Dataset Information

Non-destructive optical monitoring of grape maturation by proximal sensing.

ABSTRACT: A new, commercial, fluorescence-based optical sensor for plant constituent assessment was recently introduced. This sensor, called the Multiplex(®) (FORCE-A, Orsay, France), was used to monitor grape maturation by specifically monitoring anthocyanin accumulation. We derived the empirical anthocyanin content calibration curves for Champagne red grape cultivars, and we also propose a general model for the influence of the proportion of red berries, skin anthocyanin content and berry size on Multiplex(®) indices. The Multiplex(®) was used on both berry samples in the laboratory and on intact clusters in the vineyard. We found that the inverted and log-transformed far-red fluorescence signal called the FERARI index, although sensitive to sample size and distance, is potentially the most widely applicable. The more robust indices, based on chlorophyll fluorescence excitation ratios, showed three ranges of dependence on anthocyanin content. We found that up to 0.16 mg cm(-2), equivalent to approximately 0.6 mg g(-1), all indices increase with accumulation of skin anthocyanin content. Excitation ratio-based indices decrease with anthocyanin accumulation beyond 0.27 mg cm(-2). We showed that the Multiplex(®) can be advantageously used in vineyards on intact clusters for the non-destructive assessment of anthocyanin content of vine blocks and can now be tested on other fruits and vegetables based on the same model.

SUBMITTER: Ben Ghozlen N

PROVIDER: S-EPMC3231004 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Non-destructive optical monitoring of grape maturation by proximal sensing.

Ben Ghozlen Naïma N Cerovic Zoran G ZG Germain Claire C Toutain Sandrine S Latouche Gwendal G

Sensors (Basel, Switzerland) 20101109 11

A new, commercial, fluorescence-based optical sensor for plant constituent assessment was recently introduced. This sensor, called the Multiplex(®) (FORCE-A, Orsay, France), was used to monitor grape maturation by specifically monitoring anthocyanin accumulation. We derived the empirical anthocyanin content calibration curves for Champagne red grape cultivars, and we also propose a general model for the influence of the proportion of red berries, skin anthocyanin content and berry size on Multip ...[more]

PMID: 22163456

Similar Datasets

Project description:Reliability and economic assessment of the Multiplex® optical sensor employed for non-destructive grape parameters estimates was evaluated in 2017 against a wet chemistry approach in mature vineyards including five cultivars (two whites, two red and one pink colored) assuring a broad range of variation in both technological and phenolic maturity parameters. Among the five Multiplex (Mx) indices evaluated (SFR_R, FLAV, FLAV-UV, ANTH_RG and FERARI) in all cultivars with the exception of Barbera SFR_R showed a significant and linear correlation with total soluble solids (TSS) for TSS ≥ 10 °Brix. Conversely, no significant correlation was found between FLAV and total flavonols concentration, whereas a significant linear correlation was found in Barbera (R2 = 0.66) and Ervi (R2 = 0.63) when the FLAV index was replaced with the FLAV_UV index. Within each cultivar, both ANTH_RG and FERARI showed close correlations with total anthocyanins concentration determined by wet chemistry although under different model shapes. Expressing berry color accumulation on a per skin mass basis rather than for whole berry mass basis, allowed for better separation of behavior of single cultivars and improved accuracy of model fitting for the combination of Barbera and ANTH_RG. A strict linear correlation was always found, within each index, for Mx readings taken on the two opposite sides of the same cluster, implying no significant within-cluster differences in sugar, color and flavonol concentrations. Economic assessment of Mx by means of the Net Present Value (NPV) approach showed that Mx is economically viable for a two hectare vineyard cultivating three red grape varieties (90 samples per year) if its lifetime is at least 7 years. Conversely, if only two red varieties are grown Mx should be used at least 11 years to make it economic suitable. Bigger properties growing a higher number of red varieties are the more interested in Mx as the expected NPV assumes positive values with a Mx usage of minimum 3 years.

Project description:Plant breeders, scientists, and commercial producers commonly use growth rate as an integrated signal of crop productivity and stress. Plant growth monitoring is often done destructively via growth rate estimation by harvesting plants at different growth stages and simply weighing each individual plant. Within plant breeding and research applications, and more recently in commercial applications, non-destructive growth monitoring is done using computer vision to segment plants in images from the background, either in 2D or 3D, and relating these image-based features to destructive biomass measurements. Recent advancements in machine learning have improved image-based localization and detection of plants, but such techniques are not well suited to make biomass predictions when there is significant self-occlusion or occlusion from neighboring plants, such as those encountered under leafy green production in controlled environment agriculture. To enable prediction of plant biomass under occluded growing conditions, we develop an end-to-end deep learning approach that directly predicts lettuce plant biomass from color and depth image data as provided by a low cost and commercially available sensor. We test the performance of the proposed deep neural network for lettuce production, observing a mean prediction error of 7.3% on a comprehensive test dataset of 864 individuals and substantially outperforming previous work on plant biomass estimation. The modeling approach is robust to the busy and occluded scenes often found in commercial leafy green production and requires only measured mass values for training. We then demonstrate that this level of prediction accuracy allows for rapid, non-destructive detection of changes in biomass accumulation due to experimentally induced stress induction in as little as 2 days. Using this method growers may observe and react to changes in plant-environment interactions in near real time. Moreover, we expect that such a sensitive technique for non-destructive biomass estimation will enable novel research and breeding of improved productivity and yield in response to stress.

Dataset Information

Non-destructive optical monitoring of grape maturation by proximal sensing.

Publications

Non-destructive optical monitoring of grape maturation by proximal sensing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets