Precision Agriculture最新文献

{"title":"Retinanet_G2S: a multi-scale feature fusion-based network for fruit detection of punna navel oranges in complex field environments","authors":"Hongxing Peng, Hu Chen, Xin Zhang, Huanai Liu, Keyin Chen, Juntao Xiong","doi":"10.1007/s11119-023-10098-6","DOIUrl":"https://doi.org/10.1007/s11119-023-10098-6","url":null,"abstract":"<p>In the natural environment, the detection and recognition process of Punna navel orange fruit using machine vision systems is affected by many factors, such as complex background, uneven light illumination, occlusions of branches and leaves and large variations in fruit size. To solve these problems of low accuracy in fruit detection and poor robustness of the detection algorithm in the field conditions, a new object detection algorithm, named Retinanet_G2S, was proposed in this paper based on the modified Retinanet network. The images of Punna navel orange were collected with Microsoft Kinect V2 in the uncontrolled environment. Firstly, a new Res2Net-GF network was designed to replace the section of feature extraction in the original Retinanet, which can potentially improve the learning ability of target features of the trunk network. Secondly, a multi-scale cross-regional feature fusion grids network was designed to replace the feature pyramid network module in the original Retinanet, which could enhance the ability of feature information fusion among different scales of the feature pyramid. Finally, the original border regression localization method in Retinanet network was optimized based on the accurate boundary box regression algorithm. The study results showed that, compared with the original Retinanet network, Retinanet_G2S improved mAP, mAP50, mAP75, mAP_S, mAP_M and mAP_L by 3.8%, 1.7%, 5.8%, 2.4%, 2.1% and 5.5%, respectively. Moreover, compared with 7 types of classic object detection models, including SSD, YOLOv3, CenterNet, CornerNet, FCOS, Faster-RCNN and Retinanet, the average increase in mAP of Retinanet_G2S was 9.11%. Overall, Retinanet_G2S showed a promising optimization effect, particularly for the detection of small targets and overlapping fruits.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138740515","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":"Design, implementation and validation of a sensor-based precise airblast sprayer to improve pesticide applications in orchards","authors":"Bernat Salas, Ramón Salcedo, Francisco Garcia-Ruiz, Emilio Gil","doi":"10.1007/s11119-023-10097-7","DOIUrl":"https://doi.org/10.1007/s11119-023-10097-7","url":null,"abstract":"<p>An orchard sprayer prototype running a variable-rate algorithm to adapt the spray volume to the canopy characteristics (dimensions, shape and leaf density) in real-time was designed and implemented. The developed machine was able to modify the application rate by using an algorithm based on the tree row volume, in combination with a newly coefficient defined as Density Factor (<i>Df</i>). Variations in the canopy characteristics along the row crop were electronically measured using six ultrasonic sensors (three per sprayer side). These differences in foliage structure were used to adjust the flow rate of the nozzles by merging the ultrasonic sensors data and the forward speed information received from the on-board GNSS. A set of motor-valves was used to regulate the final amount of sprayed liquid. Laboratory and field tests using artificial canopy were arranged to calibrate and select the optimal ultrasonic sensor configuration (width beam and signal pre-processing method) that best described the physical canopy properties. Results indicated that the sensor setup with a medium beam width offered the most appropriate characterization of trees in terms of width and <i>Df</i>. The experimental sprayer was also able to calculate the application rate automatically depending on changes on target trees. In general, the motor valves demonstrated adequate capability to supply and control the required liquid pressure at all times, mainly when spraying in a range between 4.0 and 14.0 MPa. Further work is required on the equipment, such as designing field efficiency tests for the sprayer or refining the accuracy of <i>Df</i>.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138714144","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":"High-throughput phenotyping of individual plant height in an oilseed rape population based on Mask-RCNN and UAV images","authors":"Yutao Shen, Xuqi Lu, Mengqi Lyu, Hongyu Zhou, Wenxuan Guan, Lixi Jiang, Yuhong He, Haiyan Cen","doi":"10.1007/s11119-023-10095-9","DOIUrl":"https://doi.org/10.1007/s11119-023-10095-9","url":null,"abstract":"<p>Plant height, a key agronomic trait, affects crop structure, photosynthesis, and thus the final yield and seed quality. The combination of digital cameras on unmanned aerial vehicles (UAVs) and use of structure from motion have enabled high-throughput crop canopy height estimation. However, the focus of prior research has mainly been on plot-level height prediction, neglecting precise estimations for individual plants. This study aims to explore the potential of UAV RGB images with mask region-based convolutional neural network (Mask-RCNN) for high-throughput phenotyping of individual-level height (IH) in oilseed rape at