Precision Agriculture最新文献

{"title":"Practical methods for aerial image acquisition and reflectance conversion using consumer-grade cameras on manned and unmanned aircraft","authors":"Chenghai Yang, Bradley K. Fritz, Charles P.-C. Suh","doi":"10.1007/s11119-024-10145-w","DOIUrl":"https://doi.org/10.1007/s11119-024-10145-w","url":null,"abstract":"<p>Consumer-grade cameras have emerged as a cost-effective alternative to conventional scientific cameras in precision agriculture applications. However, there is a lack of information on their appropriate use and calibration. This study focused on developing practical methodologies for determining optimal camera settings and converting image digital numbers (DNs) to reflectance. Two Nikon D7100 and two Nikon D850 cameras with visible and near-infrared (NIR) sensitivity were deployed on both manned and unmanned aircraft for image acquisition. To optimize camera settings, including exposure time and aperture, an approach that considered flight parameters and image histograms was employed. Linear and nonlinear regression analyses based on multiple nonlinear models were performed to accurately characterize the reflectance-DN relationship across all four bands (blue, green, red and NIR) based on seven calibration tarps. The results revealed that the exponential model with vertical translation was the optimal model for reflectance conversion for both camera types. Based on the optimized camera parameters and the optimal model type, this study provided an extensive analysis of the models and their root mean square errors (RMSE) derived from all 952 possible 2- to 6-tarp combinations for all bands in both camera types. This analysis led to the selection of optimal tarp combinations based on the desired level of accuracy for each of the five multi-tarp configurations. As the number of tarps increased to 4, 5, or 6, the RMSE values stabilized for all bands, indicating 4-tarp combinations were the optimal choice. These findings hold significant practical implications for practitioners in precision agriculture seeking guidance for configuring consumer-grade cameras effectively while ensuring accurate reflectance conversion.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"9 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895490","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":"Hyperspectral sensing and mapping of soil carbon content for amending within-field heterogeneity of soil fertility and enhancing soil carbon sequestration","authors":"Yoshio Inoue, Kunihiko Yoshino, Fumiki Hosoi, Akira Iwasaki, Takashi Hirayama, Takashi Saito","doi":"10.1007/s11119-024-10140-1","DOIUrl":"https://doi.org/10.1007/s11119-024-10140-1","url":null,"abstract":"<p>Soil fertility is one of the most critical bases for high productivity and sustainability in crop production. Within-field heterogeneity is often problematic in both crop management practices and crop productivity. Besides, appropriate soil management practices leads to the effective carbon sequestration. Since the soil carbon content (SCC) is the most simple and effective indicator of soil fertility, accurate and high-resolution mapping of SCC is an essential basis for addressing these issues. Here, we developed a tractor-based hyperspectral sensing system for speedy and accurate mapping of SCC. A new hybrid spectral algorithm linking normalized difference spectral index (<i>h</i>-NDSI) and machine learning proved superior. Appropriate algorithms were implemented to generate diagnostic map and prescription map from SCC map for the variable-rate application of pellet manure. The field performance of the sensing/mapping system was tested in the farmers' fields in the Fukushima region of Japan where the within-field heterogeneity of soil fertility was disastrous due to the decontamination after the nuclear power-plant disaster. The structure and functioning of the system proved promising. Moreover, the spatial simulation by linking the SCC data and a dynamic simulation model clearly showed the significant impact of variable-rate application of pellet manure on the chronosequential change of SCC, within-field heterogeneity, and carbon stock. The systematic linkage of the sensing/mapping system with the variable-rate spreader and dynamic simulation model would be effective for improving soil fertility and soil carbon stock. Applicability of the system will be extended through an extensive validation of the predictive models.