Grading Of Agricultural Products Research Articles

Fast and simultaneous prediction of inner quality parameters on intact mangos by near infrared spectroscopy: Impact of spectra pre-processing on prediction accuracy

Near infrared spectroscopy or known as NIRS has been widely employed in many fields including agriculture, especially for sorting and grading of agricultural products. Spectra pre-processing is one of the main factors affecting model accuracy and prediction capabilities of NIRS. The objective of the present study was to study the impact of different spectra corrections namely mean centering (MC), mean normalization (MN), de-trending (DT), multiplicative scatter correction (MSC), standard normal variate (SNV) and orthogonal signal correction (OSC), to the prediction accuracy of quality parameters: titratable acidity (TA) and soluble solids content (SSC) in intact mango. A total of 91 mango samples (cv. Kent) were used as dataset for calibration and external prediction which was separated by means of systematic sampling based on a property (SSBP) approach. Diffuse reflectance spectra (log1/R) were acquired and recorded in wavelength range of 1000 – 2500 nm by Antaris Fourier transform NIR instrument. Judging from calibration and prediction performance, MSC found to be the best spectra pre-processing method prior to prediction model development with R2 prediction are 0.72 for TA and 0.76 for SSC. Although MSC increase the prediction performances based on R2, RMSE, RPD and RER metrics compared to the baseline, the achieved RPD, 1.9 for TA and 1.8 for SSC of this findings are still poor and need improvements to achieve even higher levels of accuracy and reliability necessitates for real-time applications.

Development of effective model for non-destructive detection of defective kiwifruit based on graded lines.

The accurate detection of external defects in kiwifruit is an important part of postharvest quality assessment. Previous studies have not considered the problems posed by the actual grading environment. In this study, we designed a novel approach based on improved Yolov5 to achieve real-time and efficient non-destructive detection of multiple defect categories in kiwifruit. First, a kiwifruit image acquisition device based on grading lines was developed to enhance the image acquisition. Subsequently, a kiwifruit dataset was constructed based on the external defect characteristics and a new data enhancement method was proposed to augment the kiwifruit samples. Thereafter, the SPD-Conv and DW-Conv modules were combined to improve Yolov5s, with EIOU as the loss calculation function. The results demonstrated that the improved model training loss value was 0.013 lower, the convergence was accelerated, the number of parameters was reduced, and the computational effort was increased. The detection accuracies of the samples in the test set, which included healthy, leaf-rubbing damaged, healed cuts or scarred, and sunburned samples, were 98.8%, 98.7%, 97.6%, and 95.9%, respectively, with an overall detection accuracy of 97.7%. The detection time was 8.0 ms, thereby meeting real-time sorting demands. The average detection accuracy and model size of SSD, Yolov5s, Yolov7, and Yolov5-Ours were compared. When the confidence threshold was 0.5, the detection accuracy of Yolov5-Ours was 10% and 6.4% higher than that of SSD and Yolov5s, respectively. In terms of the model size, Yolov5-Ours was approximately 6.5- and 4-fold smaller than SSD and Yolov7, respectively. Thus, Yolov5-Ours achieved the highest accuracy, adaptability, and robustness for the detection of all kiwifruit categories as well as a small volume and portability. These results can provide technical support for the non-destructive detection and grading of agricultural products in the future.

Facilitated machine learning for image-based fruit quality assessment

Image-based machine learning models can be used to make the sorting and grading of agricultural products more efficient. In many regions, implementing such systems can be difficult due to the lack of centralization and automation of postharvest supply chains. Stakeholders are often too small to specialize in machine learning, and large training data sets are unavailable. We propose a machine learning procedure for images based on pre-trained Vision Transformers. It is easier to implement than the current standard approach of training Convolutional Neural Networks (CNNs) as we do not (re-)train deep neural networks. We evaluate our approach based on two data sets for apple defect detection and banana ripeness estimation. Our model achieves a competitive classification accuracy equal to or less than one percent below the best-performing CNN. At the same time, it requires three times fewer training samples to achieve a 90% accuracy.

Non-Destructive Measurement of Quality Parameters of Apple Fruit by Using Visible/Near-Infrared Spectroscopy and Multivariate Regression Analysis