different growth stages. Field-measured height (FH) of nine sampling plants in each subplot of the 150 subplots was obtained by manual measurement after the UAV flight. An instance segmentation model for oilseed rape with data augmentation based on the Mask-RCNN model was developed. The IHs were then used to obtain plot-level height based on individual-level height (PHIH). The results show that Mask-RCNN performed better than the conventional Otsu method with the F1 score increased by 60.8% and 26.6% under high and low weed pressure, respectively. The trained model with data augmentation achieved accurate crop height estimation based on overexposed and underexposed UAV images, indicating the model’s applicability in practical scenarios. The PHIH can be predicted with the determination coefficient (r²) of 0.992, root mean square error (RMSE) of 4.03 cm, relative root mean square error (rRMSE) of 7.68%, which outperformed the results in the reported studies, especially in the late bolting stage. The IHs of the whole growth stages of oilseed can be predicted by this method with an r² of 0.983, RMSE of 2.60 cm, and rRMSE of 7.14%. Furthermore, this method enabled a comprehensive Genome-wide association study (GWAS) in a 293-accession genetic population. The GWAS identified 200 and 65 statistically significant single nucleotide polymorphisms (SNPs), which were tightly associated with 28 and 11 candidate genes, at the late bolting and flowering stages, respectively. These findings demonstrated that the proposed method is promising for accurate estimations of IHs in oilseed rape as well as exploring the variations within the subplot, thus providing great potential for high-throughput plant phenotyping in crop breeding.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138634978","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":"Extracting illuminated vegetation, shadowed vegetation and background for finer fractional vegetation cover with polarization information and a convolutional network","authors":"Hongru Bi, Wei Chen, Yi Yang","doi":"10.1007/s11119-023-10094-w","DOIUrl":"https://doi.org/10.1007/s11119-023-10094-w","url":null,"abstract":"<p>Shadows are inevitable in vegetated remote sensing scenes due to variations in viewing and solar geometries, resulting in illuminated vegetation, shadowed vegetation, illuminated background and shadowed background. In RGB images, shadowed vegetation is difficult to separate from the shadowed background because their spectra are very similar in the visible light range. Furthermore, shadowed vegetation may provide different ecological functions than illuminated vegetation. Therefore, it is important to extract both illuminated and shadowed vegetation instead of combining them into one vegetation class. However, most previous studies focused on extracting total vegetation cover and neglected separating illuminated and shadowed vegetation, partly due to a lack of sufficient information. In this study, polarization information is introduced to extract illuminated vegetation, shadowed vegetation and background simultaneously with different deep learning algorithms. The experimental results show that the addition of polarization information can effectively improve the extraction accuracy of illuminated vegetation, shadowed vegetation and background, with a maximum accuracy improvement of 12.2%. The accuracy of shadow vegetation improved the most, with a rate of 21.8%. The results of this study suggest that by adding polarization information, illuminated and shadowed vegetation can be accurately extracted to provide a reliable vegetation cover product for remote sensing.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138582519","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":"DAE-Mask: a novel deep-learning-based automatic detection model for in-field wheat diseases","authors":"Rui Mao, Yuchen Zhang, Zexi Wang, Xingan Hao, Tao Zhu, Shengchang Gao, Xiaoping Hu","doi":"10.1007/s11119-023-10093-x","DOIUrl":"https://doi.org/10.1007/s11119-023-10093-x","url":null,"abstract":"<p>Wheat diseases seriously restrict the safety of wheat production and food quality. For farmers and agriculture technicians, diagnosing the disease with the naked eye is not suitable for modern precision agriculture. Deep learning has shown promise in crop disease diagnosis, but accuracy and speed remain a significant challenge in natural field conditions. In this study, a novel DAE-Mask method based on <b>d</b>iversification-<b>a</b>ugmented features and <b>e</b>dge features was proposed for intelligent wheat disease detection. DAE-Mask used Densely Connected Convolutional Networks (DenseNet) for preliminary feature extraction, and a backbone feature extraction network combining Feature Pyramid Network (FPN) and attention mechanism was designed to extract diversification-augmented features. To accelerate DAE-Mask, an Edge Agreement Head module based on Sobel filters was designed to compare edge features during training, which improved the model’s mask generation efficiency. We also built a multi-scene wheat disease dataset, MSWDD2022, containing images of wheat stripe rust, wheat powdery mildew, wheat yellow dwarf, and wheat scab. Our model achieved detection speed of 0.08s/pic. On MSWDD2022, our model with mean average precision (<i>mAP</i>) of 96.02% outperformed YOLOv5s, YOLOv8x, SSD, EfficientDet, CenterNet, and RefineDet by 7.79, 1.32, 3.54, 4.79, 9.77, and 5.29 percentage points, respectively. On the public dataset PlantDoc, our model with <i>mAP</i> of 57.68% outperformed YOLOv5s, YOLOv8x, SSD, EfficientDet, CenterNet, and RefineDet by 27.76, 6.48, 14.43, 11.79, 19.40, and 13.40 percentage points, respectively. Finally, the DAE-Mask was deployed on WeChat Mini Program to realize the real-time detection of in-field wheat diseases. The <i>mAP</i> reached 92.78%, and the average return delay of each image was 1.43s.