Precision Agriculture最新文献

{"title":"Evaluating the utility of combining high resolution thermal, multispectral and 3D imagery from unmanned aerial vehicles to monitor water stress in vineyards","authors":"V. Burchard-Levine, J. G. Guerra, I. Borra-Serrano, H. Nieto, G. Mesías-Ruiz, J. Dorado, A. I. de Castro, M. Herrezuelo, B. Mary, E. P. Aguirre, J. M. Peña","doi":"10.1007/s11119-024-10179-0","DOIUrl":"https://doi.org/10.1007/s11119-024-10179-0","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>High resolution imagery from unmanned aerial vehicles (UAVs) has been established as an important source of information to perform precise irrigation practices, notably relevant for high value crops often present in semi-arid regions such as vineyards. Many studies have shown the utility of thermal infrared (TIR) sensors to estimate canopy temperature to inform on vine physiological status, while visible-near infrared (VNIR) imagery and 3D point clouds derived from red–green–blue (RGB) photogrammetry have also shown great promise to better monitor within-field canopy traits to support agronomic practices. Indeed, grapevines react to water stress through a series of physiological and growth responses, which may occur at different spatio-temporal scales. As such, this study aimed to evaluate the application of TIR, VNIR and RGB sensors onboard UAVs to track vine water stress over various phenological periods in an experimental vineyard imposed with three different irrigation regimes.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>A total of twelve UAV overpasses were performed in 2022 and 2023 where in situ physiological proxies, such as stomatal conductance (g_s), leaf (Ψ_leaf) and stem (Ψ_stem) water potential, and canopy traits, such as LAI, were collected during each UAV overpass. Linear and non-linear models were trained and evaluated against in-situ measurements.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Results revealed the importance of TIR variables to estimate physiological proxies (g_s, Ψ_leaf, Ψ_stem) while VNIR and 3D variables were critical to estimate LAI. Both VNIR and 3D variables were largely uncorrelated to water stress proxies and demonstrated less importance in the trained empirical models. However, models using all three variable types (TIR, VNIR, 3D) were consistently the most effective to track water stress, highlighting the advantage of combining vine characteristics related to physiology, structure and growth to monitor vegetation water status throughout the vine growth period.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>This study highlights the utility of combining such UAV-based variables to establish empirical models that correlated well with field-level water stress proxies, demonstrating large potential to support agronomic practices or even to be ingested in physically-based models to estimate vine water demand and transpiration.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"378 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021975","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":"A decision-supporting system for vineyard management: a multi-temporal approach with remote and proximal sensing","authors":"A. Deidda, A. Sassu, L. Mercenaro, G. Nieddu, C. Fadda, P. F. Deiana, F. Gambella","doi":"10.1007/s11119-024-10177-2","DOIUrl":"https://doi.org/10.1007/s11119-024-10177-2","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Site-specific field management operations represent one of the fundamental principles of precision viticulture. The purpose of the research is to observe and analyse the evolution of a vineyard over three consecutive years to understand which factors most significantly influence the quality of the vineyard’s production.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>The research involved technologically advanced tools for crop monitoring, such as remote and proximal sensors for vegetation surveys. In association, grape quality analyses were performed through laboratory analysis, constructing geostatistical interpolation maps and matrix correlation tables.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Both remote and proximal sensing instruments demonstrated their ability to effectively estimate the spatial distribution of vegetative and quality characteristics within the vineyard. Information obtained from GNDVI and CHM proved to be valuable and high-performance tools for assessing field variability. The differentiated plant management resulted in uniform production quality characteristics, a change evident through the monitoring techniques.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The research highlights the effectiveness of using advanced technological instruments for crop monitoring and their importance in achieving uniformity in production quality characteristics through differentiated plant management. From the results obtained, it was possible to observe how differentiated plant management led to a uniformity of production quality characteristics and how the monitoring techniques can observe their evolution. This result represents a positive accomplishment in field management during the three monitoring years, responding to the principles and objectives of precision agriculture.