Precision Agriculture最新文献

{"title":"Maize seedling center detection based on instance segmentation and skeleton-driven geometric morphological analysis","authors":"Jiahang Pan, Xingyu Ban, Yuanzhi Xu, Qingzhen Zhu, Liyuan Zhang, Jizhan Liu, Liang Cao, Aichen Wang","doi":"10.1007/s11119-026-10438-2","DOIUrl":"https://doi.org/10.1007/s11119-026-10438-2","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"18 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842848","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":"Corn stalk diameter estimation using deep learning","authors":"Nathan Sprague, John Evans, Michael Mardikes","doi":"10.1007/s11119-026-10430-w","DOIUrl":"https://doi.org/10.1007/s11119-026-10430-w","url":null,"abstract":"Purpose Accurate measurement of corn stalk diameter is important for assessing plant robustness, lodging resistance, and harvest performance, but automated measurement across changing crop conditions remains challenging. This study evaluated whether a ground-based stereo-vision system could reliably estimate stalk diameter throughout the growing season, from mid-summer through senescence. Methods A stereo-vision pipeline using dual AR0234 global-shutter cameras was deployed on an autonomous ground robot. YOLOv8 provided stalk localization and pose correction, BoT-SORT enabled multi-frame tracking, and U-Net segmentation with an edge-mask strategy extracted stalk boundaries under partial occlusion. Repeated per-frame width estimates were filtered and converted to physical dimensions using disparity-based calibration. Vision estimates were compared with perpendicular caliper measurements. Results Under mid-summer conditions with minimal leaf interference, the system achieved a mean absolute error (MAE) of 1.1–1.5 mm and r² of 0.90. During late-season senescence, leaf-sheath expansion and occlusion caused systematic diameter overestimation and reduced accuracy. Applying a seasonally derived offset of approximately 3.8 mm reduced MAE to approximately 1.3 mm, although correlation remained modest (r² ≈ 0.41). Independent human measurements also exhibited variability, with inter-rater r² ≈ 0.77 and mean disagreement of approximately 1.0 mm. Conclusion Stereo vision can provide accurate, non-contact corn stalk diameter measurements under favorable canopy conditions and remains viable across the crop lifecycle. However, robust late-season phenotyping will require improved modeling of leaf sheaths and occlusions together with more reliable ground-truth measurement procedures.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"17 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842873","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 rice genotype dependency in remote sensing: challenges in nitrogen discrimination and yield forecasting","authors":"Fàtima Della-Bellver, Belen Franch, Javier Tarin-Mestre, Cesar José Guerrero-Benavent, Concha Domingo, Mar Català Forner, Karen Marti-Jerez, Luis Marqués","doi":"10.1007/s11119-026-10432-8","DOIUrl":"https://doi.org/10.1007/s11119-026-10432-8","url":null,"abstract":"Background Rice ( <jats:italic>Oryza sativa</jats:italic> L.) is a staple crop that accounts for 8% of the global primary crop production. This sector faces an environmental challenge driven by climate change, rising water scarcity, and the need for more resilient and adaptive production systems. Although Remote Sensing (RS) offers solutions for optimizing inputs, most current models are calibrated for specific varieties, limiting their application across diverse cultivars. This study evaluates the transferability of RS models for monitoring nitrogen (N) fertilization status and predicting yield across a highly heterogeneous dataset of rice genotypes, locations and seasons. Materials and methods Six field trials were conducted across three locations in Spain (Valencia and Tarragona) during seasons 2022 and 2023. The study analysed over 170 cultivars, including commercial <jats:italic>Japonica</jats:italic> and <jats:italic>Indica</jats:italic> varieties, and a selection of 170 non-commercialized-under development varieties, both subjected to low (100 kg N/ha) and high (200 kg N/ha) fertilization regimes. Multispectral UAV imagery (MAIA S2) was normalized using Accumulated Growing Degree Days (GDD) to align phenological stages across sites. Random Forest (RF) classifiers were employed to analyse the capacity of RS to identify whether rice paddies are under- or over-fertilized. The transferability of N models between rice genotypes was also assessed. Furthermore, the previously established MS3 + yield regression model, originally