Forecast: Fresh Tomatoes Consumption in the US 2022 - 2026 Discover more data with ReportLinker!

Forecast: Tomatoes Consumption in the US 2022 - 2026 Discover more data with ReportLinker!

In these datasets you may find information regarding dry matter content (Table 1), the consumption of nutrient solution in a soil-less tomato crop (Table 2) and the plant tissues composition (Tables 3-7).

Overview

• Introduction
• Object Classes
  • Green Tomatoes
  • Red Tomatoes

Introduction

This dataset focuses on the identification and annotation of tomatoes at different ripening stages for object detection tasks. The goal is to accurately label and distinguish between green and red tomatoes.

Classes: - Green Tomatoes: Unripe tomatoes typically not ready for consumption. - Red Tomatoes: Ripe tomatoes ready for harvest and consumption.

Object Classes

Green Tomatoes

Description

Green tomatoes are unripe and have a consistent green appearance. Their surface is typically smooth and firm, often with visible green stems and calyxes.

Instructions

• Identify and annotate each green tomato, including those that are partially occluded but can still be clearly identified by their green color and smooth surface.
• Include the entire visible area of the tomato, but do not include the stems and leaves unless they are part of the visible surface of the tomato.
• Do not label tomatoes that are turning yellow or show any significant red coloration.

Red Tomatoes

Description

Red tomatoes are ripe, displaying a consistent red or orange-red color over most of the fruit. They are typically smooth and firm, often showing a glossy surface.

Instructions

• Identify and annotate each red tomato, considering tomatoes with red or orange-red coloration as part of this class.
• Annotate the entire visible fruit, ensuring that the bounding box encloses the round shape of the tomato but excludes stems and leaves unless they cover part of the tomato's surface.
• Do not include tomatoes that are predominantly green or just starting to blush with red. Use the dominant color as a guide for classification.

Global Tomatoes Market Size Volume Per Capita by Country, 2023 Discover more data with ReportLinker!

Forecast: Fresh Tomatoes Consumption per Capita in Canada 2022 - 2026 Discover more data with ReportLinker!

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

Screening of Jordan BF: (a) tomato 1+3 growers Region: Jordan Valley
Farm holder grower (own their farms and consider this as their sole living source)
Indeterminate tomato under greenhouse/tunnels
Commercial grower for export and local consumption
Labors hands needs (=labor shortage)
Adaptability to acquire innovative solutions - Innovative growers
High level of tech adoption (They are willing to leverage their skills and to adopt new technologies, learning their stuff on how to spray pesticides, how to use the best fertilizer program in order to have better yields.)
Drip irrigation use Price constraint and price sensitive growers
Need to identify benchmark farms that have similar size but adopt technological practices. Rotation with the same crop is common (= screening criteria).
Adopt Syngenta products and services (only for RF)

(b) tomato 2 growers Region: Safi Area
Farm holder grower (own their farms and consider this as their sole living source)
Open field Determinate Tomato farm
Commercial grower for local consumption and for export (to Gulf countries)
Labors hands needs (=labor shortage)
Adaptability to acquire innovative solutions
High level of tech adoption (They are willing to leverage their skills and to adopt new technologies, learning their stuff on how to spray pesticides, how to use the best fertilizer program in order to have better yields.)
Drip irrigation use Price constraint and price sensitive growers
Need to identify benchmark farms that have similar size but adopt technological practices. Rotation with watermelon is common (=screening criteria).

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

United States CPI U: AW: FB: Food: Home: FV: Fresh: Vegetables: Tomatoes data was reported at 0.090 % in 2017. This records an increase from the previous number of 0.084 % for 2016. United States CPI U: AW: FB: Food: Home: FV: Fresh: Vegetables: Tomatoes data is updated yearly, averaging 0.092 % from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 0.138 % in 2004 and a record low of 0.075 % in 2011. United States CPI U: AW: FB: Food: Home: FV: Fresh: Vegetables: Tomatoes data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I011: Consumer Price Index: Urban: Weights (Annual).

Get Daily/weekly data of Wholesale prices of Tomato. Price is monitored for essential commodities based on data collected from 75 market centres spread across the country representing North, West, East, South and North-eastern regions of the country. Price Monitoring Cell (PMC) in the Department of Consumer Affairs is responsible for monitoring prices of selected essential commodities. The Quality and variety of the item for which prices are reported may vary from centre to centre but remains the same for a given centre. Generally, prices are reported for the Fair Average Quality of the item for a given centre. Every centre has a standard quality and variety of item for which prices are reported by them.

DL datasets representing the number of counts released by tomato fruits in a time interval of 30 s after excitation.

Raw dataset associated with the publication:

Breeding tomato flavor: modeling consumer preferences of tomato landraces.