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"31 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013778","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":"Simulating within-field spatial and temporal corn yield response to nitrogen with APSIM model","authors":"Laura J. Thompson, Sotirios V. Archontoulis, Laila A. Puntel","doi":"10.1007/s11119-024-10178-1","DOIUrl":"https://doi.org/10.1007/s11119-024-10178-1","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Context</h3><p>Process-based crop growth models can explain soil and crop dynamics that influence the optimal N rate for crop production. Currently, there is a lack of understanding regarding the accuracy of process-based models for site-specific zones within fields, as well as the key factors that need to be considered when calibrating these models for zone-specific economic optimum N rate (EONR).</p><h3 data-test=\"abstract-sub-heading\">Objective</h3><p>We calibrated the Agricultural Production Systems sIMulator (APSIM) model in contrasting zones within fields, quantified the model performance, and used the calibrated model to develop long-term corn yield response to N to assess the temporal variability between zones and sites to assist decision making.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We conducted four N rate experiments (2 fields × 2 zones within a field) over two years in southeast Nebraska. Experimental data were used to calibrate and test the APSIM model. APSIM simulated corn yield response to N for each zone and site was obtained by running numerous iterations of the calibrated model at different N rates. Observed and simulated corn yield response to N rate were analyzed with statistical models to estimate the EONR.</p><h3 data-test=\"abstract-sub-heading\">Results and conclusions</h3><p>The APSIM model predicted corn yield over 11 historical years with a relative root mean square error (RRMSE) of 12% and yield at EONR in the N studies with RRMSE of 8.8%. The simulated EONR was lower than the observed EONR across sites, years, and zones with greater error than yield. The simulated yield increase with N fertilization was under-estimated in fine textured soils and over-estimated in medium textured soils. Long-term corn yield response to N showed that temporal variation in simulated EONR was greater than spatial variation. Long-term EONR and yield at EONR increased with increasing rainfall, while yield at zero N was greatest in normal years. Temporal variation was driven primarily by year-to-year variation in N loss (CV of 67% ± 9.5). Soil texture, hydrological properties, water table, and tile drainage were key variables for accurate site-specific model calibration. Improvements in simulating site-specific EONR may be realized by including in-situ or remotely sensed data for better estimation of N dynamics. We concluded that APSIM can provide valuable insights into systems dynamics in this region, but it can’t provide precise N-rate estimates. Our study contributes to understanding of the within-field variability using simulation modeling.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"35 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141980872","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":"Predicting on-farm soybean yield variability using texture measures on Sentinel-2 image","authors":"Rodrigo Greggio de Freitas, Henrique Oldoni, Lucas Fernando Joaquim, João Vítor Fiolo Pozzuto, Lucas Rios do Amaral","doi":"10.1007/s11119-024-10176-3","DOIUrl":"https://doi.org/10.1007/s11119-024-10176-3","url":null,"abstract":"<p>Yield forecasting and within-field yield variation is essential information that helps farmers develop sustainable agriculture. However, such information still needs to be included for most of them, and remote sensing is an alternative to provide it. Our objective was to assess Random Forest regression models composed of unique GLCM texture measures as an alternative to usual empirical models that use spectral response and auxiliary data, which is complex and reaches varied results. Eleven GLCM texture models based on eight texture measures of a single spectral layer were assessed to represent soybean field yield variation in two sites and seasons. Several models achieved satisfactory results, reaching R² from 0.90 to 0.95 and RMSE from 0.06 to 0.26 t/ha. Models above 15-window size are recommended for the soybean yield prediction as window size is an essential attribute to GLCM performance. Models derived from the bands individually (red, red-edge, near-infrared, and short wavelength infrared) were more sensitive to the window size than those derived from vegetation indices (EVI, GNDVI, GRNDVI, NDMI, NDRE, NDVI, SFDVI). The data aggregated by texture measures improve the individual spectral responses, providing alternatives to predict soybean within-field yield variation using random forest models.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"441 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918830","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":"Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies","authors":"Erekle Chakhvashvili, Miriam Machwitz, Michal Antala, Offer Rozenstein, Egor Prikaziuk, Martin Schlerf, Paul Naethe, Quanxing Wan, Jan Komárek, Tomáš Klouek, Sebastian Wieneke, Bastian Siegmann, Shawn Kefauver, Marlena Kycko, Hamadou Balde, Veronica Sobejano Paz, Jose A. Jimenez-Berni, Henning Buddenbaum, Lorenz Hänchen, Na Wang, Amit Weinman, Anshu Rastogi, Nitzan Malachy, Maria-Luisa Buchaillot, Juliane Bendig, Uwe Rascher","doi":"10.1007/s11119-024-10168-3","DOIUrl":"https://doi.org/10.1007/s11119-024-10168-3","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Introduction</h3><p>Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking.