Precision Agriculture最新文献

{"title":"Monitoring the interannual dynamic changes of soil organic matter using long-term Landsat images","authors":"Chang Liu, Qian Sun, Chi Zhang, Wentao Chen, Xuzhou Qu, Boyi Tang, Kai Ma, Xiaohe Gu","doi":"10.1007/s11119-025-10245-1","DOIUrl":"https://doi.org/10.1007/s11119-025-10245-1","url":null,"abstract":"<p>Current approaches for monitoring soil organic matter (SOM) exhibit limitations in long-term predictive accuracy and data efficiency. This study aims to develop a remote sensing framework that integrating Landsat imagery and three modeling algorithms (PLSR, RF, Cubist) to address these challenges, reduce sampling workload, and enable large scale soil fertility assessments. Feature selection via Boruta and recursive feature elimination (RFE) was applied to optimize model performance, with PLSR identified astheoptimal algorithm. The framework utilized long-term Landsat imagery (2007–2021) and an inter-annual migration learning approach to map SOM dynamics. PLSR achieved cross-year SOM prediction (R² = 0.51, RMSE = 3.97 g/kg), enabling accurate mapping of non-sample years with minimal field data and long-term imagery. Analysis of SOM trends revealed a decade-long decline in the study area, strongly correlated with land-use intensity. The proposed inter-annual migration learning method demonstrates that SOM dynamics can be efficiently tracked using sparse sampling and time-series remote sensing, offering a scalable tool for soil fertility management and precision agriculture.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"30 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066944","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":"Improving the performance of plant nitrogen assessment in drip-irrigated potatoes using optimized spectral indices-based machine learning","authors":"Haibo Yang, Fei Li, Yuncai Hu, Kang Yu","doi":"10.1007/s11119-025-10248-y","DOIUrl":"https://doi.org/10.1007/s11119-025-10248-y","url":null,"abstract":"<p>Timely and accurate monitoring of plant nitrogen concentration (PNC) is vital for optimizing field N management. Hyperspectral indices are commonly used as a predictor for monitoring the PNC of crops, but individual spectral indices are often susceptible to cultivars and growth stages. Machine learning (ML) is a promising method for mining more spectral variables to assess the PNC of crops. To monitor the PNC of potatoes, therefore, this study extended previous work to further use hyperspectral optimized spectral indices (OSI) as input variables of ML, while, comparing with the ML models that used full-spectrum (FS), existing spectral indices (ESI) and sensitive spectral bands (SSB) as input variables, as well as simple regression model based on OSI alone. The partial least squares regression (PLSR), random forest (RF), support vector regression (SVR), and artificial neural network (ANN) models were calibrated using a dataset encompassing three cultivars and critical fertigation growth stages under three to six N levels. The calibrated ML models were evaluated using the datasets from independent experiments and two farmers´ fields. The OSI as an input variable in ML models showed superiority for predicting the potato PNC compared to FS, SSB, and ESI. The OSI-based RF model with an R² of 0.79, RMSE of 0.27%, and RPD of 2.18 had higher accuracy for predicting potato PNC than other ML models. Comparing the simple optimized spectral indices regression model alone, the OSI-based RF model reduced RMSE by mitigating the effects of cultivars and growth stages on PNC prediction. The OSI-based RF model significantly contributes to optimum fertilization management based on actual potato N status during critical growth periods.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"54 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066943","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":"Possibilities of using digital technologies in agriculture in areas with high agrarian fragmentation","authors":"Paulina Kramarz, Henryk Runowski","doi":"10.1007/s11119-025-10244-2","DOIUrl":"https://doi.org/10.1007/s11119-025-10244-2","url":null,"abstract":"<p>The Małopolskie and Podkarpackie provinces in Poland are characterized by many small farms with many small, scattered fields. This farm structure is labeled “agrarian fragmentation”. Using digital technologies in such small farm areas is usually a challenge. However, there are several digital technologies that, with minimal financial investment, can yield results in the form of improved resource management and agricultural production processes, as well as data-driven decision-making. The overall objective of this analysis is to determine the limitations of using digital technologies in farms operating in areas with high agrarian fragmentation. In