Fruit Quality Assessment Research Articles

“AgriVision” A Flutter -Based Application for RealTime Detection of Pesticides Residues on Fruits

“AgriVision” is a Flutter-based mobile application designed to address the critical issue of pesticide contamination on fruits. The application utilizes real-time image processing and machine learning techniques to detect pesticide residues on the surface of various fruits, ensuring food safety and healthstandards. By capturing and analyzing images through a smartphone camera, the app provides instant and reliablefeedback on the presence of harmful chemicals. The user-friendly interface, combined with the portability of the solution, offers farmers, consumers, and vendors a practical tool to assess fruit quality and promote safer agricultural practices. This project aims to contribute to healthier food choices and enhance consumer safety. Key Words: AgriVision , Flutter application , Real-time detection , Pesticide residues , Fruits , Agricultural technology , Pesticide detection , Mobile app , Food safety , AgriTech , Image processing , Machine learning , Smart agriculture , Fruit quality analysis.

Rating pome fruit quality traits using deep learning and image processing.

Quality assessment of pome fruits (i.e. apples and pears) is used not only for determining the optimal harvest time but also for the progression of fruit-quality attributes during storage. Therefore, it is typical to repeatedly evaluate fruits during the course of a postharvest experiment. This evaluation often includes careful visual assessments of fruit for apparent defects and physiological symptoms. A general best practice for quality assessment is to rate fruit using the same individual rater or group of individual raters to reduce bias. However, such consistency across labs, facilities, and experiments is often not feasible or attainable. Moreover, while these visual assessments are critical empirical data, they are often coarse-grained and lack consistent objective criteria. Granny, is a tool designed for rating fruit using machine-learning and image-processing to address rater bias and improve resolution. Additionally, Granny supports backward compatibility by providing ratings compatible with long-established standards and references, promoting research program continuity. Current Granny ratings include starch content assessment, rating levels of peel defects, and peel color analyses. Integrative analyses enhanced by Granny's improved resolution and reduced bias, such as linking fruit outcomes to global scale -omics data, environmental changes, and other quantitative fruit quality metrics like soluble solids content and flesh firmness, will further enrich our understanding of fruit quality dynamics. Lastly, Granny is open-source and freely available.

Frequency selection method with support vector machine to minimize the impact of individual differences on the electrical impedance spectra of fruits

Individual differences in electrical impedance spectroscopy (EIS) parameters is a technical bottleneck in fruit quality detection. To achieve non-destructive fruit quality testing and minimize the impact of individual differences, this paper proposes a frequency selection + support vector machine strategy based on the frequency response characteristics of biological tissues and the path of current flow. This study found obvious differences in the EIS parameters of different samples. The measurement stability was affected by the electrode type and frequency. The destructive needle electrode was much less affected by individual differences than the non-destructive patch electrode. The effect of low frequencies was greater than that of high frequencies. The heterogeneity of the fruit peel and pulp and the contact area between the electrode and peel were the main reasons for individual differences. When detecting banana maturity, the highest precision of the models was 98.9% by proposed methods; however, the generalizability of the model and its application for other fruit quality tests still require further verification and optimization. The results promote the practical application of EIS in the field of fruit quality assessment and provide new insights for the quality evaluation of other agricultural products.

Simplified Deep Learning for Accessible Fruit Quality Assessment in Small Agricultural Operations

Simplified Deep Learning for Accessible Fruit Quality Assessment in Small Agricultural Operations

Quality analysis and assessment of representative sea buckthorn fruits in northern China

Sea buckthorn (SB) primarily grows in northern China and is rich in nutritional components, making it popular among consumers. This study aims to select suitable SB varieties for processing by analyzing physicochemical components, color, taste, and volatile compounds. The results showed that the physicochemical content of Chinese SB from Gansu were as follows: total soluble solids 13.50 ± 0.37°Brix, titratable acidity 6.46 ± 0.39 %, ascorbic acid 578 mg/100 g, polyphenols 517 mg/100 g, and flavonoids 194 mg/100 g, which were higher than those of the other four SB samples; the content of organic acids was relatively abundant. Taste analysis via electronic tongue indicated that Chinese SB had the highest ANS (sweetness) value and the lowest SCS (bitterness) value, exhibiting the richest flavor. Gas chromatography-mass spectrometry analysis showed that Gansu Chinese SB had a rich variety of volatile components, totaling 74. In summary, Gansu Chinese SB is a variety suitable for processing.