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"11 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140845464","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-modal fusion and multi-task deep learning for monitoring the growth of film-mulched winter wheat","authors":"Zhikai Cheng, Xiaobo Gu, Yadan Du, Chunyu Wei, Yang Xu, Zhihui Zhou, Wenlong Li, Wenjing Cai","doi":"10.1007/s11119-024-10147-8","DOIUrl":"https://doi.org/10.1007/s11119-024-10147-8","url":null,"abstract":"<p>The precision monitoring of film-mulched winter wheat growth facilitates field management optimization and further improves yield. Unmanned aerial vehicle (UAV) is an effective tool for crop monitoring at the field scale. However, due to the interference of background effects caused by soil and mulch, achieving accurate monitoring of crop growth in complex backgrounds for UAV remains a challenge. Additionally, the simultaneous inversion of multiple growth parameters helped us to comprehensively monitor the overall crop growth status. This study conducted field experiments including three winter wheat mulching treatments: ridge mulching, ridge–furrow full-mulching, and flat cropping full-mulching. Three machine learning algorithms (partial least squares, ridge regression, and support vector machines) and deep neural network were employed to process the vegetation indices (VIs) feature data, and the residual neural network 50 (ResNet 50) was used to process the image data. Then the two modalities (VI feature data and image data) were fused to obtain a multi-modal fusion (MMF) model. Meanwhile, a film-mulched winter wheat growth monitoring model that simultaneously predicted leaf area index (LAI), aboveground biomass (AGB), plant height (PH), and leaf chlorophyll content (LCC) was constructed by coupling multi-task learning techniques. The results showed that the image-based ResNet 50 outperformed the VI feature-based model. The MMF improved prediction accuracy for LAI, AGB, PH, and LCC with coefficients of determination of 0.73–0.92, mean absolute errors of 0.29–3.89 and relative root mean square errors of 9.48–12.99%. A multi-task MMF model with the same loss weight distribution ([1/4, 1/4, 1/4, 1/4]) achieved comparable accuracy to the single-task MMF model, improving training efficiency and providing excellent generalization to different film-mulched sample areas. The novel technique of the multi-task MMF model proposed in this study provides an accurate and comprehensive method for monitoring the growth status of film-mulched winter wheat.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"51 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140845626","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":"Maize tassel number and tasseling stage monitoring based on near-ground and UAV RGB images by improved YoloV8","authors":"Xun Yu, Dameng Yin, Honggen Xu, Francisco Pinto Espinosa, Urs Schmidhalter, Chenwei Nie, Yi Bai, Sindhuja Sankaran, Bo Ming, Ningbo Cui, Wenbin Wu, Xiuliang Jin","doi":"10.1007/s11119-024-10135-y","DOIUrl":"https://doi.org/10.1007/s11119-024-10135-y","url":null,"abstract":"<p>The monitoring of the tassel number and tasseling time reflects the maize growth and is necessary for crop management. However, it mainly depends on field observations, which is very labor intensive and may be biased by human errors. Tassel detection remains challenging due to the varying appearance of tassels across maize varieties, tasseling stages, and spatial resolutions. Moreover, the capability of the deep learning model for monitoring tassel number change and the time of entering tasseling stage has not been explored. In this study, we propose a novel approach for fast tassel detection using PConv (Partial Convolution) within YoloV8 series, named PConv-YoloV8 series. Compared to seven state-of-the-art deep learning methods, PConv-YoloV8 × 6 best trades off detection accuracy with the number of parameters (Parameters = 52.50 MB, AP = 0.950, R² = 0.92, rRMSE = 9.08%). The potential of PConv-YoloV8 × 6 to provide an accurate detection of tassels in complex situations from near-ground and UAV images were comprehensively studied. PConv-YoloV8 × 6 maintained an excellent detection accuracy for maize at different tasseling stages (AP = 0.826–0.972, R² = 0.83–0.92, RMSE = 1.94–3.01, rRMSE = 21.06%-7.09%), for different varieties (AP = 0.901–0.978, R² = 0.77–0.97, RMSE = 1.39–3.16, rRMSE = 11.72%-5.06%), at different resolutions (AP = 0.921–0.956, R² = 0.84–0.93, rRMSE = 8.72%-17.71%), and on UAV images with different resolutions (AP = 0.918–0.968, R² = 0.98–0.99, rRMSE = 6.43%-12.76%), which proved the robustness of the model. The tasseling number and the time of entering tasseling stage detected from images were basically consistent with the trends observed in the manually labeled results. This study provides an effective method to monitor the tassel number and the time of entering the tasseling stage. A new maize tassel detection dataset (18260 tassels in 729 near-ground images and 20835 tassels in 144 UAV images) is created. Future studies will focus on making more lightweight models and achieving real-time detection capabilities.