynamic atmospheric prediction climate model (IAP-CAS FGOALS-f2), with an addition of SIF, to predict maize yields in the U.S. Corn Belt, based on the developed machine learning dynamical hybrid model (MHCF). By comparison, we found that SIF performed well in the correlation analysis with yield, with average correlations up to 0.719 in August. Then we utilized different algorithms, different models (S2S data for MHCF, climate data for the Benchmark), and different input combinations to train and predict maize yields. All four algorithms using SIF significantly improved prediction performance. S2S + VIs + SIF combination (FGOALS-f2、NDVI、EVI、SIF) can achieve the best performance, while the XGBoost algorithm reached 0.897 of R². With the best combination, it can achieve 4 months before maize harvest (with R² value of 0.85, and RMSE &lt; 13 bu/acre). In 2012, the year had a severe drought, although predictive capability decreased in all the predictions, the models with SIF still maintained robust and improved the prediction (improved R² by 5.92%, and RMSE decreased by 18.08% of XGBoost). According to the study, it can be expected, the combination of MHCF and SIF will play a greater role in subseasonal yield prediction. We also provide an operational proposition of hybrid yield forecasting method to fully integrating climate prediction and machine learning for early notice of crop production losses.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"3 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140954228","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":"Effects of pre-emergence herbicide on targeted post-emergence herbicide application in plasticulture production","authors":"Ana C. Buzanini, Arnold Schumann, Nathan S. Boyd","doi":"10.1007/s11119-024-10150-z","DOIUrl":"https://doi.org/10.1007/s11119-024-10150-z","url":null,"abstract":"<p>Smart spray technology developed at the University of Florida was designed to reduce off-target applications when applying postemergence (POST) herbicides for weed control in plasticulture systems. A trial was conducted in the fall of 2021 and spring of 2022 to evaluate smart spray technology in row middles in a banana pepper field at the Gulf Coast Research and Education Center in Balm, FL. The objective of this study was to evaluate the efficacy of targeted POST-herbicide applications in plasticulture pepper row middles in the presence or absence of a pre-emergent (PRE) herbicide. Flumioxazin reduced broadleaf and overall weed densities in both seasons and lowered grass density in the spring. Two targeted applications reduced the nutsedge density in spring compared to the two banded applications. No significant pepper damage was observed in any treatments. Applied POST herbicide volume following PRE-herbicide was reduced by 84% and 54% for fall and spring respectively. In the absence of a PRE herbicide, targeted applications reduced POST-herbicide volumes by 30% and 45% for fall and spring respectively. No reduction in weed control or pepper yield was observed when comparing targeted with banded applications. Overall, the use of smart spray technology for POST herbicides in row middles reduced applied spray volume with no reduction in weed control, significant injuries on pepper, or negative effects on yield.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"59 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140953616","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":"Quantifying real-time opening disk load during planting operations to assess compaction and potential for planter control","authors":"Sylvester A. Badua, Ajay Sharda, Bhaskar Aryal","doi":"10.1007/s11119-024-10151-y","DOIUrl":"https://doi.org/10.1007/s11119-024-10151-y","url":null,"abstract":"<p>Uniform plant spacing, seeding depth, and emergence are important factors heavily influenced by both machine settings and soil conditions. Understanding load distribution across the planter toolbar at varying planter settings and soil conditions provide feedback to improve planter performance and achieve desired seed placement consistency. One important soil property that affects opening disc load requirement in creating seed trench is soil texture which relates to soil strength. However, none of the existing methods (soil apparent electrical conductivity (ECa) maps, historic soil maps, and cone penetrometer) provide accurate soil strength data on a high spatial resolution which could be used to optimize planter performance. This study was conducted to (1) quantify the percentage of time row-planters need uplift during planting and (2) quantify opening disc loads using real-time machine control system recorded data across different ECa zones. Results showed that uplift events varied from 13 to 18% with wing and track sections revealed higher instances of uplift. Higher instances of uplift were observed on the non-track section for planter with wing wheels. Results revealed a modest correlation between soil ECa and opening disc load with 435 N more or 12% higher opening disc load applied on high soil ECa zones as compared in low soil ECa zones.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"48 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140949387","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}