scholarly journals Predicting tomato field-yield using continuous monitoring of young tomato water status

2021 ◽  
Author(s):  
Sanbon Gosa ◽  
Amit Koch ◽  
Itamar Shenhar ◽  
Joseph Hirschberg ◽  
Dani Zamir ◽  
...  
Keyword(s):  
Prediction Models ◽  
Water Status ◽  
Total Yield ◽  
Field Measurements ◽  
Carotenoid Biosynthesis ◽  
Yield Prediction ◽  
Drought Treatment ◽  
Recessive Mutations ◽  
Tomato Yield ◽  
Tomato Field

To address the challenge of predicting tomato yields in the field, we used whole-plant functional phenotyping to evaluate water relations under well-irrigated and drought conditions. The genotypes tested are known to exhibit variability in their yields in wet and dry fields. The examined lines included two lines with recessive mutations that affect carotenoid biosynthesis, zeta z2083 and tangerine t3406, both isogenic to the processing tomato variety M82. The two mutant lines were reciprocally grafted onto M82 and multiple physiological characteristics were measured continuously, as well as before, during and after drought treatment in the greenhouse. A comparative analysis of greenhouse and field yields showed that the whole-canopy stomatal conductance (gsc) in the morning and cumulative transpiration (CT) were strongly correlated with field measurements of total yield (TY: r2 = 0.9 and 0.77, respectively) and plant vegetative weight (PW: r2 = 0.6 and 0.94, respectively). Furthermore, the minimum CT during drought and the rate of recovery when irrigation was resumed were both found to predict resilience. Keywords: drought tolerance, functional genomic mapping, functional phenotyping, physiological trait, time-series measurements, tomato, yield prediction, yield-prediction models

scholarly journals Prediction of Tomato Yield in Chinese-Style Solar Greenhouses Based on Wavelet Neural Networks and Genetic Algorithms

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 336
Author(s):  
Yonggang Wang ◽  
Ruimin Xiao ◽  
Yizhi Yin ◽  
Tan Liu
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Economic Benefits ◽  
Yield Prediction ◽  
Tomato Yield ◽  
Relative Errors ◽  
Bp Neural Network Model ◽  
Mean Square Errors ◽  
Production Plans

Yield prediction for tomatoes in greenhouses is an important basis for making production plans, and yield prediction accuracy directly affects economic benefits. To improve the prediction accuracy of tomato yield in Chinese-style solar greenhouses (CSGs), a wavelet neural network (WNN) model optimized by a genetic algorithm (GA-WNN) is applied. Eight variables are selected as input parameters and the tomato yield is the prediction output. The GA is adopted to optimize the initial weights, thresholds, and translation factors of the WNN. The experiment results show that the mean relative errors (MREs) of the GA-WNN model, WNN model, and backpropagation (BP) neural network model are 0.0067, 0.0104, and 0.0242, respectively. The results root mean square errors (RMSEs) are 1.725, 2.520, and 5.548, respectively. The EC values are 0.9960, 0.9935, and 0.9868, respectively. Therefore, the GA-WNN model has a higher prediction precision and a better fitting ability compared with the BP and the WNN prediction models. The research of this paper is useful from both theoretical and technical perspectives for quantitative tomato yield prediction in the CSGs.

Assessment of rice yield prediction models based on big data analytics for better supply chain decision-making in Thailand

2021 ◽  
Vol 12 (3) ◽  
pp. 221
Author(s):  
John K.M. Kuwornu ◽  
Chutiporn Anutariya ◽  
Attaphongse Taparugssanagorn ◽  
Sumanya Ngandee
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Prediction Models ◽  
Rice Yield ◽  
Big Data Analytics ◽  
Yield Prediction

scholarly journals Yield Prediction for Tomato Greenhouse Using EFuNN

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Kefaya Qaddoum ◽  
E. L. Hines ◽  
D. D. Iliescu
Keyword(s):  
Large Scale ◽  
Management Practices ◽  
Intelligent System ◽  
Vapour Pressure Deficit ◽  
Knowledge Bases ◽  
Yield Prediction ◽  
Tomato Yield ◽  
Fuzzy Neural ◽  
Rule Sets ◽  
Human Operators

