There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to 80 % .

/pdf/deep-learning-based-defective-bean-inspection-with-gan-3xl7x5175q.pdf

Deep-learning-based defective bean inspection with GAN-structured automated labeled data augmentation in coffee industry

This article presents a preliminary study on the analysis of samples of Colombian coffee for the detection and classification of defects (i.e. using “Cup Tests”), which was conducted in Almacafe quality control laboratory Almacafe in Cucuta, Colombia). The results obtained show the application of an Electronic Nose (EN), called “A‐NOSE,” used in the coffee sector for the cupping tests. The results show that e‐nose technology can be a useful tool as quality control of coffee grain.

Electronic Nose for Quality Control of Colombian Coffee Through the Detection of Defects in “Cup Tests”

Computer vision based food grain classification: A comprehensive survey

The quality of coffee beans differs from each other based on the geographic locations of its sources. The coffee bean quality is conventionally determined by visual inspection, which is subjective, requiring considerable effort and time and prone to error. This calls for the development of an alternative method that is precise, non-destructive and objective. This paper was conducted with the objective of developing an appropriate computer routine that can characterize coffee beans from the different towns of Cavite, Philippines. Imaging techniques were employed to automatically classify the coffee bean samples according to their specie. Important coffee bean features based in morphology such as area of the bean, perimeter, equivalent diameter, and percentage of roundness were extracted from 195 training images and 60 testing images. Artificial neural network (ANN) and K nearest neighbor (KNN) were employed to automatically categorize the coffee beans. Using ANN, classification scores of 96.66% were achieved while using KNN the following classification scores were achieved 84.12%(k=1), 84.10%(k=2), 81.53%(k=3), 82.56%(k=4), 75.38%(k=5),80.35% (k=6), 38.79%(k=7), 77.44%(k=8), 72.82%(k=9) and 78.45% (k=10). In conclusion, the results of this study have revealed that imaging technique could be used as an effective method to classify coffee bean species. ANN is the more preferred method over KNN in classifying coffee beans.

Classification of coffee bean species using image processing, artificial neural network and K nearest neighbors

The quality of a coffee bean is determined by several factors including color, texture, and size. High quality beans are carefully refined where defects, such as black beans, are removed. The assessment through visual inspection may be subjected to external factors such as light and the amount of beans to be inspected. This study presents a method of controlling the coffee bean quality using Image Processing techniques. Normal beans are identified through the extraction of RGB color components of training image. The RGB values were integrated in an image processing technique that eliminated the black beans in the image. Using the technique in this study, a classification of 100% were achieved for eliminating the black beans in the testing images.

An image processing technique for coffee black beans identification

Cryptography, which involves the use of a cipher, describes a process of encrypting information so that its meaning is hidden and thus, secured from those who do not know how to decrypt the information. Cryptography algorithms come with the various types including the symmetric key algorithms and asymmetric key algorithms. In this paper, the authors applied the most commonly used algorithm, which is the RSA algorithm together with the Chaos system and the basic security device employed in the worldwide organizations which is the Data Encryption Standard (DES) with the objective to make a hybrid data encryption. The advantage of a chaos system which is its unpredictability through the use of multiple keys and the secrecy of the RSA which is based on integer factorization’s difficulty is combined for a more secure and reliable cryptography. The key generation was made more secure by applying the DES schedule to change the keys for encryption. The main strength of the proposed system is the chaotic variable key generator that chages the value of encrypted message whenever a different number of key is used. Using the provided examples the strength of security of the proposed system was tested and demonstrated.

https://beei.org/index.php/EEI/article/download/627/929

Chaotic Rivest-Shamir-Adlerman Algorithm with Data Encryption Standard Scheduling

Civet coffee is regarded as the most expensive coffee in the world. Owing to its high price the conventional method of harvesting civet coffee in the wild has been replaced by a farming method, wherein the wild civet cats are being captured, caged, and then force-fed with ripe, hand-picked, coffee cherries. This is contrary to the production of wild civet coffee from the ripest and sweetest coffee cherries, carefully picked by the civet cat. As the wild civet coffee is quite hard to obtain, most of the civet coffee commercially sold as authentic civet coffee is actually from a caged civet cat. Traders and consumers have no way of differentiating the civet coffee from other types of coffee. This study aimed to differentiate caged civet coffee beans from ordinary green coffee beans. The technique used was the near infrared spectroscopy (NIRS) as it is nondestructive and can generate quick results. A total of 218 samples were scanned, which generated absorbance in the entire 780 wavelength capacity of the Cavite State University Indium, Gallium and Arsenic (CvSU InGaAs)-based NIR instrument, ranging from 904 to 1684 nanometres (nm). A total of 170,040 spectra were generated. The average spectral absorbance’s having major differences between the two groups, were chosen, which are 907nm, 1088 nm, 1540 nm and 1650 nm, respectively. Out of 218 samples , 130 samples were used as training data, 40 samples as testing data, and the remaining 48 samples for validation purposes. The training data were subjected to the 4 layers, 15 neurons feed forward back propagation artificial neural network (FFBPANN) for training. Classification scores of 95% to 100% were achieved. Using the combined NIRS and FFBPANN, the civet coffee can be successfully discriminated from coffee beans not eaten by a civet.

https://accentsjournals.org/PaperDirectory/Journal/IJACR/2018/11/2.pdf

Discrimination of civet coffee using near infrared spectroscopy and artificial neural network

The judgement of meat quality through image processing with automatic fuzzy logic based classifier is proposed. Grading of meat will be based on the marbling formation present on the sample carcasses. The number of clusters of marbling and their areas will be obtained using MatLab Image Processing Tool. The resulting fat-to-meat ratio will then be used as the input parameter to the fuzzy logic controlled classifier system. The paper generally aims to produce an automatic system that will be able to process, analyze and sort the meat samples according to their palatability condition.

Edwin R. Arboleda

Papers

Classification of coffee bean species using image processing, artificial neural network and K nearest neighbors

An image processing technique for coffee black beans identification

Chaotic Rivest-Shamir-Adlerman Algorithm with Data Encryption Standard Scheduling

Discrimination of civet coffee using near infrared spectroscopy and artificial neural network

Meat Marbling Scoring Using Image Processing with Fuzzy Logic Based Classifier