Evaluating the shelf life of strawberries using a portable Vis-NIR spectrophotometer and a Reflectance Quality Index (RQI)

This study aimed to develop a postharvest quality index for strawberry (Fragaria ananassa Duch.) based on Vis-NIR reflectance, using a portable handset spectrometer to assess fruit quality and establish maximum shelf life. Fresh strawberries were stored at different temperatures (5, 15, 25 and 35 °C) and at 60 % relative humidity for up to 15 days. Various quality parameters were evaluated, including colour, firmness, weight loss, appearance, and microbial quality, every three days during storage. Kinetic models were used to describe the changes in weight loss, appearance, colour, and firmness of strawberries during storage under different temperatures. Vis-NIR spectroscopy was employed to establish a Reflectance Quality Index (RQI) for determining fruit quality during storage. This index helped to reduce the high number of wavelengths to a lower set of representative independent variables. Calibration and prediction models were created using the partial least squares (PLS) regression method, incorporating cross-validation, to predict quality indicators of strawberries. These models were developed for predicting appearance, weight loss, L*, a*, b* colour parameters, and firmness. A very good fit was obtained (R2 = 0.95), demonstrating the feasibility of both the RQI and PLS for evaluating strawberry quality. This study presents a novel approach for assessing the shelf life of strawberries based on storage time and temperature using a portable spectrophotometer. This method can be a valuable tool for the food industry to ensure optimal fruit quality and minimize food losses.

Optimizing ‘Red Fuji’ apple quality: Auxin-mediated calcium distribution via fruit-stalk in bagging practices

In apples, a bottleneck effect in calcium (Ca) transport within fruit stalk has been observed. To elucidate that how auxin affects Ca forms and distribution in the apple fruit stalk, we investigated the effects of different concentrations of auxin treatment (0, 10, 20, and 30 mg·L−1) on Ca content, forms, distribution, and fruit quality during later stages of fruit expansion. The results showed that auxin treatment led to a dramatic reduction in total Ca content in stalk, while an approximately 30 % increase in fruit. Furthermore, auxin treatment effectively enhanced the functionality of xylem vessels in vascular bundles of the stalk in bagged apples. Finally, TOPSIS method was used to assess fruit quality, with treatments ranked as follows: IAA20 > NAA20 > IAA30 > IAA10 > CK > NPA. The findings lay a foundation for further studies on the bottleneck in Ca transport within stalk, uneven distribution of Ca in fruit, and provide insights into Ca utilization efficiency in bagged apples.

Capacitive Impedance Analysis for Noncontact Assessment of Fruit Quality and Ripening

Capacitive Impedance Analysis for Noncontact Assessment of Fruit Quality and Ripening

Recent Advances in Light Penetration Depth for Postharvest Quality Evaluation of Fruits and Vegetables.

Light penetration depth, as a characteristic parameter reflecting light attenuation and transmission in biological tissues, has been applied in nondestructive detection of fruits and vegetables. Recently, with emergence of new optical detection technologies, researchers have begun to explore methods evaluating optical properties of double-layer or even multilayer fruit and vegetable tissues due to the differences between peel and pulp in the chemical composition and physical properties, which has gradually promoted studies on light penetration depth. A series of demonstrated research on light penetration depth could ensure the accuracy of the optical information obtained from each layer of tissue, which is beneficial to enhance detection accuracy for quality assessment of fruits and vegetables. Therefore, the aim of this review is to give detailed outlines about the theory and principle of light penetration depth based on several emerging optical detection technologies and to focus primarily on its applications in the field of quality evaluation of fruits and vegetables, its future applicability in fruits and vegetables and the challenges it may face in the future.

Development of a non-destructive fruit quality assessment utilizing odour sensing, expert vision and deep learning algorithm

Development of a non-destructive fruit quality assessment utilizing odour sensing, expert vision and deep learning algorithm

Analysis of the Pomelo Peel Essential Oils at Different Storage Durations Using a Visible and Near-Infrared Spectroscopic on Intact Fruit.