addition, the aim was also to identify the differences in the potential for implementing individual digital solutions depending on farm size and activity type conducted in the surveyed area. A survey was conducted by the Paper and Pen Personal Interview (PAPI) method, in which 389 farmers took part. Research showed that the technologies most commonly used in the study area include applications recognizing plant diseases and applications supporting decision-making. The use of advanced digital tools related to precision agriculture and the automation of crop production was very rare. Farm size, the age of the farmer managing the farm, and the number of farm activities were significant factors that increased the probability of implementing digital technologies. The main barriers to their implementation were a lack of sufficient knowledge and trust. The implementation of digital technologies in small farms requires actions aimed at increasing farmer knowledge. Meanwhile, designing new digital solutions must take the specific regional conditions into account, such as geographical factors or the limited investment capacity of farms.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"24 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898303","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":"UAV-based multispectral and thermal indexes for estimating crop water status and yield on super-high-density olive orchards under deficit irrigation conditions","authors":"J. M. Ramírez-Cuesta, M. A. Martínez-Gimeno, E. Badal, M. Tasa, L. Bonet, J. G. Pérez-Pérez","doi":"10.1007/s11119-025-10240-6","DOIUrl":"https://doi.org/10.1007/s11119-025-10240-6","url":null,"abstract":"<p>Efficient water management is critical for sustainable agriculture in Mediterranean climates, particularly in super-high-density (SHD) olive orchards where water scarcity poses significant challenges. This study assessed the potential of UAV-based thermal and multispectral imagery to monitor crop water status and predict yield under different regulated deficit irrigation (RDI) strategies. Conducted over two seasons (2018–2019) in a commercial SHD olive orchard (<i>Olea europaea</i> L., cv. ‘Arbequina’) in Villena, Spain, the experiment involved four irrigation treatments ranging from full irrigation (FI) to progressively restricted RDIs. UAV flights captured thermal infrared and multispectral imagery at key phenological stages, to calculate Crop Water Stress Index (CWSI) and Normalized Difference Vegetation Index (NDVI), which were validated against plant-based measurements of stem water potential (Ψ_stem). The results demonstrated that thermal parameters, including canopy temperature and CWSI, effectively identified water stress levels, although their sensitivity was influenced by environmental conditions and sensor limitations. NDVI proved to be a reliable indicator of vegetative growth and yield, with values closely linked to irrigation levels and fruit load. The approach incorporating both canopy and soil reflectance (NDVI_crop+ground) provided the most accurate assessment of crop performance. These findings highlight the value of UAV-based remote sensing technologies for optimizing irrigation management in SHD olive orchards, particularly under deficit irrigation regimes. However, further advancements in sensor accuracy and index normalization are recommended to enhance their applicability and precision in agricultural practices.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"17 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875947","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":"Navigation line detection algorithm for corn spraying robot based on improved LT-YOLOv10s","authors":"Zhihua Diao, Shushuai Ma, Jiangbo Li, Jingcheng Zhang, Xingyi Li, Suna Zhao, Yan He, Baohua Zhang, Liying Jiang","doi":"10.1007/s11119-025-10243-3","DOIUrl":"https://doi.org/10.1007/s11119-025-10243-3","url":null,"abstract":"<p>The deep integration of artificial intelligence technology and agriculture has significantly propelled the rapid development of smart agriculture. However, the field still faces numerous challenges, including high algorithm complexity and limited detection speed in farmland environments. To address the challenges encountered by corn spraying robots in navigating and identifying lines, we have proposed a corn crop row navigation line recognition algorithm based on the LT-YOLOv10s model. By introducing lightweight network models (GhosNet), efficient feature pyramid models (SPPFA), and efficient feature attention modules (PSCA) into the YOLOv10s network, we have reduced the complexity of the model and significantly enhanced the detection efficiency of corn plants. Then, the algorithm precisely locates corn plants using the center points of detection boxes and accurately fits crop rows using the least squares method. Finally, the navigation lines centered on the corn crop rows are determined through the adjacent centerline method. Experimental data significantly demonstrates that the comprehensive performance of the LT-YOLOv10s model surpasses industry benchmark models such as YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and the traditional YOLOv10s. The proposed algorithm for extracting the center navigation line of corn crop rows boasts an average fitting time of just 26ms with an accuracy rate of up to 93.8%, ensuring precision and reliability in navigation line extraction. This provides robust technical support for precise navigation of corn-spraying robots.