scholarly journals METODE KLASIFIKASI MUTU GREENBEAN KOPI ARABIKA LANANG DAN BIASA MENGGUNAKAN K-NEAREST NEIGHBOR BERDASARKAN BENTUK

2020 ◽  
Vol 18 (2) ◽  
pp. 1
Author(s):  
Dedy Ikhsan ◽  
Ema Utami ◽  
Ferry Wahyu Wibowo
Keyword(s):  
Image Processing ◽  
Classification System ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Random Data ◽  
Arabica Coffee ◽  
Trial Process ◽  
Classification Feature ◽  
Quality Classification ◽  
Sorting Process

During this time, the Greenbean coffee sorting process is still done manually which still has many shortcomings. Manually, this result is classified in inappropriate and inconsistent classification results due to human negligence. Grading in the processing and marketing sectors is important. Inappropriate grading opposes farmers simply because Lanang and ordinary Arabica coffee are the same. Hence, we need a consistent classification system. This research uses image processing to recognize Greenbean Arabica coffee. K-NN (K-Nearest Neighbor) method is used for a quality classification. This research will classify Arabica Greenbean coffee into 4 quality classes, namely intact Lanang Arabica, broken Lanang Arabica, intact ordinary Arabica, and ordinary broken Arabica. The search of trial process shows that K-NN classification feature is able to recognize Arabica coffee Greenbean into 4 classes with an accuracy value of 63.5%, very good at recognizing 90% of regular Arabica intact and 97% of whole Arabica intact. However, it is still weak in recognizing broken coffee Greenbean based on its type. The area feature is better in recognizing Arabica coffee Greenbean based on 4 classes with an accuracy of 69.8%. This research obtains 120 datasets from 80 tested data trains and 40 tested random data.

scholarly journals METODE KLASIFIKASI MUTU JAMBU BIJI MENGGUNAKAN KNN BERDASARKAN FITUR WARNA DAN TEKSTUR

2017 ◽  
Vol 6 (2) ◽  
pp. 113
Author(s):  
Taftyani Yusuf Prahudaya ◽  
Agus Harjoko
Keyword(s):  
Nearest Neighbor ◽  
Texture Features ◽  
Psidium Guajava ◽  
K Nearest Neighbor ◽  
Average Value ◽  
Quality Classification ◽  
Defect Area ◽  
Class B ◽  
Sorting Process

Guava (Psidium guajava L.) is a fruit that has many health benefits. Guava also has commercial value in Indonesia and has a large market share. This indicates that the commodity of guava has been consumed by society extensively. This time the sorting process is still done manually which still has many shortcomings. This classification gives the classification results are less accurate and inconsistent due to the carelessness of humans. Grading process in the marketing sector is essential. Improper grading potentially detrimental to farmers because all the fruit quality were priced the same. Therefore, we need a consistent classification system.The system uses image processing to extract the color and texture features of guava. As a quality classification KNN method (K-Nearest Neighbor) is used. This system will classify guava into four quality classes, namely the super class, class A, class B, and external quality. KNN designed with input 7 features extraction which is the average value of RGB (Red, Green, and Blue), total defect area, and the GLCM value (entropy, homogeneity, and contrast) with the 4 outputs of quality. From the test results showed that the classification method is able to classify the quality of guava. The highest accuracy is obtained in testing K = 3 with 91.25% accuracy rate.

Crop Disease Detection Using Data Science Techniques

2021 ◽  
pp. 80-97
Author(s):  
Shakti Kumar
Keyword(s):  
Data Science ◽  
Nearest Neighbor ◽  
Plant Diseases ◽  
K Nearest Neighbor ◽  
Classification Feature ◽  
Crop Disease ◽  
Using Data ◽  
Agricultural Yields ◽  
Day By Day

Plant disease is a mutilation of the normal state of a plant that changes its essential quality and prevents a plant from performing to its actual potential. Due to drastic environment changes, plant diseases are growing day by day, which results the higher losses in quantity of agricultural yields. To prevent the loss in the crop yield, the timely disease identification is necessary. Monitoring the plant diseases without any digital mean makes it difficult to identify the disease correctly and timely. It requires more amounts of work, time, and great experience in the plant diseases. Automatic approach of image processing and applying the different data science techniques to classify the disease correctly is a good idea for this which includes acquisition, classification, feature extraction, pre-processing, and segmentation all are performed on the leaf images. This chapter will briefly discuss the data science techniques used for the classification of the images like SVM, k-nearest neighbor, decision tree, ANN, and convolutional neural network (CNN).

scholarly journals Detection of Diabetic Retinopathy by Retinal Screening using Image Processing Techniques

2020 ◽  
pp. 447-457
Author(s):  
Shaziya Banu S ◽  
Ravindra S
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Nearest Neighbor ◽  
Extreme Condition ◽  
Diabetic Patients ◽  
K Nearest Neighbor ◽  
Image Processing Techniques ◽  
Primary Driver ◽  
Processing Techniques

<p>Diabetic Retinopathy (DR) is a related malady with diabetes and primary driver of sightlessness in diabetic patients. Epidemiological overview categorizes DR among four significant reasons for sight impedance. DR is a microvascular entanglement in which meager retinal veins may blast, bringing about vision misfortune. In this condition veins in retina swells and may blast in severe extreme condition. Operative medication is timely discovery by steady screenings that is by emphasizing the determination of retinal images using appropriate image processing techniques such as, Preprocessing of retinal image, image segmentation using sobel edge detector, local features extraction like mean, standard deviation, variance, Entropy, histogram values and so on. For classification of retina, system uses K-Nearest Neighbor (KNN) classifier. By adopting this approach, The classification of normal and abnormal images of retina is easy and will reduce the number of reviews for the ophthalmologists. Developing a method to automate functionality of retinal examination helps doctor to identify patient’s condition on disease. So that they can medicate the disease accordingly.</p>

scholarly journals Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System

2019 ◽  
Vol 9 (19) ◽  
pp. 4195 ◽  
Author(s):  
García ◽  
Candelo-Becerra ◽  
Hoyos
Keyword(s):  
Image Processing ◽  
Nearest Neighbor ◽  
Vision System ◽  
Coffee Bean ◽  
Green Coffee ◽  
K Nearest Neighbor ◽  
Green Coffee Beans ◽  
Coffee Beans ◽  
Supervised Learning Algorithms ◽  
K Nearest Neighbor Algorithm

There is an increased industry demand for efficient and safe methods to select the best-quality coffee beans for a demanding market. Color, morphology, shape and size are important factors that help identify the best quality beans; however, conventional techniques based on visual and/or mechanical inspection are not sufficient to meet the requirements. Therefore, this paper presents an image processing and machine learning technique integrated with an Arduino Mega board, to evaluate those four important factors when selecting best-quality green coffee beans. For this purpose, the k-nearest neighbor algorithm is used to determine the quality of coffee beans and their corresponding defect types. The system consists of logical processes, image processing and the supervised learning algorithms that were programmed with MATLAB and then burned into the Arduino board. The results showed this method has a high effectiveness in classifying each single green coffee bean by identifying its main visual characteristics, and the system can handle several coffee beans present in a single image. Statistical analysis shows the process can identify defects and quality with high accuracy. The artificial vision method was helpful for the selection of quality coffee beans and may be useful to increase production, reduce production time and improve quality control.

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