Villena, J.^a, Moreno, C.^a, Roselló, S.^b, Beltran, J. ^c, Cebolla-Cornejo, J.^d, Moreno, M.M.^a*

^aUniversity of Castilla-La Mancha, Higher Technical School of Agricultural Engineering in Ciudad Real, Ronda de Calatrava 7, 13071, Ciudad Real, Spain

^bJoint Research Unit UJI-UPV ‐ Improvement of agri‐food quality. Agricultural Sciences and Natural Environment Department, Universitat Jaume I, Avda. Sos Baynat s/n, 12071 Castelló de la Plana, Spain

^c Research Institute for Pesticides and Water (IUPA). Universitat Jaume I, Avda. Sos Baynat s/n, 12071 Castelló de la Plana, Spain

^dJoint Research Unit UJI-UPV ‐ Improvement of agri‐food quality. COMAV. Universitat Politècnica de València, Cno. de Vera s/n, 46022 València, Spain

*Corresponding author

To be published in the journal Scientia horticulturae

The data in this dataset is a spatial inventory of urban agriculture (UA) carried out in the city of Milan (Italy). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.

License

Creative Commons CC-BY

Disclaimer

Despite our best efforts to validate the data, some information may be incorrect.

Description of the dataset

Typologies of UA

Residential garden: Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.

Community garden: A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.

Urban farm: Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus on arable crops, horticulture, vineyard, olive groves and orchard.

Institutional garden: Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).

Illegal garden: Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.

Nurseries: A large area subdivided into multiple plots managed for growing ornamental plants and flowers.

Land use typologies

Horticulture: annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).

Vineyard: grape vines grown in order to produce wine or table grape.

Olive groves: olive trees grown in order to produce olive oil or table olives.

Orchards: mixed trees such as orange, stone fruit, pome fruit, olive trees.

Mixed crops: an area grown with a mix of horticulture crops and fruit trees, not divisible.

Nurseries: ornamental plants, trees, flowers.

Credit

Pulighe G., Lupia F. (2019) Multitemporal Geospatial Evaluation of Urban Agriculture and (Non)-Sustainable Food Self-Provisioning in Milan, Italy. Sustainability 2019, 11(7), 1846

https://www.mdpi.com/2071-1050/11/7/1846

Motivation The data in this dataset is a spatial inventory of urban agriculture (UA) carried out in the city of Rome (Italy) (Grande Raccordo Anulare (GRA)). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps. License Creative Commons CC-BY Disclaimer Despite our best efforts to validate the data, some information may be incorrect. Description of the dataset Typologies of UA Residential garden: Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes. Community garden: A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common. Urban farm: Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus exclusively on horticulture, vineyard, olive groves and orchard. Institutional garden: Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.). Illegal garden: Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption. Land use typologies Horticulture: annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers). Vineyard: grape vines grown in order to produce wine or table grape. Olive groves: olive trees grown in order to produce olive oil or table olives. Orchards: mixed trees such as orange, stone fruit, pome fruit, olive trees. Mixed crops: an area grown with a mix of horticulture crops and fruit trees, not divisible. Credit Pulighe G., Lupia F. (2016) Mapping spatial patterns of urban agriculture in Rome (Italy) using Google Earth and web-mapping services. Land Use Policy 59(2016) 49-58. www.sciencedirect.com/science/article/pii/S0264837716300059 {"references": ["Pulighe G., Lupia F. (2016) Mapping spatial patterns of urban agriculture in Rome (Italy) using Google Earth and web-mapping services. Land Use Policy 59 (2016) 49\u201358"]}

Autonomous Greenhouse Challenge, Second Edition (2019-2020)

[Version2] Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.

posted on 23.09.2020 by S. (Silke) Hemming, H.F. (Feije) de Zwart, A. (Anne) Elings, A. (Anna) Petropoulou, Isabella Righini

https://data.4tu.nl/articles/dataset/Autonomous_Greenhouse_Challenge_Second_Edition_2019_/12764777

What is AGC?

The goal of the Hackathon was to optimize net profit of a virtual tomato crop grown in a virtual greenhouse. For that tomato yield and product quality, thus income, had to be maximized on one side, while the use of resources such as energy, CO2 and water, thus costs, had to be minimized on the other side. The optimization had to be done using models of WUR of a virtual greenhouse and tomato crop using artificial intelligence algorithms.

https://www.wur.nl/en/project/autonomous-greenhouses-2nd-edition.htm

Introduction of Team in AGC

• The Automators
• AICU
• IUA.CAAS
• Digilog
• Automatoes
• Etc.

Documents for Dataset

1. Economics This document reports the prices necessary for the calculation of net profit in the Autonomous Greenhouse Challenge 2020.
2. ReadMe This document explains the following in details:
3. Parameters' description
4. Weather data
5. Greenhouse climate
6. Etc.