In the area of greenhouse operation, yield prediction still relies heavily on human expertise. This paper proposes an automatic tomato yield predictor to assist the human operators in anticipating more effectively weekly fluctuations and avoid problems of both overdemand and overproduction if the yield cannot be predicted accurately. The parameters used by the predictor consist of environmental variables inside the greenhouse, namely, temperature, CO2, vapour pressure deficit (VPD), and radiation, as well as past yield. Greenhouse environment data and crop records from a large scale commercial operation, Wight Salads Group (WSG) in the Isle of Wight, United Kingdom, collected during the period 2004 to 2008, were used to model tomato yield using an Intelligent System called “Evolving Fuzzy Neural Network” (EFuNN). Our results show that the EFuNN model predicted weekly fluctuations of the yield with an average accuracy of 90%. The contribution suggests that the multiple EFUNNs can be mapped to respective task-oriented rule-sets giving rise to adaptive knowledge bases that could assist growers in the control of tomato supplies and more generally could inform the decision making concerning overall crop management practices.

scholarly journals Remotely Sensed Water Limitation in Vegetation: Insights from an Experiment with Unmanned Aerial Vehicles (UAVs)

2019 ◽  
Author(s):  
Kelly Easterday ◽  
Chippie Kislik ◽  
Tod E. Dawson ◽  
Sean Hogan ◽  
Maggi Kelly
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Water Content ◽  
Water Deficit ◽  
Water Status ◽  
Field Measurements ◽  
Flying Height ◽  
Plant Water ◽  
Spectral Indices ◽  
Aerial Vehicles ◽  
The Relationship

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors present an opportunity to monitor vegetation with on-demand high spatial and temporal resolution. In this study, we use multispectral imagery from quadcopter UAVs to monitor the progression of a water manipulation experiment on a common shrub, Baccharis pilularis (coyote brush), at the Blue Oak Ranch Reserve (BORR) near San Jose, California. We recorded multispectral data from the plants at several altitudes with nearly hourly intervals to explore the relationship between two common spectral indices, NDVI and NDRE, and plant water content and water potential, as physiological metrics of plant water status, across a gradient of water deficit. An examination of the spatial and temporal thresholds at which water limitations were most detectable revealed that the best separation between levels of water deficit were at higher resolution (lower flying height), and in the morning (NDVI) and early morning (NDRE). We found that both measures were able to identify moisture deficit in plants and distinguish them from control and watered plants; however, NDVI was better able to distinguish between treatments than NDRE and was more positively correlated with field measurements of plant water content than NDRE. Finally, we explored how relationships between spectral indices and water status changed when the imagery was scaled to courser resolutions provided by satellite-based imagery (PlanetScope) and found that PlanetScope data was able to capture the overall trend in treatments but was not able to capture subtle changes in water content. These kinds of experiments that evaluate the relationship between direct field measurements and UAV camera sensitivity are needed to enable translation of field-based physiology measurements to landscape or regional scales.

scholarly journals Theory Survey of Stock Yield Prediction Models

2009 ◽  
Vol 1 (1) ◽  
Author(s):  
Wei Shen
Keyword(s):  
Prediction Models ◽  
Yield Prediction

scholarly journals The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L)

2021 ◽  
Vol 245 ◽  
pp. 106584
Author(s):  
Paul Reuben Mwinuka ◽  
Boniface P. Mbilinyi ◽  
Winfred B. Mbungu ◽  
Sixbert K. Mourice ◽  
H.F. Mahoo ◽  
...  
Keyword(s):  
Multispectral Imaging ◽  
Water Status ◽  
Yield Prediction ◽  
Status Assessment ◽  
Canopy Water

Implications of using two natural enemies of Tuta absoluta (Lepidoptera: Gelechiidae) toward tomato yield enhancement