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138571150","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":"Precision of grain yield monitors for use in on-farm research strip trials","authors":"A. A. Gauci, J. P. Fulton, A. Lindsey, S. A. Shearer, D. Barker, E. M. Hawkins","doi":"10.1007/s11119-023-10092-y","DOIUrl":"https://doi.org/10.1007/s11119-023-10092-y","url":null,"abstract":"<p>On-farm research (OFR) has become popular as a result of precision agriculture technology simplifying the process and farm software capabilities to summarize results collected through the technology. Different OFR designs exists with strip-trials being a simple approach to evaluate different treatments. Common in OFR is the use of yield monitors to collect crop performance data since yield represents a primary response variable in these type studies. The objective was to investigate the ability of grain yield monitoring technologies to accurately inform strip trials when frequent yield variability exists within an experimental unit. A combination of six sub-plot treatment resolutions (TR) that differed in length of imposed yield variation (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) were harvested at combine ground speeds of 3.2, 6.4, 7.2, and 8.1 kph, depending on study site (three study sites total). Intentional yield differences in maize (<i>Zea mays L.</i>) were created for each sub-plot by alternating the amount nitrogen (N) applied: 0 or 202 kg N/ha. Yield was measured by four commercially available yield monitoring (YM) technologies and a weigh wagon. Comparisons were made between the accumulated mass of the YM technology and weigh wagon through percent differences along with testing the significance of the plotted relationship between YM and weigh wagon. Results indicated that yield monitoring technology can be used to evaluate strip trial performance regardless of yield frequency and variability (error &lt; 3%) within an experimental unit when operating within the calibrated range of the mass flow sensor. Operating outside of the calibrated range of the mass flow sensor resulted in &gt; 15% error in estimating accumulated weight and overestimation of yield by 23%. Finally, no significant differences existed in estimating accumulated weight values between grain yield monitor technologies (all p-values ≥ 0.54).</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138565149","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":"Within-season vegetation indices and yield stability as a predictor of spatial patterns of Maize (Zea mays L) yields","authors":"Guanyuan Shuai, Ames Fowler, Bruno Basso","doi":"10.1007/s11119-023-10101-0","DOIUrl":"https://doi.org/10.1007/s11119-023-10101-0","url":null,"abstract":"<p>Accurate evaluation of crop performance and yield prediction at a sub-field scale is essential for achieving high yields while minimizing environmental impacts. Two important approaches for improving agronomic management and predicting future crop yields are the spatial stability of historic crop yields and in-season remote sensing imagery. However, the relative accuracies of these approaches have not been well characterized. In this study, we aim to first, assess the accuracies of yield stability and in-season remote sensing for predicting yield patterns at a sub-field resolution across multiple fields, second, investigate the optimal satellite image date for yield prediction, and third, relate bi-weekly changes in GCVI through the season to yield levels. We hypothesize that historical yield stability zones provide high accuracies in identifying yield patterns compared to within-season remote sensing images.</p><p>To conduct this evaluation, we utilized biweekly Planet images with visible and near-infrared bands from June through September (2018–2020), along with observed historical yield maps from 115 maize fields located in Indiana, Iowa, Michigan, and Minnesota, USA. We compared the yield stability zones (YSZ) with the in-season remote sensing data, specifically focusing on the green chlorophyll vegetative index (GCVI). Our analysis revealed that yield stability maps provided more accurate estimates of yield within both high stable (HS) and low stable (LS) yield zones within fields compared to any single-image in-season remote sensing model.