48 Global import shipment records of Fruit Ripening with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Banana Ripening Process Dataset and Model

This dataset contains images of the classes below:

• freshripe
• freshunripe
• overripe
• ripe
• rotten
• unripe

Usage

This is an object detection model that can be used to possibly identify where in the Fruit Ripening Process fruit at stores are and when to take them off the shelves and put them in composting.

Auxin response factors (ARFs) are transcription factors that play important roles in plants. ARF2 is a member of the ARF family and participates in many plant growth and developmental processes. However, the role of ARF2 in strawberry fruit quality remains unclear. In this study, FveARF2 was isolated from the woodland strawberry ‘Ruegen’ using reverse transcription-polymerase chain reaction (RT-PCR), which showed that FveARF2 expression levels were higher in the stem than in other organs of the ‘Ruegen’ strawberry. Moreover, FaARF2 was higher in the white fruit stage of cultivated strawberry fruit than in other stage. Subcellular localization analysis showed that FveARF2 is located in the nucleus, while transcriptional activation assays showed that FveARF2 inhibited transcription in yeast. Silencing FveARF2 in cultivated strawberry fruit revealed earlier coloration and higher soluble solid, sugar, and anthocyanin content in the transgenic fruit than in the control fruit, overexpression of FveARF2 in strawberry fruit delayed ripening and lower soluble solid, sugar, and anthocyanin content compared to the control fruit. Gene expression analysis indicated that the transcription levels of the fruit ripening genes FaSUT1, FaOMT, and FaCHS increased in FveARF2-RNAi fruit and decreased in FveARF2-OE fruit, when compared with the control. Furthermore, yeast one-hybrid (Y1H) and GUS activity experiments showed that FveARF2 can directly bind to the AuxRE (TGTCTC) element in the FaSUT1, FaOMT, and FaCHS promoters in vitro and in vivo. Potassium ion supplementation improved the quality of strawberry fruit, while silencing FveARF2 increased potassium ion content in transgenic fruit. The Y1H and GUS activity experiments also confirmed that FveARF2 could directly bind to the promoter of FveKT12, a potassium transporter gene, and inhibited its expression. Taken together, we found that FveARF2 can negatively regulate strawberry fruit ripening and quality, which provides new insight for further study of the molecular mechanism of strawberry fruit ripening.

Palm Fruit Ripeness Classificationcnn

## Overview

Palm Fruit Ripeness Classificationcnn is a dataset for classification tasks - it contains Palm Fruits annotations for 3,024 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Autophagy is a catabolic and recycling pathway that maintains cellular homeostasis under normal growth and stress conditions. Two major types of autophagy, microautophagy and macroautophagy, have been described in plants. During macroautophagy, cellular content is engulfed by a double-membrane vesicle called autophagosome. This vesicle fuses its outer membrane with the tonoplast and releases the content into the vacuole for degradation. During certain developmental processes, autophagy is enhanced by induction of several autophagy-related genes (ATG genes). Autophagy in crop development has been studied in relation to leaf senescence, seed and reproductive development, and vascular formation. However, its role in fruit ripening has only been partially addressed. Strawberry is an important berry crop, representative of non-climacteric fruit. We have analyzed the occurrence of autophagy in developing and ripening fruits of the cultivated strawberry. Our data show that most ATG genes are conserved in the genome of the cultivated strawberry Fragaria x ananassa and they are differentially expressed along the ripening of the fruit receptacle. ATG8-lipidation analysis proves the presence of two autophagic waves during ripening. In addition, we have confirmed the presence of autophagy at the cellular level by the identification of autophagy-related structures at different stages of the strawberry ripening. Finally, we show that blocking autophagy either biochemically or genetically dramatically affects strawberry growth and ripening. Our data support that autophagy is an active and essential process with different implications during strawberry fruit ripening.