2019 ◽  
Vol 109 (05) ◽  
pp. 617-625
Author(s):  
M.A. Mirhosseini ◽  
Y. Fathipour ◽  
M. Soufbaf ◽  
G.V.P. Reddy
Keyword(s):  
Total Yield ◽  
Egg Parasitoid ◽  
Tuta Absoluta ◽  
Yield Enhancement ◽  
Leaf Miner ◽  
Trichogramma Brassicae ◽  
Tomato Yield ◽  
Nesidiocoris Tenuis ◽  
Mirid Bug ◽  
Predatory Mirid

AbstractTomato leaf miner (TLM), Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) is one of the most destructive tomato pests worldwide. We tested quantity and quality of tomato fruits after simultaneous use of two biological control agents, the predatory mirid bug Nesidiocoris tenuis (Reuter) and the egg parasitoid Trichogramma brassicae Bezdenko against TLM. We varied the timing of predator releases (before or after pest establishment) and the number of parasitoids released (ten or 30 females per week per m2). The highest number of fruits per cage, percentage of undamaged fruits, total yield weight, and undamaged yield weight were all obtained with predator-in-first treatments, with or without parasitoid releases. Furthermore, measures of fruit quality were also highest in predator-in-first treatments, including, highest percentage of water, greatest proportional fresh weight of carbohydrates, most lycopene, most β-carotene, most flavonoids, and highest total chlorophyll. Thus, our findings support a predator-in-first augmentation approach for management of TLM.

scholarly journals Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

2019 ◽  
Vol 11 (2) ◽  
pp. 533 ◽  
Author(s):  
Gniewko Niedbała
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Precision Agriculture ◽  
Prediction Models ◽  
Approximation Error ◽  
Main Element ◽  
Forecast Errors ◽  
Yield Prediction ◽  
Winter Rapeseed ◽  
Artificial Neural

The aim of the work was to produce three independent, multi-criteria models for the prediction of winter rapeseed yield. Each of the models was constructed in such a way that the yield prediction can be carried out on three dates: April 15th, May 31st, and June 30th. For model building, artificial neural networks with multi-layer perceptron (MLP) topology were used, on the basis of meteorological data (temperature and precipitation) and information about mineral fertilisation. The data were collected from the years, 2008–2015, from 328 production fields located in Greater Poland, Poland. An assessment of the quality of forecasts produced based on neural models was verified by determination of forecast errors using RAE (relative approximation error), RMS (root mean square error), MAE (mean absolute error) error indicators, and MAPE (mean absolute percentage error). An important feature of the produced prediction models is the ability to realize the forecast in the current agrotechnical year on the basis of the current weather and fertiliser information. The lowest MAPE error values were obtained for the neural model WR15_04 (April 15th) based on the MLP network with structure 15:15-18-11-1:1, which reached 7.51%. Other models reached MAPE errors of 7.85% for model WR31_05 (May 31st) and 8.12% for model WR30_06 (June 30th). The performed sensitivity analysis gave information about the factors that have the greatest impact on winter rapeseed yields. The highest rank of 1 was obtained by two networks for the same independent variable in the form of the sum of precipitation within a period from September 1st to December 31st of the previous year. However, in model WR15_04, the highest rank obtained a feature in the form of a sum of molybdenum fertilization in the current year (MO_CY). The models of winter rapeseed yield produced in the work will be the basis for the construction of new forecasting tools, which may be an important element of precision agriculture and the main element of decision support systems.

scholarly journals Estimating Biomass of Black Oat Using UAV-Based RGB Imaging

Agronomy ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 344 ◽  
Author(s):  
Matheus Gabriel Acorsi ◽  
Fabiani das Dores Abati Miranda ◽  
Maurício Martello ◽  
Danrley Antonio Smaniotto ◽  
Laercio Ricardo Sartor
Keyword(s):  
Cover Crops ◽  
Precision Agriculture ◽  
Temporal Variability ◽  
Prediction Models ◽  
Plant Biomass ◽  
Digital Terrain Model ◽  
Field Measurements ◽  
Growth Stages ◽  
Spatial And Temporal Variability ◽  
Growth Variability

