A review of the issues, methods and perspectives for yield estimation, prediction and forecasting in viticulture

2021 ◽  
Vol 130 ◽  
pp. 126339
Author(s):  
Cécile Laurent ◽  
Baptiste Oger ◽  
James Arnold Taylor ◽  
Thibaut Scholasch ◽  
Aurelie Metay ◽  
...  
Keyword(s):  
Yield Estimation ◽  
Prediction And Forecasting

scholarly journals Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1789
Author(s):  
André Barriguinha ◽  
Miguel de Castro Neto ◽  
Artur José Freire Gil
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Spatial Scales ◽  
Estimation Methods ◽  
Future Research ◽  
Small Scale ◽  
Success Factor ◽  
Yield Estimation ◽  
Estimation Errors ◽  
Prediction And Forecasting

Purpose—knowing in advance vineyard yield is a critical success factor so growers and winemakers can achieve the best balance between vegetative and reproductive growth. It is also essential for planning and regulatory purposes at the regional level. Estimation errors are mainly due to the high inter-annual and spatial variability and inadequate or poor performance sampling methods; therefore, improved applied methodologies are needed at different spatial scales. This paper aims to identify the alternatives to traditional estimation methods. Design/methodology/approach—this study consists of a systematic literature review of academic articles indexed on four databases collected based on multiple query strings conducted on title, abstract, and keywords. The articles were reviewed based on the research topic, methodology, data requirements, practical application, and scale using PRISMA as a guideline. Findings—the methodological approaches for yield estimation based on indirect methods are primarily applicable at a small scale and can provide better estimates than the traditional manual sampling. Nevertheless, most of these approaches are still in the research domain and lack practical applicability in real vineyards by the actual farmers. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Research limitations—this work is based on academic articles published before June 2021. Therefore, scientific outputs published after this date are not included. Originality/value—this study contributes to perceiving the approaches for estimating vineyard yield and identifying research gaps for future developments, and supporting a future research agenda on this topic. To the best of the authors’ knowledge, it is the first systematic literature review fully dedicated to vineyard yield estimation, prediction, and forecasting methods.

Conceptualizing the impact of machines, science and technologies on the sociocultural environment that shapes the teacher's paradigm of values and views of life

2020 ◽  
Vol 16 (11) ◽  
pp. 2161-2179
Author(s):  
A.B. Lanchakov ◽  
S.A. Filin ◽  
A.Zh. Yakushev ◽  
E.E. Zhusipova
Keyword(s):  
Social Relationships ◽  
Cognitive Activity ◽  
Environmentally Friendly ◽  
Professional Activity ◽  
Sociocultural Environment ◽  
Sociocultural Development ◽  
Prediction And Forecasting ◽  
The Impact ◽  
Positive Motivation ◽  
The Way

Subject. In this article we analyze how machinery, science and technologies influence the sociocultural environment that engenders the teacher's paradigm of values and views of life. Objectives. We herein outline guidance to predict the way teachers' views of life might evolve in corresponding sociocultural periods more precisely. The article analyzes making more precise forecasts of oncoming economic crises, which will cause some changes in teachers' mindset. Methods. The study involves learning methodologies, methods of prediction and forecasting, including foresight. Results. We propose and analyze the theory holding that the human civilization passes cycles during its sociocultural development in terms of a new set of values in contemporary teachers' views of life. The article sets forth our recommendations on innovation-driven views of life, mindset and thinking and, consequently, the development of intellectual qualities, knowledge, skills, cognitive activity, positive motivation to the professional activity of a teacher and alumni during more elevated periods, which requires to more precisely predict the way teachers’ mindset may change in certain sociocultural periods. Conclusions and Relevance. As the human civilization enters the innovation-driven sociocultural period, teachers and social relationships should demonstrate more innovative and environmentally-friendly attitudes and views of life.

Evaluation of alternative methods to calculate evapotranspiration and their impact on soybean yield estimation

Agrometeoros ◽  
2020 ◽  
Vol 28 ◽  
Author(s):  
Rodrigo Cornacini Ferreira ◽  
Otávio Jorge Grigoli Abi-Saab ◽  
Marcelo Augusto de Aguiar e Silva ◽  
Rubson Natal Ribeiro Sibaldellib ◽  
José Renato Bouças Farias
Keyword(s):  
Alternative Methods ◽  
Yield Estimation ◽  
Soybean Yield

scholarly journals Occluded Grape Cluster Detection and Vine Canopy Visualisation Using an Ultrasonic Phased Array

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2182
Author(s):  
Baden Parr ◽  
Mathew Legg ◽  
Stuart Bradley ◽  
Fakhrul Alam
Keyword(s):  
Near Field ◽  
Low Frequency ◽  
Volume Estimation ◽  
Yield Estimation ◽  
3D Scan ◽  
Management Processes ◽  
Canopy Volume ◽  
Ultrasonic Phased Array ◽  
Added Benefit ◽  
Grape Yield

Grape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directional low frequency ultrasonic array composed of ultrasonic air-coupled transducers and microphones is used to image grapes through leaves. A fan is used to help differentiate between ultrasonic reflections from grapes and leaves. Improved resolution and detail are achieved with chirp excitation waveforms and near-field focusing of the array. The overestimation in grape volume estimation using ultrasound reduced from 222% to 112% compared to the 3D scan obtained using photogrammetry or from 56% to 2.5% compared to a convex hull of this 3D scan. This also has the added benefit of producing more accurate canopy volume estimations which are important for common precision viticulture management processes such as variable rate applications.

Fruits yield estimation using Faster R-CNN with MIoU

2021 ◽  
Author(s):  
Santi Kumari Behera ◽  
Amiya Kumar Rath ◽  
Prabira Kumar Sethy
Keyword(s):  
Yield Estimation

scholarly journals Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1633
Author(s):  
Elena-Simona Apostol ◽  
Ciprian-Octavian Truică ◽  
Florin Pop ◽  
Christian Esposito
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Change Point ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Change Point Detection ◽  
Change Points ◽  
Series Data ◽  
Prediction And Forecasting ◽  
Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

scholarly journals Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 223
Author(s):  
Rubaiya Binte Mostafiz ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Vegetation Indices ◽  
Land Suitability ◽  
Assessment System ◽  
Yield Estimation ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

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