Fruit Ripeness Identification

## Overview

Fruit Ripeness Identification is a dataset for classification tasks - it contains Ripeness Level annotations for 1,301 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Abstract Abscisic acid (ABA) regulates fruit ripening, yet little is known about the exact roles of ABA receptors in fruit. In this study, we revealed the role of SlPYL9, a tomato PYR (pyrabactin resistance) / PYL (pyrobactin resistance-like) / RCAR (regulatory component of ABA receptors) ABA receptor, as a positive regulator of ABA signaling and fruit ripening. SlPYL9 inhibited protein phosphatase-type 2C (PP2C2/6) in ABA dose-dependent way, and it interacts physically with SlPP2C 2 / 3 / 4 / 5 in an ABA-dependent manner. Expression of SlPYL9 was observed in the seeds, flowers and fruit. Overexpression and suppression of SlPYL9 induced a variety of phenotypes via altered expression of ABA signaling genes (SlPP2C1/2/9, SlSnRK2.8, SlABF2), thereby affecting expression of ripening-related genes involved in ethylene release and cell wall modification. SlPYL9-OE / RNAi plants showed a typical ABA hyper- / hypo-sensitive phenotype in terms of seed germination, primary root growth and the response to drought. Fruit ripening was significantly accelerated in SlPYL9-OE by 5-7 days as a result of increased endogenous ABA accumulation and advanced release of ethylene compared with the wild-type (WT). Meanwhile, in the SlPYL9-RNAi lines, fruit ripening was delayed, mesocarp thickness was enhanced, and the petals did not abscise as timely as the WT, resulting in conical / oblong and gourd-shaped fruit. These results suggest that SlPYL9 is involved in ABA signaling, thereby playing a role in the regulation of flower abscission and fruit ripening in tomato.

Fruits-360: A dataset of images containing fruits and vegetables

Version: 2024.08.02.0

A high-quality, dataset of images containing fruits and vegetables. The following fruits and vegetables are included: Apples (different varieties: Crimson Snow, Golden, Golden-Red, Granny Smith, Pink Lady, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red, Lady Finger), Beetroot Red, Blueberry, Cactus fruit, Cantaloupe (2 varieties), Carambula, Carrot, Cauliflower, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Chestnut, Clementine, Cocos, Corn (with husk), Cucumber (ripened, various), Dates, Eggplant (normal and long), Fig, Ginger Root, Granadilla, Grape (Blue, Pink, White (different varieties)), Grapefruit (Pink, White), Guava, Hazelnut, Huckleberry, Kiwi, Kaki, Kohlrabi, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango (Green, Red), Mangostan, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine (Regular, Flat), Nut (Forest, Pecan), Onion (Red, White), Orange, Papaya, Passion fruit, Peach (different varieties), Pepino, Pear (different varieties, Abate, Forelle, Kaiser, Monster, Red, Stone, Williams), Pepper (Red, Green, Orange, Yellow), Physalis (normal, with Husk), Pineapple (normal, Mini), Pitahaya Red, Plum (different varieties), Pomegranate, Pomelo Sweetie, Potato (Red, Sweet, White), Quince, Rambutan, Raspberry, Redcurrant, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red, Yellow, not ripened, Heart), Walnut, Watermelon, Zucchini (white and dark).

Dataset properties Total number of images: 94110.

Training set size: 70491 images (one fruit or vegetable per image).

Test set size: 23619 images (one fruit or vegetable per image).

Number of classes: 141 (fruits, vegetables and nuts).

Image size: 100x100 pixels.

Filename format: image_index_100.jpg (e.g. 32_100.jpg) or r_image_index_100.jpg (e.g. r_32_100.jpg) or r2_image_index_100.jpg or r3_image_index_100.jpg. "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis. "100" comes from image size (100x100 pixels).

Different varieties of the same fruit (apple for instance) are stored as belonging to different classes.

Repository structure Folders Training and Test contain images for training and testing purposes.

Alternate download The Fruits-360 dataset can be downloaded from:

https://www.kaggle.com/moltean/fruits

https://github.com/fruits-360

How to cite Mihai Oltean, Fruits-360 dataset, 2017-.