Pomelo fruit pulp mainly is consumed fresh and with very little processing, and its peels are discarded as biological waste, which can cause the environmental problems. The peels contain several bioactive chemical compounds, especially essential oils (EOs). The content of a specific EO is important for the extraction process in industry and in research units such as breeding research. The explanation of the biosynthesis pathway for EO generation and change was included. The chemical bond vibration affected the prediction of EO constituents was comprehensively explained by regression coefficient plots and x-loading plots. Visible and near-infrared spectroscopy (VIS/NIRS) is a prominent rapid technique used for fruit quality assessment. This research work was focused on evaluating the use of VIS/NIRS to predict the composition of EOs found in the peel of the pomelo fruit (Citrus maxima (J. Burm.) Merr. cv Kao Nam Pueng) following storage. The composition of the peel oil was analyzed by gas chromatography-mass spectrometry (GC-MS) at storage durations of 0, 15, 30, 45, 60, 75, 90, 105 and 120 days (at 10 °C and 70% relative humidity). The relationship between the NIR spectral data and the major EO components found in the peel, including nootkatone, geranial, β-phellandrene and limonene, were established using the raw spectral data in conjunction with partial least squares (PLS) regression. Preprocessing of the raw spectra was performed using multiplicative scatter correction (MSC) or second derivative preprocessing. The PLS model of nootkatone with full MSC had the highest correlation coefficient between the predicted and reference values (r = 0.82), with a standard error of prediction (SEP) of 0.11% and bias of 0.01%, while the models of geranial, β-phellandrene and limonene provided too low r values of 0.75, 0.75 and 0.67, respectively. The nootkatone model is only appropriate for use in screening and some other approximate calibrations, though this is the first report of the use of NIR spectroscopy on intact fruit measurement for its peel EO constituents during cold storage.

Electrochemical fingerprint profiling for quality assessment of Kadsura coccinea fruit

Electrochemical fingerprint profiling for quality assessment of Kadsura coccinea fruit

Non-Destructive Measurement on Kwini Mango Fruit using Capacitive Sensing Technique at 250kHz to 2MHz for Ripeness Determination

This study aims to evaluate the characteristics and quality of Kwini mango fruit using non-destructive capacitive sensing methods. Unlike other fruits, identifying ripe Kwini mangoes can be challenging due to their green appearance and unfamiliar characteristics to consumers. Despite the growing interest in non-destructive fruit quality assessment techniques, there has not been significant prior exploration of capacitive sensing in the context of Kwini mangoes. Therefore, in this experiment, a parallel plate was used to measure the capacitance of Kwini mango fruit at frequencies ranging from 250 kHz to 2 MHz. Results showed that the moisture content and pH level decreased within 15 days of investigation. This technique was able to predict Kwini maturity with significant precision by applying the capacitance formula. Because of its high R2 value and sensitivity in linear regression, 500kHz was determined to be the most efficient operating frequency for Kwini mango fruits among the frequencies evaluated. In this experiment, the relationship between capacitance and Brix also yielded encouraging findings. The study also includes the development of a GUI application for determining the sweetness and maturity of Kwini mangoes. Overall, this research demonstrates the potential of capacitive sensing technology as a non-destructive approach to measuring Kwini mango fruit ripeness.

Recent developments in time domain NIRS for non-destructive fruit quality assessment: non-contact measurements and device miniaturization

Recent developments in time domain NIRS for non-destructive fruit quality assessment: non-contact measurements and device miniaturization

Fresh Fruit Detection Using Deep Learning

The Fresh Fruit Identification System constitutes a notable advancement in the meals industry. Employing sophisticated technologies along with machine-getting-to-know and computer imagination and prescient, it complements the great evaluation and optimization of perishable items, mainly focusing on culmination and greens. This contemporary system employs advanced gadgets gaining knowledge of models, especially convolutional neural networks (CNNs), to meticulously analyze high-decision pics of processed meals. By rapidly and appropriately identifying objects based totally on attributes that include shade, texture, and shape, the machine guarantees no longer the simplest precision in categorization but also an in-intensity best evaluation. This entails defect detection and the assessment of ripeness, making sure that the most effective rate-satisfactory merchandise reaches customers. The implementation of sturdy and scalable systems has adeptly addressed demanding situations associated with facts, model education, and real-time processing. Consequently, the Fresh Food Identification System emerges as a pivotal contributor to raising the standards of the perishable meals supply chain, embodying a dedication to excellence and performance. Keywords— Fresh Fruit Detection, Machine Learning, Computer Vision, Fruit Quality Assessment, Food Safety, Image Processing,Classification Algorithms, Object Detection, Feature Extraction, Deep Learning,Convolutional Neural Networks (CNN).

FruitVision: A deep learning based automatic fruit grading system

Abstract Quality assessment of fruits plays a key part in the global economy’s agricultural sector. In recent years, it has been shown that fruits are affected by different diseases, which can lead to widespread economic failure in the agricultural industry. Traditional manual visual grading of fruits could be more accurate, making it difficult for agribusinesses to assess quality efficiently. Automatic grading of fruits using computer vision has become a prominent area of study for many researchers. In this study, a deep learning-based model called FruitVision is proposed for the automatic grading of various fruits. The results showed that FruitVision performed all the existing models and obtained an accuracy of 99.42, 99.50, 99.24, 99.12, 99.38, 99.38, 99.17, 98.86, and 97.96% for the apple, banana, guava, lime, orange, pomegranate, Ajwa date, Mabroom date, and mango, respectively, using 5-fold cross-validation. This is a remarkable achievement in the field of AI-based fruit grading systems.