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"32 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867026","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":"Assessing benefits of two sensing approaches for variable rate nitrogen fertilization in wheat","authors":"Rukayat Afolake Oladipupo, Ajit Borundia, Abdul Mounem Mouazen","doi":"10.1007/s11119-025-10241-5","DOIUrl":"https://doi.org/10.1007/s11119-025-10241-5","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>In contemporary agriculture, achieving sustainable food production while preserving the environment is crucial. Traditional uniform rate nitrogen fertilization (URNF) often leads to over- or under-applications of N in fields with negative economic, agronomic and environmental issues. Variable rate nitrogen fertilization (VRNF) has shown promise in optimizing N application by accounting for soil and crop variability, thus improving nitrogen use efficiency and reducing environmental impact. This study evaluates and compares two VRNF solutions in two wheat fields in Belgium and France.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>The first, VRNF1 relied on onsite measurement of soil nitrate using ion-selective electrode (ISE) sensors, whereas the second, VRNF2, utilizes the fusion of on-line measured key soil properties using a visible and near-infrared spectrometer (vis-NIRS) and crop normalized difference vegetation index (NDVI). In VRNF1, soil nitrate values were used to rank the fertility level of management zones (MZs), delineated by the clustering analysis of vis-NIRS-NDVI data (like for VRNF2), with N fertilization rates adjusted by 30–50%, applying lower rates to high-fertility zones and higher rates to low-fertility zones. In VRNF2, after the fertility level of MZ was ranked by examining the on-line measurements of pH, organic carbon (OC), moisture content (MC), potassium (K), phosphorus (P), and calcium (Ca), and crop NDVI, N fertilizer rates were adjusted similarly to VRNF1.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>A cost-benefit analysis revealed that the gross margin of both VRNF solutions was larger than that of the URNF, with VRNF1 providing up to 289 EUR ha⁻¹ and VRNF2 up to 358 EUR ha⁻¹ more gross margin than URNF. VRNF1 increased crop yield by up to 8%, while VRNF2 resulted in a 9.2% yield increase compared to URNF. However, VRNF1 achieved a slight economic advantage (14 EUR ha⁻¹) in one field, while VRNF2 was more profitable in the other field by 69 EUR ha⁻¹. Additionally, VRNF2 demonstrated superior environmental benefits, using 14% less fertilizer than URNF and 12% less than VRNF1.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Overall, VRNF2 offered better economic and environmental outcomes than VRNF1 and URNF. However, the subjectivity of ranking MZs into different fertility levels in the absence of historical yield data for the VRNF2 raises concerns, calling in such a situation for VRNF1 to be adopted in future VRNF schemes.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"8 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857205","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":"Integrating UAV-based multispectral and thermal infrared imageries with machine learning for predicting water stress in winter wheat","authors":"Santosh S. Mali, Michael Scobie, Justine Baillie, Corey Plant, Sayma Shammi, Anup Das","doi":"10.1007/s11119-025-10239-z","DOIUrl":"https://doi.org/10.1007/s11119-025-10239-z","url":null,"abstract":"<p>Assessing spatial and temporal variations in crop water stress is vital for precision irrigation. This study utilized Unmanned Aerial Vehicles (UAVs) equipped with multispectral (MSS) and thermal band (TB) sensors to map the crop water stress index (CWSI) in wheat. A water deficit experiment was conducted on winter wheat under varying irrigation levels during late vegetative, reproductive, and maturation stages. CWSI was calculated using canopy temperature, ambient air temperature, and vapor pressure deficit (VPD). Six machine learning (ML) models—linear model (LM), random forest (RF), decision tree (DT), support vector