Content

The dataset contains data on outdoor and indoor greenhouse climate, irrigation, status of actuators, requested and realized climate setpoints, resource consumption, harvest, crop-related parameters, tomato quality, analysis of irrigation and drain samples and root-zone/slab information. Data were collected during a 6-month cherry tomato production (cv. Axiany) in 6 high-tech glasshouse compartments, located at the Wageningen Research Centre in Bleiswijk (The Netherlands). The dataset was produced during the second edition of Autonomous Greenhouse Challenge. This competition sees five international teams - consisting of scientists, professionals and students with multi-disciplinary expertise - challenging themselves in order to make a large step towards the Autonomous Greenhouse. The teams' names are: The Automators, AICU, IUA.CAAS, Digilog and Automatoes. The teams developed their own intelligent algorithms and used them to determine the set points for climate, irrigation and a number of cultivation-related parameters and control the production of cherry tomato crop remotely. The teams objective was to maximize net profit, by minimizing use of resources (e.g. water, nutrients, energy -heating and electricity- CO2) while optimizing income as a function of production and fruit quality. The achievements in AI-controlled compartments were compared with a reference compartment, operated manually by three Dutch commercial growers (named Reference). The dataset contains raw and processed data. Raw data were collected via climate measuring boxes and sensors, climate and irrigation process computer, weather station, manual registrations (performed by the greenhouse staff).

Acknowledgements

Wageningen University & Research, Greenhouse Horticulture, and DIGILOG

Inspiration

Can you use the data to grow crops better than the competition teams?

This submission derives from Task 5.3, "Farmers’ and Consumers’ Preferences and Behavioural Economics Underpinning Underutilised Crops" (T5.3), of the Horizon 2020 project "Realising Dynamic Value Chains for Underutilised Crops – RADIANT" (2021–2025). It consists of six datasets derived from discrete choice experiment (DCE) surveys conducted online with farmers and consumers to investigate their preferences for underutilised crops and derived food products, respectively.

We use an ‘XXYYZZ’ sequence to name the files as follows:
(1) 'XX' = two letters indicating whether the respondents are farmers (FA) or consumers (CO);
(2) 'YY' = two letters indicating the European country where the data were collected (BG = Bulgaria, ES = Spain, IT = Italy, SC = Scotland, United Kingdom);
(3) 'ZZ' = two letters indicating the underutilised crop or food product considered (BB = Bere barley, HT = hanging tomatoes, ID = IDEAL tomato, PR = Parmigiano Reggiano cheese, YP = yellow pea [and einkorn wheat]).

e.g., 'COITPR' = Consumers data from Italy on the Parmigiano Reggiano product

Each dataset consists of one sheet called "survey" (one row per respondent), which reports socio-demographic characteristics and attitudes of the respondent, as well as other information derived from the complementary survey; one sheet called "choices" (18 rows per respondent), which reports their choices in the DCE; and one sheet called "metadata", which describes all the variables (one table per data sheet).

The farmers' dataset also includes a sheet called "PGG" (one row per respondent), which includes data on a public good game administered to a randomly selected subset of farmers as part of the survey.

The total number of consumer respondents is 299 for Bere barley, 345 for hanging tomatoes, 336 for IDEAL tomato, 346 for yellow pea (and einkorn wheat), and 340 for Parmigiano Reggiano cheese. The number of farmer respondents is 331, of whom 186 participated in the PGG (220 and 108 when considering only high-quality responses based on restrictive checks).

European Fresh Consumption Purpose Tomatoes Harvested Production Share by Country (Thousand Metric Tons), 2023 Discover more data with ReportLinker!

Forecast: Tomatoes Pulp, Paste and Puree Consumption per Capita in Canada 2022 - 2026 Discover more data with ReportLinker!

Wholesale Price: Average: Tomato: All India data was reported at 1,754.900 INR/Quintal in Nov 2018. This records an increase from the previous number of 1,727.110 INR/Quintal for Oct 2018. Wholesale Price: Average: Tomato: All India data is updated monthly, averaging 1,583.630 INR/Quintal from Jan 2009 (Median) to Nov 2018, with 112 observations. The data reached an all-time high of 4,999.490 INR/Quintal in Aug 2017 and a record low of 475.000 INR/Quintal in Jan 2009. Wholesale Price: Average: Tomato: All India data remains active status in CEIC and is reported by Department of Consumer Affairs. The data is categorized under India Premium Database’s Price – Table IN.PC064: Wholesale Price: Department of Consumer Affairs: Agriculture Commodities: Monthly Average: by Cities: Tomato.

Forecast: Canned Tomatoes Consumption per Capita in Canada 2022 - 2026 Discover more data with ReportLinker!

Retail Price: DCA: Month End: Tomato: South Zone: Chennai data was reported at 23.000 INR/kg in Sep 2023. This records a decrease from the previous number of 30.000 INR/kg for Aug 2023. Retail Price: DCA: Month End: Tomato: South Zone: Chennai data is updated monthly, averaging 19.000 INR/kg from Jan 2009 (Median) to Sep 2023, with 170 observations. The data reached an all-time high of 187.000 INR/kg in Jul 2023 and a record low of 5.000 INR/kg in Mar 2011. Retail Price: DCA: Month End: Tomato: South Zone: Chennai data remains active status in CEIC and is reported by Department of Consumer Affairs. The data is categorized under India Premium Database’s Price – Table IN.PC134: Retail Price: Department of Consumer Affairs: Agriculture Commodities: Month End: by Cities: Tomato (Discontinued).