The quality assessment and grading of agricultural products is one of the post-harvest activities that has received considerable attention due to the growing demand for healthy and better-quality products. Recently, various non-destructive methods have been used to evaluate the quality of agricultural products, which are very desirable and faster and more economical than destructive methods. Optical methods are one of the most important non-destructive methods that use the high speed of light detection and computer data processing and are able to evaluate the quality and classification of products with high accuracy. Among the optical methods, visible–near-infrared (Vis/NIR) spectroscopy is considered one of the most accurate methods. In this research, Vis/NIR spectroscopy technology was used in the spectral range of 350–1150 nm for non-destructive detection of some quality parameters including pH, TA, SSC, and TP of two varieties of Red Delicious and Golden Delicious apples. Various pre-processing models were developed to predict the parameters, which brought the desired results with high accuracy so that pH prediction results were for yellow apples (RMSEC = 0.009, rc = 0.991, SDR = 2.51) and for red apples (RMSEC = 0.005, rc = 0.998, SDR = 2.56). The results for TA were also (RMSEC = 0.003, rc = 0.996, SDR = 2.51) for red apples and (RMSEC = 0.001, rc = 0.998, SDR = 2.81) for yellow apples. The results regarding SSC were for red apples (RMSEC = 0.209, rc = 0.990 and SDR = 2.82) and for yellow apples (RMSEC = 0.054, SDR = 2.67 and rc = 0.999). In addition, regarding TP, the results were for red apples (RMSEC = 0.2, rc = 0.989, SDR = 2.05) and for yellow apples (RMSEC = 1.457, rc = 0.998, SDR = 1.61). The obtained results indicate the detection of the mentioned parameters with high accuracy by visible/infrared spectroscopic technology.

Intelligent System to Evaluate the Quality of DRC using Image Processing and then Categorize using Artificial Neural Network (ANN)

Background: In this digital era, agricultural science can significantly benefit from automation. More specifically, automated processes can be used to evaluate the quality of various fruits and vegetables. The main selling point of fruits and vegetables is their sensory characteristics, i.e., their appearance and smell as they significantly impact consumer choice and market value. Sorting and grading of agricultural products are carried out manually, which lead to inconsistency in results; besides, it can also be time-consuming, variable, subjective, onerous, expensive and prone to be influenced by surroundings. These factors build the case for an intelligent automated system to grade fruits and vegetables. Methods: This paper proposes an intelligent automated system that uses, Image Processing Techniques and Artificial Neural Network (ANN), to grade Dry Red Chillies(DRC). Result: A HSV based algorithm with the efficiency of 80.5 was developed and used to measure the various morphological characteristics of DRC and then clustered into various grades using ANN - Self-Organizing Map (SOM). .

Aflatoxin contaminated degree detection by hyperspectral data using band index

Aflatoxin is a highly toxic and carcinogenic substance with fluorescence characteristic. To explore the feasibility of detection the degree of aflatoxin contamination using hyperspectral imaging technology, we proposed a machine learning detection method based on support vector machine (SVM) combining band index and narrow band. First, five concentrations of aflatoxin solutions (10ug/L, 20ug/L, 50ug/L, 100ug/L and 10 mg/L) were prepared and dripped onto the surface of different peanut kernels. Next, hyperspectral images with 33 bands (400–720 nm) were acquired for each sample using a hyperspectral imaging system under 365 nm ultraviolet (UV) light. Then four fluorescence indexes including Radiation Index (RI), Difference Radiation Indexes (DRI), Ratio Radiation Index (RRI) and Normalized Difference Radiation Index (NDRI) were proposed. Finally, Fisher method was used to optimize and obtain a narrowband spectrum, and RBF-SVM model was used to recognize aflatoxin and make regression analysis on the degree of aflatoxin contamination. Experimental results showed that DRI index had the optimal performance, and the accuracy rate of 5-fold cross validation of SVM were 95.5% and the mean square error (MSE) and correlation coefficient R were respectively 0.0223 and 0.9785. Results of this paper are of positive significance for the online aflatoxin detection and grading of agricultural products.

Study and Application of Machine Vision Technique for Quality Detection of Agricultural Products

The study and application of machine vision technique for the detection of agricultural products, which includes the quality detection and grading of fruits, dried fruits, vegetables, are discussed. It summarizes the existing problems of detection and grading of agricultural products based on machine vision, and analyzes the methods of solving problems through establishing a national collaborative innovation center.

A Microwave Method for Grading Agricultural Products

This paper describes a microwave method for non-contact grading of agricultural products. An open-ended cavity is used as a sensor and requires no particular orientation of the product to be graded...

Book Reviews : Color Measurement and Its Application to the Grading of Agricultural Products. A Handbook on the Method of Disc Colorimetry. Dorothy Nickerson. Washington, U. S. Department of Agri culture, March, 1946. Misc. Pub. 580, 62 pp., illus. Price, 25¢

Book Reviews : Color Measurement and Its Application to the Grading of Agricultural Products. A Handbook on the Method of Disc Colorimetry. Dorothy Nickerson. Washington, U. S. Department of Agri culture, March, 1946. Misc. Pub. 580, 62 pp., illus. Price, 25¢

Artifial Daylighting for Color Grading of Agricultural Products

Artifial Daylighting for Color Grading of Agricultural Products