developed for the <jats:italic>JSendra</jats:italic> variety, was evaluated against a multi-variety dataset. Results Random Forest classifiers effectively discriminated between nitrogen application rates across diverse genotypes, with several sites exceeding an 85% validation accuracy. A consistent trend emerges when analysing spectral importance: visible (VIS) bands take importance during the early season stages, whereas near-infrared (NIR) and red-edge (RE) reflectance provide critical diagnostic information throughout the entire crop cycle. Notably, 82% of the evaluated varieties demonstrated a high compatibility with the global model. Conversely, the yield model showed limited transferability between varieties. While it performed poorly on the global dataset, it successfully predicted yields for 48.8% of commercial varieties (residues within ± 1 tons per hectare), specifically those with genetic and structural similarities to the training variety. Conclusion The study concludes that N-status monitoring via RS classifiers is robust across varying rice genetics, whereas yield prediction models exhibit strong genotype dependency.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"35 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842852","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 soil and canopy sensing to map and relate variability in tart cherry orchards","authors":"Kurt Wedegaertner, Brent Black, Anderson Safre, Alfonso Torres-Rua, Grant Cardon, Matt Yost","doi":"10.1007/s11119-026-10436-4","DOIUrl":"https://doi.org/10.1007/s11119-026-10436-4","url":null,"abstract":"Purpose Evaluate how soil and canopy sensing can map within-block variability in tart cherry orchards and identify indicators robust enough for repeatable management decisions. Methods Soil apparent electrical conductivity (ECa) was mapped in spring 2022 across four commercial tart cherry blocks (8.5–10.5 ha; approximately 3,500 trees per block), followed by canopy sensing in 2023–2024. Canopy structure was measured using unmanned aerial vehicle (UAV) photogrammetry and mobile terrestrial laser scanning (MTLS) using light detection and ranging (LiDAR), and canopy density using mobile ceptometry. Spatial layers were aligned to per-tree grid cells. An August 2025 campaign compared UAV- and LiDAR-derived tree height with ground-truthed height. Results Soil-to-canopy relationships were weak to moderate but consistent within blocks ( <jats:italic>r</jats:italic> = 0.10–0.40), with strength and direction varying by site conditions. Canopy density was more strongly associated with UAV-derived volume than height. UAV-derived 90th-percentile height best predicted ground-truthed height ( <jats:italic>R</jats:italic> ² = 0.89; RMSE = 0.34 m), whereas LiDAR showed a weaker relationship and greater error ( <jats:italic>R</jats:italic> ² = 0.70; RMSE = 0.52 m). Cross-sensor agreement was moderate to strong ( <jats:italic>r</jats:italic> = 0.41–0.65). Per-tree rankings were stable between years for UAV height and volume. Conclusion Whole-block sensing revealed persistent spatial patterns that could support management-zone delineation. UAV photogrammetry provided accurate canopy metrics, MTLS offered measurements suited to routine orchard operations, ceptometry added seasonal canopy-density information, and ECa provided soil context. Occasional ECa mapping combined with strategically timed UAV surveys and other sensors as needed could reduce redundant sensing while supporting fertilizer evaluation, pruning, and labor allocation.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"26 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842850","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 map-based decision-support framework for variable-depth seeding of fodder maize integrating proximal soil sensing and multi-temporal satellite NDVI data","authors":"Jialu Sun, Manuel Vázquez-Arellano, Abdul Mounem Mouazen","doi":"10.1007/s11119-026-10437-3","DOIUrl":"https://doi.org/10.1007/s11119-026-10437-3","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"16 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842956","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":"Determination of critical nitrogen loss zones and high-resolution nitrate leaching modeling in drinking water protection areas with satellite-based algorithms","authors":"Martin Mittermayer, Joseph Donauer, Felix Klein, Frank Leßke, Ludwig Hagn, Johannes Schuster, Kurt-Jürgen Hülsbergen","doi":"10.1007/s11119-026-10405-x","DOIUrl":"https://doi.org/10.1007/s11119-026-10405-x","url":null,"abstract":"Purpose This study developed