machine (SVM), extreme gradient boosting (XGB), and artificial neural network (ANN)—were developed for pre-heading, post-heading, and seasonal datasets. The top five vegetation indices (VIs), selected using Recursive Feature Elimination (RFE), along with thermal data, were used as inputs to the ML models. Results showed that seasonal ML models outperformed those based only on pre-heading or post-heading data. Particularly, the RF model performed well, with respective R² and RMSE values of 0.87 and 0.09 for seasonal, 0.82 and 0.05 for pre-heading, and 0.93 and 0.06 for post-heading datasets. SHapley Additive exPlanations (SHAP) analysis identified Red Normalized Value (RNV), TB, and Green Red Vegetation Index (GRVI) as key predictors of CWSI in the RF model. CWSI maps effectively captured spatial variations in water stress, aligning with irrigation management practices. This study demonstrates the effectiveness of combining UAV remote sensing and ML for precision irrigation management.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"26 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143827706","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":"Application of artificial intelligence for identification of peanut maturity using climatic variables and vegetation indices","authors":"Thiago Orlando Costa Barboza, Jarlyson Brunno Costa Souza, Marcelo Araújo Junqueira Ferraz, Samira Luns Hatum de Almeida, Cristiane Pilon, George Vellidis, Rouverson Pereira da Silva, Adão Felipe dos Santos","doi":"10.1007/s11119-025-10237-1","DOIUrl":"https://doi.org/10.1007/s11119-025-10237-1","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p> The hull scrape and vegetation indices are widely used for predicting peanut maturation, but they are time-consuming, subjective, labor-intensive, and fail to account for climate variables, reducing their accuracy.Thus, the objective was to verify the potential of using artificial intelligence associating IV and climate variables to predict the variability of peanut pod maturity in the field</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p> For this purpose, peanut maturity data collected on different dates in commercial fields in Brazil and the United States. In addition, high-resolution satellite images were used to calculate nine IV and four climatic variables for each area were acquired using the NASA-POWER platform. Four machine learning models were tested and the input for the training were selected using the Random Forest feature selection. Thus, the models were trained using 70% of the data for training and 30% for testing and applied the cross validation with K-fold.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The best results were obtained for the XGBoosting model with R² test values varying 0.90, 0.89, 0.93 and 0.87 and a minimum MAE and RMSE of 0.05. Except for the Georgia dataset where the MLP model presents the highest performance R² value of 0.93, MAE 0.05 and RMSE 0.06 for the test. The RBF models present the worst results with a low index of agreement (d) 0.4 for all the datasets demonstrating a low agreement between the predicted and observed values.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p> Combining the climatic variables was able to improve the model’s performance, however detailed information about the field such as topographic conditions and soil type seem to be a different approach to enhance the model performance. Using the calibrated model for overall dataset peanut farmers from any localities can use to monitor and map the PMI variability in the fields, improve the decision-making, decrease the loss and increase the kernels quality.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"8 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775693","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":"Shared digital agricultural technology on farms in Southern Germany-analysing farm and socio-demographic characteristics in an inter-farm context","authors":"Michael Gscheidle, Thies Petersen, Reiner Doluschitz","doi":"10.1007/s11119-025-10235-3","DOIUrl":"https://doi.org/10.1007/s11119-025-10235-3","url":null,"abstract":"&lt;h3 data-test=\"abstract-sub-heading\"&gt;Introduction&lt;/h3&gt;&lt;p&gt;Up till now, digitalisation in agriculture has almost only been discussed in the context of large farms. However, sooner or later, ongoing digitalisation will reach the agricultural sector as a whole. Indeed, even smaller farms can also benefit from the opportunity and make profitable use of digital agricultural technology by adopting inter-farm organisational forms e.g. collaboration between farmers or contractor services. This article seeks to gain a better understanding of the digital transformation process and to validate relevant forecasts by analysing farm and socio-demographic characteristics that have a possible influence on the likelihood of inter-farm use of digital agricultural technology in general and regardless of the organisational form.