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"35 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140808438","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":"Comparative study of interpolation methods for low-density sampling","authors":"F. H. S. Karp, V. Adamchuk, P. Dutilleul, A. Melnitchouck","doi":"10.1007/s11119-024-10141-0","DOIUrl":"https://doi.org/10.1007/s11119-024-10141-0","url":null,"abstract":"<p>Given the high costs of soil sampling, low and extra-low sampling densities are still being used. Low-density soil sampling usually does not allow the computation of experimental variograms reliable enough to fit models and perform interpolation. In the absence of geostatistical tools, deterministic methods such as inverse distance weighting (IDW) are recommended but they are susceptible to the “bull’s eye” effect, which creates non-smooth surfaces. This study aims to develop and assess interpolation methods or approaches to produce soil test maps that are robust and maximize the information value contained in sparse soil sampling data. Eleven interpolation procedures, including traditional methods, a newly proposed methodology, and a kriging-based approach, were evaluated using grid soil samples from four fields located in Central Alberta, Canada. In addition to the original 0.4 ha⋅sample⁻¹ sampling scheme, two sampling design densities of 0.8 and 3.5 ha⋅sample⁻¹ were considered. Among the many outcomes of this study, it was found that the field average never emerged as the basis for the best approach. Also, none of the evaluated interpolation procedures appeared to be the best across all fields, soil properties, and sampling densities. In terms of robustness, the proposed kriging-based approach, in which the nugget effect estimate is set to the value of the semi-variance at the smallest sampling distance, and the sill estimate to the sample variance, and the IDW with the power parameter value of 1.0 provided the best approaches as they rarely yielded errors worse than those obtained with the field average.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"42 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140807358","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 new method for satellite-based remote sensing analysis of plant-specific biomass yield patterns for precision farming applications","authors":"Ludwig Hagn, Johannes Schuster, Martin Mittermayer, Kurt-Jürgen Hülsbergen","doi":"10.1007/s11119-024-10144-x","DOIUrl":"https://doi.org/10.1007/s11119-024-10144-x","url":null,"abstract":"<p>This study describes a new method for satellite-based remote sensing analysis of plant-specific biomass yield patterns for precision farming applications. The relative biomass potential (rel. BMP) serves as an indicator for multiyear stable and homogeneous yield zones. The rel. BMP is derived from satellite data corresponding to specific growth stages and the normalized difference vegetation index (NDVI) to analyze crop-specific yield patterns. The development of this methodology is based on data from arable fields of two research farms; the validation was conducted on arable fields of commercial farms in southern Germany. Close relationships (up to r &gt; 0.9) were found between the rel. BMP of different crop types and study years, indicating stable yield patterns in arable fields. The relative BMP showed moderate correlations (up to r = 0.64) with the yields determined by the combine harvester, strong correlations with the vegetation index red edge inflection point (REIP) (up to r = 0.88, determined by a tractor-mounted sensor system) and moderate correlations with the yield determined by biomass sampling (up to r = 0.57). The study investigated the relationship between the rel. BMP and key soil parameters. There was a consistently strong correlation between multiyear rel. BMP and soil organic carbon (SOC) and total nitrogen (TN) contents (r = 0.62 to 0.73), demonstrating that the methodology effectively reflects the impact of these key soil properties on crop yield. The approach is well suited for deriving yield zones, with extensive application potential in agriculture.