and validated an integrated framework to identify critical nitrate loss zones at sub-field scale in a drinking water protection area. The key question was whether satellite-based estimation of grain N uptake, combined with site-specific N balancing and soil–water–nitrate modeling, can reliably capture spatial patterns of nitrate leaching under practical farming conditions. Methods A satellite-based algorithm was developed to estimate winter wheat N uptake using data from georeferenced plots in southern Germany. Multiple linear regression and Random Forest models were applied based on Sentinel-2 vegetation indices, as well as soil, topographic, and climatic variables. Results and Discussion The Random Forest model performed slightly better (R <jats:sup>2</jats:sup> = 0.74, RMSE = 14.8 kg ha⁻ <jats:sup>1</jats:sup> , MAE = 11.2 kg ha⁻ <jats:sup>1</jats:sup> ) than the linear regression model (R <jats:sup>2</jats:sup> = 0.70, RMSE = 16.0 kg ha⁻ <jats:sup>1</jats:sup> , MAE = 12.5 kg ha⁻ <jats:sup>1</jats:sup> ). Predicted N uptake was combined with field-specific fertilization data to calculate site-specific N balances, which were integrated into a soil–water–nitrate leaching model. Low-yield zones showed lower N uptake but higher N surplus, modeled nitrate leaching, and measured nitrate–N stocks in deeper soil layers compared to high-yield zones. During validation (Field A), low-yield zones showed N surpluses of 98 kg ha⁻ <jats:sup>1</jats:sup> and modeled nitrate concentrations of 120 mg L⁻ <jats:sup>1</jats:sup> in seepage water, compared to 33 kg ha⁻ <jats:sup>1</jats:sup> and 49 mg L⁻ <jats:sup>1</jats:sup> in high-yield zones. Modeled nitrate concentrations correlated with measured nitrate–N stocks (R <jats:sup>2</jats:sup> = 0.68). The approach shows strong potential for identifying nitrate-prone areas and enabling targeted measures for drinking water protection.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"16 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842941","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":"Phenology-adaptive machine learning for early mapping of field-scale corn crop yield using fusion of Sentinel-2 satellite spectral imagery, and weather-based accumulated heat units","authors":"Arunachalam Manimozhian, Abhilash K. Chandel","doi":"10.1007/s11119-026-10428-4","DOIUrl":"https://doi.org/10.1007/s11119-026-10428-4","url":null,"abstract":"Purpose Timely, accurate, and field-scale crop yield mapping is essential for precision crop management, yet most existing studies rely on late- or full-season data, limiting in-season decision-making. This study aims to develop a stage-aware earliest possible corn yield mapping framework that balances early data availability, predictive accuracy, and spatial fidelity by integrating Sentinel-2 imagery, vegetation indices (VIs), and accumulated growing degree days (AGDD). Methods Corn yield data were collected from a commercial farm over three growing seasons (2018–2020). The final modeling dataset included 51,794 Sentinel-2 10 m aggregated yield samples across three seasons: 2018 ( &lt;jats:italic&gt;n&lt;/jats:italic&gt; = 16400), 2019 ( &lt;jats:italic&gt;n&lt;/jats:italic&gt; = 18534), and 2020 ( &lt;jats:italic&gt;n&lt;/jats:italic&gt; = 16860). Sentinel-2 raw spectral bands and derived VIs were organized for V4, V6, R1, R5, and R6 growth stages based on AGDD- and DAP-defined corn growth-stage windows, also confirmed visually on-ground, allowing observations from different planting and harvest dates across the three growing seasons to be aligned by crop developmental stage rather than calendar date. A stage-wise Pearson correlation and frequency-based selection identified informative and non-redundant VI subsets across crop development. Four machine learning models: Random Forest (RF), XGBoost (XGB), k-Nearest Neighbors (kNN), and a Neural Network (NNET; multi-layer perceptron) were trained using 13 input configurations, including individual growth stages and multi-stage combinations capturing phenological progression. Models were tuned via randomized search, trained on 2018–2019 data, and independently validated on the 2020 season. Yield predictions were mapped directly at 10 m Sentinel-2-pixel resolution without spatial interpolation to preserve fine-scale variability. Results Model performance was strongly influenced by phenological stage selection. Among single-stage inputs, R1 was the earliest stage where reliable yield mapping could be availed (RF: R² = 0.56, RMSE = 29.50%). While combining V6 with R1 stage inputs substantially improved predictive performance (RF: R² = 0.70, RMSE = 24.13%) for the yield mapping at the R1 stage, where V6-stage signals provided complementary yield-related information. The full-season combination (V4 + V6 + R1 + R5 + R6) produced the highest accuracy (RF: R² = 0.72, RMSE = 23.28%) but would be less suitable for early in-season decision-making. Early- or late-stage-only inputs (V4, R5, R6) showed weaker and less stable cross-year performance. Among algorithms, RF consistently generalized best to the independent 2020 dataset and is recommended for operational use. XGB showed strong training performance but reduced cross-year stability, kNN yielded moderate accuracy, and NNET achieved accuracy comparable to RF while closely reproducing observed spatial patterns such as center-pivot effects and edge gradients. Direct 10 m mapping preserved yield","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"35 6 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842927","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":"Study on the inversion of soybean leaf relative chlorophyll content by integrating thermal infrared and multispectral images","authors":"Haina Zhang, Chengyang Ni, Xianghui Lu, Ruiqi Du, Hongzhao Shi, Naining Zhong, Xin Luo, Yue Zhang, Wei Liu, Zihuang Yan, Xiaoying Feng","doi":"10.1007/s11119-026-10402-0","DOIUrl":"https://doi.org/10.1007/s11119-026-10402-0","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"15 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842958","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":"Multi-scale calibration strategies and minimal ground-truth requirements for soil moisture mapping on golf course fairways with portable L-band passive radiometry","authors":"Madan Sapkota, Elia Scudiero, Weston W. Floyd, Benjamin G. Wherley, Jacob O. Winger, David S. McCall, Travis L. Roberson, Elisabeth Kitchin, Chase M. Straw","doi":"10.1007/s11119-026-10433-7","DOIUrl":"https://doi.org/10.1007/s11119-026-10433-7","url":null,"abstract":"Purpose L-band passive microwave radiometry enables reliable soil moisture retrieval from satellites but at spatial resolutions too coarse for precision irrigation. Portable near-ground L-band radiometers offer a high-resolution alternative. This study evaluated a Portable L-band Radiometer (PoLRa) for estimating volumetric water content (VWC), comparing manufacturer-calibrated estimates with data-driven calibration approaches and quantifying ground-truth requirements. Methods Field experiments were conducted at four golf courses in Texas and Virginia from August 2023 to March 2025, with three fairways per site across 17 surveys. Brightness temperature was collected using a vehicle-mounted PoLRa, and ground-truth VWC was measured at 11 to 12 locations per fairway using time domain reflectometry (TDR). Model performance was evaluated across 5,000 calibration and evaluation iterations, with three samples per fairway. Site-dependent models employed analysis of covariance (ANOCOVA), and minimal calibration analyses evaluated ground-truth sample sizes ( <jats:italic>N</jats:italic> = 2 to 8) per fairway. Results Manufacturer-calibrated PoLRa estimates showed weak agreement with TDR (R² = 0.40). Data-driven site-independent models failed to maintain accuracy across locations and times (RMSE &gt; 0.089 m <jats:sup>3</jats:sup> m <jats:sup>− 3</jats:sup> ). Site-dependent models substantially improved performance (R² = 0.727 to 0.757; RMSE = 0.052 to 0.056 m <jats:sup>3</jats:sup> m <jats:sup>− 3</jats:sup> ), with the global ANOCOVA model balancing accuracy and scalability. Minimal calibration analysis showed three ground-truth samples per fairway achieved acceptable errors (RMSE &lt; 0.06 m³ m⁻³) in 75% of evaluations, while five or more ensured nearly all met this threshold. Conclusion Through site-dependent calibration, PoLRa can provide reliable soil moisture estimates, enabling scalable soil moisture mapping for precision irrigation and turfgrass management.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"49 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148768986","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":"Robust identification of known and unknown weeds with crop center localization based on Open-set object detection","authors":"Lamin L. Janneh, Leilei He, Bryan Gilbert Murengam, Xiaojuan Liu, Mbemba Hydara, Man Xia, Rui Li, Yanxin Zhang, Longsheng Fu","doi":"10.1007/s11119-026-10431-9","DOIUrl":"https://doi.org/10.1007/s11119-026-10431-9","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"351 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148728731","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}