</p><h3 data-test=\"abstract-sub-heading\">Materials and methods</h3><p>This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"191 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915161","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 grapevine water status in a variably irrigated vineyard with NIR/SWIR hyperspectral imaging from UAV","authors":"E. Laroche-Pinel, K. R. Vasquez, L. Brillante","doi":"10.1007/s11119-024-10170-9","DOIUrl":"https://doi.org/10.1007/s11119-024-10170-9","url":null,"abstract":"<p>Remote sensing is now a valued solution for more accurately budgeting water supply by identifying spectral and spatial information. A study was put in place in a <i>Vitis vinifera</i> L. cv. Cabernet-Sauvignon vineyard in the San Joaquin Valley, CA, USA, where a variable rate automated irrigation system was installed to irrigate vines with twelve different water regimes in four randomized replicates, totaling 48 experimental zones. The purpose of this experimental design was to create variability in grapevine water status, in order to produce a robust dataset for modeling purposes. Throughout the growing season, spectral data within these zones was gathered using a Near InfraRed (NIR) - Short Wavelength Infrared (SWIR) hyperspectral camera (900 to 1700 nm) mounted on an Unmanned Aircraft Vehicle (UAV). Given the high water-absorption in this spectral domain, this sensor was deployed to assess grapevine stem water potential, Ψ_stem, a standard reference for water status assessment in plants, from pure grapevine pixels in hyperspectral images. The Ψ_stem was acquired simultaneously in the field from bunch closure to harvest and modeled via machine-learning methods using the remotely sensed NIR-SWIR data as predictors in regression and classification modes (classes consisted of physiologically different water stress levels). Hyperspectral images were converted to bottom of atmosphere reflectance using standard panels on the ground and through the Quick Atmospheric Correction Method (QUAC) and the results were compared. The best models used data obtained with standard panels on the ground and allowed predicting Ψ_stem values with an R² of 0.54 and an RMSE of 0.11 MPa as estimated in cross-validation, and the best classification reached an accuracy of 74%. This project aims to develop new methods for precisely monitoring and managing irrigation in vineyards while providing useful information about plant physiology response to deficit irrigation.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"367 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141895685","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":"On-farm evaluation of a crop forecast-based approach for season-specific nitrogen application in winter wheat","authors":"Palka M., Manschadi A.M.","doi":"10.1007/s11119-024-10175-4","DOIUrl":"https://doi.org/10.1007/s11119-024-10175-4","url":null,"abstract":"<p>Inadequate nitrogen (N)-fertilisation practices, that fail to consider seasonally variable weather conditions and their impacts on crop yield potential and N-requirements, cause reduced crop N-use efficiency. As a result, both the ecological and economic sustainability of crop production systems are put at risk. The aim of this study was to develop a season-specific crop forecasting approach that allows for a targeted application of N in winter wheat while maintaining farm revenue compared to empirical N-fertilisation practices. The crop forecasts of this study were generated using the process-based crop model SSM in combination with state-of-the-art seasonal ensemble weather forecasts (SEAS5) for the case study region of Eastern Austria. Results from three winter wheat on-farm experiments showed a significant reduction in applied N when implementing a crop forecast-based N-application approach (-43.33 kgN ha^-1, -23.42%) compared to empirical N-application approaches, without compromising revenue from high-quality grain sales. The benefit of this reduced N-application approach was quantified through the economic return to applied N (ERAN). While maintaining revenue, the lower amounts of applied N led to significant benefits of + 30.22% (+ 2.20 € kgN^-1) in ERAN.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"52 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880332","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":"Detection and localization of citrus picking points based on binocular vision","authors":"Chaojun Hou, Jialiang Xu, Yu Tang, Jiajun Zhuang, Zhiping Tan, Weilin Chen, Sheng Wei, Huasheng Huang, Mingwei Fang","doi":"10.1007/s11119-024-10169-2","DOIUrl":"https://doi.org/10.1007/s11119-024-10169-2","url":null,"abstract":"<p>Accurate localization of picking points in non-structural environments is crucial for intelligent picking of ripe citrus with a harvesting robot. However, citrus pedicels are too small and resemble other background objects in color, making it challenging to detect and localize the picking point of citrus fruits. This work presents a novel approach for detecting and localizing citrus picking points using binocular vision. First, the convolutional block attention module (CBAM) attention model is integrated into the backbone network of Mask R-CNN to increase the feature extraction for citrus pedicels, and the soft-non maximum suppression (Soft-NMS) strategy is used in the region proposal network to enhance the detection performance of citrus pedicel. Second, to accurately associate the citrus fruit with the best detected pedicel, a maximum discrimination criterion is proposed by integrating the confidence score of the