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"94 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140807372","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 accuracy of crop water stress inversion of soil water content all day long","authors":"Bei Zhang, Jialiang Huang, Tianjin Dai, Sisi Jing, Yi Hua, Qiuyu Zhang, Hao Liu, Yuxiao Wu, Zhitao Zhang, Junying Chen","doi":"10.1007/s11119-024-10143-y","DOIUrl":"https://doi.org/10.1007/s11119-024-10143-y","url":null,"abstract":"<p>There is growing interest in using canopy temperature (Tc), including crop water Stress index (CWSI), for irrigation management. However, Tc varies greatly in one day, while soil water content (SWC) varies little, which may lead to different conclusions on whether irrigation is needed based on CWSI at different times. For this end, Tc of winter wheat was continuously monitored, and the data of such environmental factors as atmospheric temperature and soil water content (SWC) were simultaneously collected. CWSI was calculated based on empirical formulation and Tc and CWSI were generalized based on the normalization formulation. The correlation SWC between Tc and CWSI before and after generalization was compared and error analysis was based on SWC theoretical formula. The results showed: (1) the accuracy of SWC retrieval by Tc and CWSI increased firstly and then decreased with time during the day. The optimal time for Tc monitoring SWC was between 10:00 ~ 16:00 (R² &gt; 0.72) and the optimal time for CWSI monitoring SWC was between 9:00 ~ 18:00 (R² &gt; 0.69). (2) CWSI and Tc were mapped based on the relationship between crop water stress and soil water deficit and normalized canopy temperature expressions characterized the relationship between crop water stress and soil water deficit. (3) The accuracy of inversion of SWC by mapping Tc from 18:00 ~ 8:00 is increased from 0.5 ~ 0.6 to 0.7 ~ 0.8; the accuracy of soil water content inversion by mapping CWSI from 18:00 ~ 8:00 was improved from 0.2 ~ 0.4 to 0.4 ~ 0.6. (4) The theoretical expression of SWC deduced based on CWSI also proves that considering the relationship between crop water stress and soil water deficit change can effectively reduce the relative error from 30 to 5% in the morning and evening. This study contributes to the understanding of the reason why the correlation between Tc and SWC varies greatly during the day and solves the time-limited problem of thermal infrared remote sensing monitoring of crop water stress.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"88 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140642263","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 computer vision system for apple fruit sizing by means of low-cost depth camera and neural network application","authors":"G. Bortolotti, M. Piani, M. Gullino, D. Mengoli, C. Franceschini, L. Corelli Grappadelli, L. Manfrini","doi":"10.1007/s11119-024-10139-8","DOIUrl":"https://doi.org/10.1007/s11119-024-10139-8","url":null,"abstract":"<p>Fruit size is crucial for growers as it influences consumer willingness to buy and the price of the fruit. Fruit size and growth along the seasons are two parameters that can lead to more precise orchard management favoring production sustainability. In this study, a Python-based computer vision system (CVS) for sizing apples directly on the tree was developed to ease fruit sizing tasks. The system is made of a consumer-grade depth camera and was tested at two distances among 17 timings throughout the season, in a Fuji apple orchard. The CVS exploited a specifically trained YOLOv5 detection algorithm, a circle detection algorithm, and a trigonometric approach based on depth information to size the fruits. Comparisons with standard-trained YOLOv5 models and with spherical objects were carried out. The algorithm showed good fruit detection and circle detection performance, with a sizing rate of 92%. Good correlations (<i>r</i> &gt; 0.8) between estimated and actual fruit size were found. The sizing performance showed an overall mean error (mE) and RMSE of + 5.7 mm (9%) and 10 mm (15%). The best results of mE were always found at 1.0 m, compared to 1.5 m. Key factors for the presented methodology were: the fruit detectors customization; the <i>HoughCircle</i> parameters adaptability to object size, camera distance, and color; and the issue of field natural illumination. The study also highlighted the uncertainty of human operators in the reference data collection (5–6%) and the effect of random subsampling on the statistical analysis of fruit size estimation. Despite the high error values, the CVS shows potential for fruit sizing at the orchard scale. Future research will focus on improving and testing the CVS on a large scale, as well as investigating other image analysis methods and the ability to estimate fruit growth.