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All Journal JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA)
Kunto Aji Wibisono
Trunojoyo University Madura
Electrical & Electronics Engineering

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Automatic Pesticide Spray Based on Digital Image Processing in Chili Plants by Classification Backpropagation Neural Network Method Ian Faizal Idenugraha; Diana Rahmawati; Kunto Aji Wibisono; Miftachul Ulum
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol 4 No 1 (2020): April
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v4i1.317

Abstract

In Indonesia demand for chili still quite high and as if it has become a basic necessity for the community. Along with the world in the food processing industry, there has been an increase in the need for chillies, in addition to the high demand and the selling price of chilli peppers, it has encouraged the interest of the community to cultivate chili plants. However, biotic disorders that cause obstacles in efforts to increase chili production. On the leaves and fruit of the chili plant is a part of body the plant that allows the identification process of disease in the chili plant, because there will be changes in color and texture. The process of disease detection in chili plants through digital image processing using the feature extraction method, which has previously been done pre-processing. Then at the segmen-tation stage a thresholding operation is carried out to separate the healthy / diseased leaves / chili. For the classifi-cation of diseases using BPNN (Backpropagation Neural Network) method. The identification process will results five types of diseases, namely fusarium wilt, bacterial wilt, leaf foliage, curly leaves, and anthracnose. From this data will be sent by smartphone via IoT to the automatic sprayer to spray the type of pesticide in accordance with the dose and type of disease identified. Based on the results of testing using 150 samples of leaf and fruit images on chili plants obtained a success percentage of 43% in the leaves and 83.33% in the chilli fruit.
Design and Build a Vaname Shrimp Sorting System Based on Image Processing Miftachul Ulum; Kunto Aji Wibisono; Haryanto Haryanto; Riza Alfita; Adi Kurniawan Saputra
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol 6 No 2 (2022): October
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v6i2.1639

Abstract

Vannamei shrimp is a species of shrimp that has high economic value. In the process of trading vaname shrimp, there are different price classes. Determination of the price class of vaname shrimp is based on the size of the shrimp. But in the post-harvest process, the sorting of vaname shrimp is still done manually, namely by placing the white shrimp on a flat table and then separating it by size so that it takes a long time and the level of accuracy of the sorting process also becomes imprecise, as is done by cultivating in the coastal area of ​​Madura, this is due to the limitations of available shrimp post-harvest processing equipment. In addition, the limited supply of electrical energy for the coastal area of ​​Madura is also another factor that hinders the post-harvest process of vanname shrimp. The purpose of this study is to design and create a vannamei shrimp sorting system based on image processing. In processing this shrimp image using the Background Subtraction method. The Background Subtraction method is used as a separator between the object and the background. The sorting process is based on the size detection of shrimp by using the blob detection algorithm. BLOB (Binary Large Object) detection is an image segmentation method based on region growing. The goal is to analyze textures specifically and accurately. Because blob detection distinguishes colors that have thin gradations. Based on the tests that have been carried out, the average accuracy of the system in sorting vannamei shrimp is in the range of 90%.
Co-Authors Adi Kurniawan Saputra Diana Rahmawati Haryanto Haryanto Ian Faizal Idenugraha Riza Alfita Ulum, Miftachul