detected pedicel and the degree of positional connectivity between the pedicel and the fruit. Finally, to reduce matching errors and improve computational efficiency, a rapid and robust matching method based on the normalized cross-correlation was applied to search the picking point within the line segment between the left and right images. The experimental results show that the precision, recall and F1-score for pedicel detection are 95.04%, 88.11%, and 91.44%, respectively, which are improvement of 13.00%, 7.84%, and 10.30% compared to the original Mask R-CNN. The mean absolute error (MAE) for the localizing the citrus picking point is 8.63 mm and the mean relative error (MRE) is 2.76%. The MRE was significantly reduced by at least 1.2% compared to the stereo matching methods belief-propagation (BP), semi-global block matching (SGBM), and block matching (BM), respectively. This study provides an effective method for the precise detection and localization of citrus picking point for a harvesting robot.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"6 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769064","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":"Remote sensing imagery to predict soybean yield: a case study of vegetation indices contribution","authors":"Lucas R. Amaral, Henrique Oldoni, Gustavo M. M. Baptista, Gustavo H. S. Ferreira, Rodrigo G. Freitas, Cenneya L. Martins, Isabella A. Cunha, Adão F. Santos","doi":"10.1007/s11119-024-10174-5","DOIUrl":"https://doi.org/10.1007/s11119-024-10174-5","url":null,"abstract":"<p>Mapping the spatial variability of crops is critical for precision agriculture. In this sense, remote sensing is a key technology generally dependent on the result of vegetation indices (VIs). Therefore, investigating the sensitivity of VIs and their contribution toward explaining crop variability and assisting in predicting yield is one of the pathways scientific research needs to explore. In this study, we evaluated 12 VIs with different acquisition principles in four soybean-producing fields. Using these VIs proved to be interesting to increase the performance of yield prediction models using the Randon Forest algorithm. However, simply adding VIs to the model is not enough; these VIs must aggregate information on crop variability. Some VIs are calculated based on the variation of the scene under study, and these can be an interesting option to complement the information provided by more traditional VIs, such as NDVI, assisting in predictive models, even if their direct correlation with crop yield is low in some situations. We found that using VIs groups with the same acquisition principle in isolation did not allow reaching performance of models that contained more than one principle simultaneously. In this study, the CI and TC2 indices stood out. Thus, associating VIs with different acquisition principles and, consequently, capturing different responses to variability in vegetation vigor and canopy structure is more important than the number of predictor variables itself.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"66 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769070","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":"From pen and paper to digital precision: a comprehensive review of on-farm recordkeeping","authors":"Md. Samiul Basir, Dennis Buckmaster, Ankita Raturi, Yaguang Zhang","doi":"10.1007/s11119-024-10172-7","DOIUrl":"https://doi.org/10.1007/s11119-024-10172-7","url":null,"abstract":"<p>In the present era of agricultural digitalization, documenting on-farm operations is critical. These records contextualize other layers of data and underpin economic analysis and informed decision-making. On-farm recordkeeping is rooted in an ancient tradition and has evolved from pen and paper to digital means integrating diverse tools and methods. These tools vary widely in mode of data recording and this presents challenges in achieving complete, accurate and interoperable data. Assessing this diversity of existing recordkeeping systems is a key step toward the improvement in recordkeeping systems that enhance data quality and interoperability. Despite the importance, as of present, comprehensive studies addressing this challenge are lacking. A systematic review of existing on-farm recordkeeping systems was carried out to address their advantages and weaknesses and to analyze their features and traits, focusing on interoperability and adherence to efficient and comprehensive on-farm recordkeeping. Paper-based recordkeeping, a longstanding and reliable method, is gradually being replaced by digital platforms. Many universities and agencies have released farm management spreadsheets and interactive database forms representing the initial step toward intuitive recordkeeping. Furthermore, farm management software, web apps, and user-friendly smartphone apps are increasingly crucial for handling agricultural big data. Notably, among the surveyed software packages and apps, most of them are not free and only a few support data interoperability. The survey also indicates a scope for further development in open-source tools with automation in recordkeeping. Adopting digital on-farm recordkeeping tools can positively impact both on and off the farm, fostering data interoperability, controlled yet flexible data access, completeness, and appropriate accuracy.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"164 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141755444","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}