&lt;/p&gt;&lt;h3 data-test=\"abstract-sub-heading\"&gt;Methodological approach&lt;/h3&gt;&lt;p&gt;Univariate analysis approaches and bivariate analysis approaches were selected to describe the sample. A binary regression analysis was used to analyse the results of a written online survey of farmers from southern Germany. The characteristics listed in hypotheses H1 to H10 serve as a theory-based conceptual framework for the statistical analysis within the binary logistic regression model.&lt;/p&gt;&lt;h3 data-test=\"abstract-sub-heading\"&gt;Results&lt;/h3&gt;&lt;p&gt;The results of this study are based on a survey sample of 165 farmers, 36.4 % (n=60) of whom use digital agricultural technology on an inter-farm basis. The sample covers n=89 farms from Baden-Württemberg and n=76 from Bavaria. Most of the farmers (87.3 %) considered themselves perfectly capable of using digital technologies confidently after it had been explained to them once (x̅=2.52, s=1.02, scale: 1=completely true to 6=not true at all), with 38.2 % of them using digital agricultural technology across farms, that means they use digital agricultural technology together. Certain factors which can influence the likelihood of inter-farm use of digital agricultural technology in small-scale regions were identified using the binary logistic regression model to analyse the relevant operational and socio-demographic characteristics. Using this methodological approach, eight predictors were identified, three of which have a positive influence on the likelihood of inter-farm use of digital agricultural technology: the availability of two external labourers, the farm's focus on “finishing” or on “other” activities such as taking horses at livery or fattening livestock. Farms that have less than 200 hectares, have no clear succession plan, or whose farm managers are under 30 years old are less likely to use inter-farm digital agricultural technology.&lt;/p&gt;&lt;h3 data-test=\"abstract-sub-heading\"&gt;Conclusions&lt;/h3&gt;&lt;p&gt;In this study, several influencing factors were identified that can play a role in the shared use of digital agricultural technology, especially between farmers in small-scale regions in southern Germany. The empirical results obtained","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"4 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733988","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":"Unleashing profitability of vineyards through the adoption of unmanned aerial vehicles technology systems: the case of two Italian wineries","authors":"Serena Sofia, Martina Agosta, Antonio Asciuto, Maria Crescimanno, Antonino Galati","doi":"10.1007/s11119-025-10236-2","DOIUrl":"https://doi.org/10.1007/s11119-025-10236-2","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Precision agriculture technologies play an important role in optimising practices to increase yields and reduce costs, contributing to socio-economic progress and environmental well-being, and playing a key role in addressing climate change. Viticulture is a strategic, input-intensive agricultural sector where precision technologies can make the use of resources more efficient without compromising profitability. The aim of this study is to evaluate the profitability of implementing precision farming systems, such as unmanned aerial vehicle surveying for the production of vigour maps, compared to the conventional cultivation system in two Italian wineries.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>The profitability of using precision farming tools in viticulture compared to conventional management techniques has been investigated in two Italian wineries over a four-year period, before and after the introduction of UAV technology.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The results demonstrate the usefulness and economic viability of precision agriculture technologies in viticulture. The vigour maps produced by the data collected with UAV technology allow both the identification of problems such as diseases, and consequently the planning of phytosanitary treatments, and selective grape harvesting, which allows a significant improvement in the quality of the harvested grapes.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The results demonstrate the usefulness of precision technologies for cost-effective and sustainable vineyard management, satisfying a market segment made up of stakeholders who are increasingly sensitive to environmental issues.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"41 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723982","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}