AIMC Topic: Fruit

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Abnormal phenotypic defects detection of jujube using explainable machine learning enhanced computer vision.

Journal of food science
Jujube is susceptible to biotic and abiotic adversity stresses resulting in abnormal phenotypic defects. Therefore, abnormal phenotype fruits should be removed during postharvest sorting to increase added value. An improved maximum horizontal diamete...

Machine learning and multiple linear regression models can predict ascorbic acid and polyphenol contents, and antioxidant activity in strawberries.

Journal of the science of food and agriculture
BACKGROUND: Strawberry is a rich source of antioxidants, including ascorbic acid (ASA) and polyphenols, which have numerous health benefits. Antioxidant content and activity are often determined manually using laboratory equipment, which is destructi...

RSM and ANN-Based Optimized Ultrasound-Assisted Extraction of Functional Components from Olive Fruit (cv Arbequina): Assessment of Antioxidant Attributes and GC-MS Metabolites Profiling.

Chemistry & biodiversity
The current study devises an optimized ethanolic extraction for efficient recovery of high-value components from Pakistani olives (cv. Arbequina) using response surface methodology (RSM) and artificial neural networking (ANN). Four factors such as ti...

Developing Machine Vision in Tree-Fruit Applications-Fruit Count, Fruit Size and Branch Avoidance in Automated Harvesting.

Sensors (Basel, Switzerland)
Recent developments in affordable depth imaging hardware and the use of 2D Convolutional Neural Networks (CNN) in object detection and segmentation have accelerated the adoption of machine vision in a range of applications, with mainstream models oft...

Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies.

PloS one
Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral refl...

Modeling and Optimization of Ellagic Acid from Chebulae Fructus Using Response Surface Methodology Coupled with Artificial Neural Network.

Molecules (Basel, Switzerland)
The dried ripe fruit of Retz. is a common Chinese materia medica, and ellagic acid (EA), isolated from the plant, is an important bioactive component for medicinal purposes. This study aimed to delineate the optimal extraction parameters for extract...

Pseudo-random Number Generator Influences on Average Treatment Effect Estimates Obtained with Machine Learning.

Epidemiology (Cambridge, Mass.)
BACKGROUND: The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator.

Determination of soluble solids content in tomatoes with different nitrogen levels based on hyperspectral imaging technique.

Journal of food science
Tomato is sweet and sour with high nutritional value, and soluble solids content (SSC) is an important indicator of tomato flavor. Due to the different mechanisms of nitrogen uptake and assimilation in plants, exogenous supply of different forms of n...

Fruit-In-Sight: A deep learning-based framework for secondary metabolite class prediction using fruit and leaf images.

PloS one
Fruits produce a wide variety of secondary metabolites of great economic value. Analytical measurement of the metabolites is tedious, time-consuming, and expensive. Additionally, metabolite concentrations vary greatly from tree to tree, making it dif...

DeepDate: A deep fusion model based on whale optimization and artificial neural network for Arabian date classification.

PloS one
PURPOSE: As agricultural technology continues to develop, the scale of planting and production of date fruit is increasing, which brings higher yields. However, the increasing yields also put a lot of pressure on the classification step afterward. Im...