AIMC Topic: Fruit

Clear Filters Showing 31 to 40 of 219 articles

A Synergistic Approach Using Photoacoustic Spectroscopy and AI-Based Image Analysis for Post-Harvest Quality Assessment of Conference Pears.

Molecules (Basel, Switzerland)
This study presents a non-invasive approach to monitoring post-harvest fruit quality by applying CO laser photoacoustic spectroscopy (COLPAS) to study the respiration of "Conference" pears from local and commercially stored (supermarket) sources. Con...

Unleashing the power of AI in predicting the technological and phenolic maturity of pomegranates cultivated in Lebanon.

Scientific reports
The harvesting time of pomegranates is crucial for maximizing their health benefits and market value. However, traditional methods often fail to consider the intricate interactions between environmental conditions and fruit maturity. This study is th...

YOLOv11n for precision agriculture: lightweight and efficient detection of guava defects across diverse conditions.

Journal of the science of food and agriculture
BACKGROUND: Automated fruit defect detection plays a critical role in improving postharvest quality assessment and supporting decision-making in agricultural supply chains. Guava defect detection presents specific challenges because of diverse diseas...

A comparative analysis of machine learning approaches for predicting maturity in watermelon using acoustic and quality features.

Food chemistry
This study investigated the integration of acoustic and destructive quality features for machine learning-based classification of watermelon maturity into three categories: immature, mature, and over-mature. Nine machine learning algorithms were eval...

Comparing machine learning models to chemometric ones to detect food fraud: A case study in Slovenian fruits and vegetables.

Food chemistry
We present a method for comparing models used to detect food fraud based on stable isotopes and trace element (SITE) levels. Existing modeling procedures generally do not provide an uncertainty estimate on a model's performance due to variations in t...

Sweet pepper yield modeling via deep learning and selection of superior genotypes using GBLUP and MGIDI.

Scientific reports
Intelligent knowledge about Capsicum annuum L. germplasm could lead to effective management of germplasm. Here, 29 accessions of sweet pepper were investigated in two separate randomized complete block design with three replications in the field cond...

SmartBerry for AI-based growth stage classification and precision nutrition management in strawberry cultivation.

Scientific reports
Agriculture is vital for human sustenance and economic stability, with increasing global food demand necessitating innovative practices. Traditional farming methods have caused significant environmental damage, highlighting the need for sustainable p...

Hyperspectral Imaging and Deep Learning for Quality and Safety Inspection of Fruits and Vegetables: A Review.

Journal of agricultural and food chemistry
Quality inspection of fruits and vegetables linked to food safety monitoring and quality control. Traditional chemical analysis and physical measurement techniques are reliable, they are also time-consuming, costly, and susceptible to environmental a...

Computer Vision in Monitoring Fruit Browning: Neural Networks vs. Stochastic Modelling.

Sensors (Basel, Switzerland)
As human labour is limited and therefore expensive, computer vision has emerged as a solution with encouraging results for monitoring and sorting tasks in the agrifood sector, where conventional methods for inspecting fruit browning that are generall...

A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production.

Sensors (Basel, Switzerland)
The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth of trees and extensive orchard work are threatening the profitability of apples. This study review...