Preservation effects of chitosan/polyvinyl alcohol/clove essential oil antifungal film on yellow peaches: Physicochemical properties and fruit quality assessment

Using antifungal food packaging films presents a significant opportunity to prolong the shelf-life of food products. In this work, a chitosan-polyvinyl alcohol antibacterial film, enriched with clove essential oil (CS/PVA/CEO), was developed for preservation effects on yellow peaches, and its physical properties and structure were investigated. The good compatibility and thermal stability of CS/PVA/CEO film were demonstrated via scanning electron microscopy, Fourier-transform infrared spectroscopy, and thermogravimetric analysis. Mechanical properties are exhibited at a 0.75% CEO concentration: the tensile strength is (25.09 ± 0.32) MPa, elongation at break is (291.23 ± 2.53)%, water vapor permeability is (0.479 ± 0.005) g·mm·m−2·h−1·kPa−1, water resistance is (18.57 ± 0.35)%, and water resistance is (9.70 ± 0.26)%. During the 11 days of storage at temperatures ranging from 23 °C to 26 °C and 26 days of storage at 4 °C–5 °C, the CS/PVA/CEO film exhibited effective preservation of yellow peach fruits. CS/PVA/CEO group extended 3–4 d at room temperature and 5–10 d at 4 °C of yellow peaches preservation compared with the CS/PVA and control groups. This effectiveness was evaluated based on various parameters, including fruit appearance, decay rate, respiratory intensity, malondialdehyde concentration, firmness, total soluble solids, and titratable acidity. Therefore, adding CEO into the antifungal film demonstrated excellent potential in enhancing the preservation of yellow peaches.

FRUIT FRESHESS PREDICTION USING CNN

This Project proposes a methodology utilizing Convolutional Neural Networks (CNNs) for the holistic assessment of fruit quality, encompassing freshness, ripeness, and health status. By integrating CNNs with multi-feature analysis, including image data and spectral signatures, the model aims to provide accurate classification of fruits into categories of fresh/rotten, ripe/unripe, and healthy/diseased. The methodology outlines data collection, preprocessing, feature extraction, CNN model development, evaluation metrics, and potential applications. Ensuring fruit quality is crucial for consumer satisfaction and food safety. Manual inspection is time-consuming and subjective, necessitating automated systems. This methodology proposes a CNN-based approach for comprehensive fruit quality assessment, addressing freshness, ripeness, and health status simultaneously. Key Words: CNN , Fresh/Rotten ,Fruit Quality, Deep Learning.

Electrical Properties for Non-destructive Determination of Free Fatty Acid and Moisture Content in Oil Palm Fruit

FFA content is one of the essential qualities of oil palm fruit. FFA content exceeding 5% is considered unsuitable for human consumption. Commonly, FFA content is determined by chemical methods in the laboratory, but it is destructive, time-consuming, and costly. Several non-destructive methods have been investigated, but a maturity prediction approach has been primarily used with no direct relation to FFA content. Thus, there is a pressing need for a non-destructive framework to assess the FFA levels in the oil palm fruit directly. An attempt has been explored using a non-destructive palm fruit quality assessment that relied on electrical properties (impedance, admittance, resistance, and capacitance) in the frequency range from 50 Hz to 5 MHz was investigated to predict FFA and moisture content directly. Two statistical analyses were employed: stepwise multiple linear regressions (MLR) and artificial neural networks (ANN) to calibrate and validate electrical properties with FFA and moisture content. The best-performing models ANN showcased significant results: r= 0.96, R2=0.92, SEC at 0.86%, SEP at 0.97%, CV at 19.45%, consistency at 88.54, and RPD at 3.43 for FFA prediction, and r= 0.99, R2=0.98, SEC at 3.09 %, SEP at 3.46 %, CV at 5.44 %, consistency at 89.08 and RPD at 7.02 for predicting moisture content. In modeling oil palm quality determination, applying the ANN method significantly improved model performances, demonstrating its efficacy in predicting non-destructively both FFA levels based on admittance and moisture content based on impedance.

Novel Optimizer MAdam for Efficient Fruit Grading and Quality Assessment Using CNN-LSTM

Novel Optimizer MAdam for Efficient Fruit Grading and Quality Assessment Using CNN-LSTM