</p><p>For the in-season remote sensing predictions, we used linear models for a single image date, as well as multi-linear and random forest models incorporating multiple image dates. Results indicated that the optimal image date for yield prediction varied between and within fields, highlighting the instability of this approach. However, the multi-image models, incorporating multiple image dates, showed improved prediction accuracy, achieving R² values of 0.66 and 0.86 by September 1st for the multi-linear and random forest models, respectively. Our analysis revealed that most low or high GCVI values of a pixel were consistent across the season (77%), with the greatest instability observed at the beginning and end of the growing season. Interestingly, the historical yield stability zones provided better predictions of yield compared to the bi-weekly dynamics of GCVI. The historically high-yielding areas started with low GCVI early in the season but caught up, while the low-yielding areas with high initial GCVI faltered.</p><p>In conclusion, the historical yield stability zones in the US Midwest demonstrated robust predictive capacity for in-field heterogeneity in stable zones. Multi-image models showed promise for assessing unstable zones during the season, but it is crucial to link these two approaches to fully capture both stable and unstable zones of crop yield. This study provides oppor","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138544825","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":"Visible and near-infrared spectroscopy predicted leaf nitrogen contents of potato varieties under different growth and management conditions","authors":"Ashmita Rawal, Alfred Hartemink, Yakun Zhang, Yi Wang, Richard A. Lankau, Matthew D. Ruark","doi":"10.1007/s11119-023-10091-z","DOIUrl":"https://doi.org/10.1007/s11119-023-10091-z","url":null,"abstract":"<p>Visible-Near Infrared (vis-NIR) spectroscopy can provide a faster, cost-effective, and user-friendly solution to monitor leaf N status, potentially overcoming the limitations of current techniques. The objectives of the study were to develop and validate partial least square regression (PLSR) to estimate the total N contents of fresh and removed leaves of potatoes using the vis-NIR spectral range (350–2500 nm) generated from a handheld proximal sensor. The model was built using data collected from Hancock Agricultural Research Station, WI, USA in 2020 and was validated using samples collected in 2021 for four different conditions. The conditions included two sites (Coloma and Hancock), four potato varieties (Burbank, Norkotah, Goldrush, and Silverton), two N rates (unfertilized and 308 kg N ha⁻¹), and four growth stages (vegetative, tuber initiation, tuber bulking, and tuber maturation). The calibration and validation models had high predictive performance for leaf total N with R² &gt; 0.8 and RPD &gt; 2. The model accuracy was affected by the total N contents in the leaf samples where the model underpredicted the samples with total leaf N contents greater than 6%.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"109126842","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":"Spectroscopic determination of chlorophyll content in sugarcane leaves for drought stress detection","authors":"Jingyao Gai, Jingyong Wang, Sasa Xie, Lirong Xiang, Ziting Wang","doi":"10.1007/s11119-023-10082-0","DOIUrl":"https://doi.org/10.1007/s11119-023-10082-0","url":null,"abstract":"<p>Drought is a major abiotic stress that affects the productivity of sugarcane worldwide. Water deficiency during sugarcane growth will lead to a reduction in leaf pigment content, such as chlorophyll, known as chlorosis. Although changes in spectral reflectance signature were identified a conspicuous sign of chlorophyll content changes caused by drought stress, the quantitative relationships between leaf chlorophyll content and spectral reflection signatures are still poorly explored. In this study, we present our contribution in systematically establishing a model for estimating leaf chlorophyll content in drought-affected sugarcane using VIS/NIR reflectance spectroscopy and characteristic band extraction techniques. Leaves of sugarcane plants at early elongation stage under different controlled irrigation conditions were used for spectra data collection, and the chlorophyll contents were collected with standard analytical methods. Different characteristic band extraction techniques and regression models were compared and discussed to obtain a chlorophyll content estimation model with the best performance. As the quantitative results, the combination of characteristic bands extracted by the successive projection algorithm (SPA) with a Stacking regression model achieved a high chlorophyll content estimation performance (<i>R</i>² = 0.9834, <i>RMSE </i> = 0.0544 mg/cm²) with only 4.3% of original spectral variables as inputs. This study provides a theoretical basis for accurate and non-invasive drought stress level estimation in large-scale cultivation.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91398618","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":"Generic optimization approach of soil hydraulic parameters for site-specific model applications","authors":"Jonas Trenz, Emir Memic, William D. Batchelor, Simone Graeff-Hönninger","doi":"10.1007/s11119-023-10087-9","DOIUrl":"https://doi.org/10.1007/s11119-023-10087-9","url":null,"abstract":"<p>Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R² of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R² 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72365790","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}