The spatial and temporal variability of crop parameters are fundamental in precision agriculture. Remote sensing of crop canopy can provide important indications on the growth variability and help understand the complex factors influencing crop yield. Plant biomass is considered an important parameter for crop management and yield estimation, especially for grassland and cover crops. A recent approach introduced to model crop biomass consists in the use of RGB (red, green, blue) stereo images acquired from unmanned aerial vehicles (UAV) coupled with photogrammetric softwares to predict biomass through plant height (PHT) information. In this study, we generated prediction models for fresh (FBM) and dry biomass (DBM) of black oat crop based on multi-temporal UAV RGB imaging. Flight missions were carried during the growing season to obtain crop surface models (CSMs), with an additional flight before sowing to generate a digital terrain model (DTM). During each mission, 30 plots with a size of 0.25 m² were distributed across the field to carry ground measurements of PHT and biomass. Furthermore, estimation models were established based on PHT derived from CSMs and field measurements, which were later used to build prediction maps of FBM and DBM. The study demonstrates that UAV RGB imaging can precisely estimate canopy height (R2 = 0.68–0.92, RMSE = 0.019–0.037 m) during the growing period. FBM and DBM models using PHT derived from UAV imaging yielded R2 values between 0.69 and 0.94 when analyzing each mission individually, with best results during the flowering stage (R2 = 0.92–0.94). Robust models using datasets from different growth stages were built and tested using cross-validation, resulting in R2 values of 0.52 for FBM and 0.84 for DBM. Prediction maps of FBM and DBM yield were obtained using calibrated models applied to CSMs, resulting in a feasible way to illustrate the spatial and temporal variability of biomass. Altogether the results of the study demonstrate that UAV RGB imaging can be a useful tool to predict and explore the spatial and temporal variability of black oat biomass, with potential use in precision farming.

scholarly journals Tomato yield as a function of water depths and irrigation suspension periods

2019 ◽  
Vol 23 (8) ◽  
pp. 591-597 ◽  
Author(s):  
Cícero J. da Silva ◽  
José A. Frizzone ◽  
César A. da Silva ◽  
Adelmo Golynski ◽  
Luiz F. M. da Silva ◽  
...  
Keyword(s):  
Water Deficit ◽  
Block Design ◽  
Water Productivity ◽  
Irrigation Management ◽  
Total Yield ◽  
Crop Evapotranspiration ◽  
Industrial Processing ◽  
Tomato Yield ◽  
Subsurface Drip ◽  
Water Depths

ABSTRACT Irrigation management is essential for tomato fruits yield and quality. Therefore, the aim of this study was to evaluate the yield of tomatoes for industrial processing, ‘BRS Sena’ hybrid, subjected to water depths and irrigation suspension periods before harvest, irrigated by subsurface drip irrigation, in Goiás, Brazil (17º 49’ 19.5” S and 49º 12’ 11.3” W), in 2015 and 2016. The experiments were conducted under a randomized complete block design, with four replications, in split plots. Five irrigation levels (50, 75, 100, 125 and 150% of crop evapotranspiration) were evaluated in the plots and five irrigation suspension periods (0, 7, 14, 21 and 28 days before harvest) were evaluated in the subplots. At 125 days after transplanting the seedlings, the yields of green, mature, rotten fruits and total yield, water productivity and percentages of green, mature and rotten fruits were evaluated. The highest total fruit yields (105.86 and 58.60 t ha-1) were obtained with water replacements ranging from 125.47 (615.09 mm) to 132.11 (564.00 mm) of crop evapotranspiration, in the first and second year of experiment, respectively. Growing plants under water deficit and excess increased the incidence of rotten fruits and decreased that of mature fruits. Pre-harvest irrigation suspension reduced crop yield and incidence of green fruits and increased the incidence of rotten fruits. The highest water productivity by the crop occurred under water deficit, management that may be interesting for regions with water restrictions.

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