License: MIT License

Copyright (c) 2017- Mihai Oltean

Fruit ripening is an important process that affects fruit quality. A QTL in melon, ETHQV6.3, involved in climacteric ripening regulation, has been found to be encoded by CmNAC-NOR, a homologue of the tomato NOR gene. To further investigate CmNAC-NOR function, we obtained two CRISPR/Cas9-mediated mutants (nor-3 and nor-1) in the climacteric Védrantais background. nor-3, containing a 3-bp deletion altering the NAC domain A, resulted in ~8 days delay in ripening without affecting fruit quality. In contrast, the 1-bp deletion in nor-1 resulted in a fully disrupted NAC domain, which completely blocked climacteric ripening. The nor-1 fruits did not produce ethylene, no abscission layer was formed and there was no external color change. Additionally, volatile components were dramatically altered, seeds were not well developed and flesh firmness was also altered. There was a delay in fruit ripening with the nor-1 allele in heterozygosis of ~20 days. Our results provide new information regarding the function of CmNAC-NOR in melon fruit ripening, suggesting that it is a potential target for modulating shelf life in commercial climacteric melon varieties.

A dataset of coffee (C. Arabica ) cherries at various levels of fruit maturity.

There are 5 classes: * unripe * semi_ripe * ripe * overripe * dry

Una base de datos de frutos del cafeto (C. Arabica ) en diferentes niveles de maduración.

Hay 5 categorías: * unripe * semi_ripe * ripe * overripe * dry

This dataset is a data set of oil palm FFB images taken directly from trees in commercial oil palm plantations in Indonesia. The dataset focuses on categorizing oil palm fruits into five stages of fruit maturity: Unripe (early stage fruit), Underripe (transitional phase fruit), Ripe (ripe fruit ready to harvest), Flower (representing the flowering stage), and Abnormal (fruit with typical characteristics due to potential disease or irregularity). This dataset has been validated by oil palm experts in categorizing the level of fruit maturity. It has been pre-processed so that it can be used for developing applications for detecting the level of FFB maturity in the plantation and calculating the number of fruits on oil palm trees with various methods and approaches. Various interests can use this dataset for research and application development such as students, lecturers, researchers, mobile-based application developers, machine learning and deep learning engineers, data science engineers, oil palm post-harvest experts, and other oil palm researchers. This dataset is useful for application development, application testing, and model validation of mobile-based applications or applications embedded in robots. The dataset was meticulously compiled in Central Kalimantan Province, Indonesia, through video recordings at 30 frames per second within an oil palm plantation. We have collected a total of 440 videos, each with varying lengths ranging from 8 seconds to 1 minute and 31 seconds. These videos are captured in a resolution of 320x640 pixels, providing a portrait orientation. The datasets have been split into data training, validation, and testing using composition 70:20:10 with the total images being 10207 for training, 2896 for validation, and 1400 for testing.

Cucumber Fruit Maturity

## Overview

Cucumber Fruit Maturity is a dataset for object detection tasks - it contains Fruits annotations for 2,027 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset consists of raw video recordings capturing oil palm Fresh Fruit Bunches (FFB) directly from trees in commercial plantations in Indonesia. The dataset provides an unprocessed look at oil palm fruits in various stages of maturity, including Unripe, Underripe, Ripe, Flower, and Abnormal, each representing different phases of the fruit's development, from early growth to full ripeness, with some exhibiting irregular characteristics due to potential disease. Expert validation has confirmed the accuracy of the maturity categorizations. Recorded at 30 frames per second, the videos were captured using a smartphone, with the operator circling the trees to document all visible FFB from multiple angles. The dataset comprises 440 videos, each lasting between 8 seconds and 1 minute and 31 seconds, with a resolution of 320x640 pixels in MP4 format. This ensures the data's compatibility with various digital media platforms. Several factors contribute to the variability in the recorded footage. The height of the oil palm trees varies, leading to different perspectives in the videos. Additionally, the light intensity differs across the videos, with some appearing brighter than others due to variations in sunlight exposure among the trees. Beyond its primary use in assessing fruit maturity, this raw footage has significant potential for further research and technological development. It can serve as valuable training data for machine learning models focused on automated fruit maturity detection, support the advancement of precision agriculture techniques, and contribute to the development of robotic harvesting solutions.