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"24 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140557127","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":"An accurate monitoring method of peanut southern blight using unmanned aerial vehicle remote sensing","authors":"Wei Guo, Zheng Gong, Chunfeng Gao, Jibo Yue, Yuanyuan Fu, Heguang Sun, Hui Zhang, Lin Zhou","doi":"10.1007/s11119-024-10137-w","DOIUrl":"https://doi.org/10.1007/s11119-024-10137-w","url":null,"abstract":"<p>Peanut is a significant oilseed crop that is often affected by peanut southern blight, a disease that greatly reduces crop yield and quality. Therefore, accurate and timely monitoring of this disease is crucial to ensure crop safety and minimize the need for pesticides. Spectral features combined with texture features have been widely applied in plant disease monitoring. However, previous studies have mostly used original texture features, and its combination form has been rarely considered. This study presents a novel approach for monitoring peanut southern blight, integrating multiple spectral indices and textural indices (TIs). Firstly, a total of 20 vegetation indices (VIs) were extracted from the unmanned aerial vehicle multispectral images, while three TIs were constructed based on original textural features. Subsequently, Otsu-CIgreen algorithm was used to find the optimal threshold to eliminate the complex background of the image. Lastly, monitoring models for peanut southern blight were constructed using three machine learning models based on the screened VIs, VIs combined with TIs. Among these models, the K-nearest neighbor model using VIs combined with TIs demonstrates the best performance, with accuracy and F1 score on the test set reaching 91.89% and 91.39% respectively. The results indicate that the monitoring models utilizing VIs and TIs were more effective compared to models using only VIs. This approach provides valuable insights for non-destructive and accurate monitoring of peanut southern blight.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"37 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140349085","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-field extrapolation away from a soil moisture probe using freely available satellite imagery and weather data","authors":"R. G. V. Bramley, E. M. Perry, J. Richetti, A. F. Colaço, D. J. Mowat, C. E. M. Ratcliff, R. A. Lawes","doi":"10.1007/s11119-024-10138-9","DOIUrl":"https://doi.org/10.1007/s11119-024-10138-9","url":null,"abstract":"<p>Recognition of the importance of soil moisture information to the optimisation of water-limited dryland cereal production has led to Australian growers being encouraged to make use of soil moisture sensors. However, irrespective of the merits of different sensing technologies, only a small soil volume is sensed, raising questions as to the utility of such sensors in broadacre cropping, especially given spatial variability in soil water holding capacity. Here, using data collected from contrasting sites in South Australia and Western Australia over two seasons, during which either wheat or barley were grown, we describe a method for extrapolating soil moisture information away from the location of a probe using freely-available NDVI time series and weather data as covariates. Relationships between soil moisture probe data, cumulative NDVI (ΣNDVI), cumulative net precipitation (ΣNP) and seasonal growing degree days (GDD) were significant (<i>P</i> &lt; 0.0001). In turn, these could be used to predict soil moisture status for any location within a field on any date following crop emergence. However, differences in ΣNDVI between different within-field zones did not fully explain differences in the soil moisture from multiple sensors located in these zones, resulting in different calibrations being required for each sensor or zone and a relatively low accuracy of prediction of measured soil moisture (R²_adj ~ 0.4–0.7) which may not be sufficient to support targeted agronomic decision-making. The results also suggest that at any location within a field, the range of variation in soil moisture status down the soil profile on any given date will present as greater than the spatial variation in soil moisture across the field on that date. Accordingly, we conclude that, in dryland cereal cropping, the major value in soil moisture sensors arises from an enhanced ability to compare seasons and to relate similarities and differences between seasons as a guide to decision-making.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"1 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140343379","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}