Overview

• Introduction
• Object Classes
  • Unripe
  • Harvest

Introduction

This dataset focuses on the detection of citrus fruits at different stages of ripeness. The objective is to assist in automated harvesting by distinguishing between unripe and ripe (harvestable) fruits. The dataset contains two classes: - Unripe: Citrus fruits that are not yet ready for harvest. - Harvest: Ripe citrus fruits that are ready to be harvested.

Object Classes

Unripe

Description

The unripe class consists of citrus fruits that have not fully developed the characteristics of ripe fruits. These are typically smaller and may have a more muted appearance, not exhibiting the full color transformation that is seen in ripe fruits.

Instructions

• Locate all unripe fruits within the frame. These are usually smaller in size and lack the vibrant coloration of ripe fruits.
• Draw bounding boxes tightly around each unripe fruit, making sure to capture the entire fruit. Do not include surrounding leaves or stems unless they obscure the boundary of the fruit.
• Do not annotate fruits that are partially visible if less than half of the fruit is visible or if the fruit is not distinguishable.
• Avoid labeling fruits that are shadowed or occluded to the extent that their ripeness is indeterminable.

Harvest

Description

The harvest class includes citrus fruits that are fully ripe and ready for picking. These fruits are generally larger and exhibit a bright, saturated appearance, indicating readiness for harvest.

Instructions

• Identify all ripe fruits in the image. These fruits often appear prominently within the foliage.
• Create bounding boxes around each harvestable fruit, ensuring to encompass the full visible extent of the fruit. Focus on the distinctive round shape and substantial size.
• Exclude any elements that do not belong to the fruit itself, such as branches and surrounding leaves, unless they obscure the fruit boundary.
• Do not annotate any fruits that are significantly obscured or lack sufficient size and shape details to confirm ripeness.

Overview

• Introduction
• Object Classes
  • Unripe
  • Harvest

Introduction

This dataset is designed for the task of detecting and differentiating between unripe and ripe fruits within images. The goal is to correctly identify and label fruits as either "Unripe" or "Harvest" based on their visual characteristics.

• Unripe: Fruits that are not yet fully developed and show characteristics of immaturity.
• Harvest: Fruits that are fully ripe and ready for picking.

Object Classes

Unripe

Description

Unripe fruits are those that have not yet reached full maturity. They tend to have less vibrant colors and may appear smaller or less round compared to ripe fruits.

Instructions

• Annotate unripe fruits by drawing a bounding box that tightly encloses the entire visible fruit.
• Focus on fruits that lack full color saturation, often appearing in shades that are less vibrant compared to ripe fruits.
• Do not label any part of the fruit that is significantly occluded unless at least 50% of the fruit is visible.
• Ensure that each annotated fruit stands out as not being fully developed or mature.

Harvest

Description

Harvest fruits are those that have reached full maturity and show signs of being ready for picking. These fruits generally have a bright and consistent color.

Instructions

• Annotate harvest fruits by carefully drawing a bounding box around the entire visible fruit area.
• Focus on fruits that have a deep, vibrant color, indicating ripeness and readiness for harvest.
• If two fruits are closely overlapping, draw separate bounding boxes if each fruit can be individually distinguished.
• Do not label any fruit that does not clearly exhibit signs of full ripeness.

These instructions serve to guide annotators in identifying and accurately labeling fruits based on their stages of maturity, ensuring a consistent and reliable dataset for detection tasks.

A forward estimate of mango fruit harvest volume and scheduling is required for farm management, for organization in terms of labour planning and market sales. Harvest timing estimation in mango production is currently achieved using accumulated growing degree days (GDD) from from early stages of flower development, non-destructive estimates of fruit dry matter content by handheld near infra-red spectrometry, and destructive assessment of internal flesh colour. For fruit load estimation, current best practice involves manual counting of total fruit per tree. A range of technologies are becoming available that have relevance to assessment of mango crop harvest timing and fruit load forecast. Four activities were undertaken to assess relevant technologies: (i) A hardware system based on LoRa connected temperature sensors was characterised and recommended for field use based on measurement accuracy, battery life and reception range. An alternative algorithm on GDD calculation involving use of a function that penalises high temperatures as well as low temperatures was demonstrated to better predict harvest maturity in warmer climates. Required heat units (GDD, Tb = 12 °C, TB =32 °C) to achieve maturity were documented as 2185, 1728 and 1740 for the cultivars Keitt, Calypso, and Honey Gold, respectively. (ii) Vis-NIR spectrometry was trialled for non-invasive assessment of fruit flesh colour in the context of harvest maturity estimation, using a data set of 2034 spectra from 19 populations, where a population is an orchard/season/flowering event. The best leave-one-out-population cross validation prediction result was obtained using a Support Vector Regression (R2 of 0.63 and RMSEP of 5.52 on CIE B). However, this performance was inadequate for recommendation for use in non-invasive assessment of fruit maturity, which requires estimation to within 2.0 CIE B units. (iii) A procedure for prediction of fruit size at harvest based on measurements made prior to harvest was established, based a linear growth model for weight increment. The procedure was demonstrated for Honey Gold, Calypso and Keitt populations, with estimation error of 8.64 ± 13.7% and 0.61 ± 4.7% for measurements made between either five and four, or four and three weeks before harvest, respectively. (iv) A procedure for use of in-field machine vision-based count of fruit on tree in estimation of orchard fruit load was established, based on use of imaging on two dates to capture fruit arising from different flowering events. The two imaging estimations were accurate estimates of total orchard fruit load as measured by packhouse count, with R2 of 0.98 and slope of 0.99 across six orchards. These four activities demonstrate the potential of new technologies for improved estimation of harvest timing and load.

Apple fruit mealiness is one of the most important textural problems that results from an undesirable ripening process during storage. This phenotype is characterized by textural deterioration described as soft, grainy and dry fruit. Despite several studies, little is known about mealiness development and the associated molecular events. In this study, we integrated phenotypic, microscopic, transcriptomic and biochemical analyses to gain insights into the molecular basis of mealiness development.ResultsInstrumental texture characterization allowed the refinement of the definition of apple mealiness. In parallel, a new and simple quantitative test to assess this phenotype was developed.Six individuals with contrasting mealiness were selected among a progeny and used to perform a global transcriptome analysis during fruit development and cold storage. Potential candidate genes associated with the initiation of mealiness were identified. Amongst these, the expression profile of an early do...

6266 Global exporters importers export import shipment records of Ripe fruit with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Two crops for each cultivar, red sweet peppers (cultivar: Banji; seed company: Efal) and yellow sweet peppers (cultivar: Liri Seed; company: Hazera), were harvested in a commercial greenhouse in Camehin located in southwest Israel. The first crop (denoted as ‘Set1’) was harvested from the 12th fruit setting on November 2017 and the second crop (denoted as ‘Set2’) from the 16th fruit setting on January 2019. The peppers were selected from four maturity classes. The peppers were initially classified into immature (classes 1 and 2, defined as ‘not to be harvested’) and mature (classes 3 and 4, defined as ‘to be harvested’) peppers by professional human harvesters in the greenhouse. The mature peppers were manually harvested in the morning of the experiment by the harvesters, and then were randomly chosen for the experiment by two expert observers and classified into classes 3 and 4 according to surface color (95–100% colored were defined as class 4; all others were defined as class 3). Classes 1 and 2 were manually harvested by the observers according to surface color (0–5% colored were defined as class 1; all others were defined as class 2).

For set 1 the third letter in each file name indicates the maturity level of the pepper. (A-class 1, B-class2, C-class 3, D-class 4) For set 2 the L_ on the file name indicates the maturity level of the pepper (for example R_L2_P10_S1 is a pepper from class 2)