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All Journal IAES International Journal of Robotics and Automation (IJRA) Bulletin of Electrical Engineering and Informatics
Atiqur Rahman
Dept. Of Computer Science & Engineering
Automotive Engineering Electrical & Electronics Engineering

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Tomato pest recognition using convolutional neural network in Bangladesh Polin, Johora Akter; Hasan, Nahid; Habib, Md. Tarek; Rahman, Atiqur; Vasha, Zannatun Nayem; Sharma, Bidyut
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6073

Abstract

The tomato is one of the most popular and well-liked veggies among Asians. It is interesting to note that in Bangladesh, it is the second most significant vegetable consumed. Moreover, tomato is served not only as a vegetable, but it is also served as sauce, jam, etc., and used in making different types of cuisines. But the fact is due to the pests, thousands of tons of tomatoes are harmed every year in Bangladesh. The production of tomatoes in Bangladesh is harmed by a number of dangerous pests. We develop a solution to recognize pests at an early stage. Five different pest types, including aphids, red spider mites, whiteflies, looper caterpillars, and thrips, have been studied in this research. To identify tomato pests, we curated image datasets from online and offline repositories and processed them using a convolutional neural network (CNN) model. We used features from CNN layers for three machine learning algorithms: Random Forest (RF), support vector machine (SVM), and K-Nearest Neighbors (K-NN). This comprehensive approach allowed a thorough comparison of these algorithms in tomato pest recognition. For recognizing tomato pests, our methods generate excellent results. The accuracy of our experiment is 95.49% which indicates the successful completion of the experiment.
Smart factory for future industry development Rahman, Atiqur; Arthur, Seleman Daudi
IAES International Journal of Robotics and Automation (IJRA) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v12i2.pp192-196

Abstract

The paradigm of the smart factory is thought of as an innovative outline for the fourth industrial revolt. The GLOVA G7-DR20U is set as a programmable logic controller (PLC) for monitoring the performance of the smart factory while using the NodeMCU-V3 esp8266 as the internet of things (IoT) board for interaction between managers and the factory using the personal digital assistant (PDA) programming that has been written in the RabitMQ platform. The program logged inner PLC by applying ladder language for monitoring the performance of PLC. With the completion of intelligent PLC, it is likely to extend the existing making capability in the factory with simplicity. This work joins a PLC used as a parent control unit, apps, user programs, and human-machine interface, with the Internet. The proposed model of the smart factory holds two motors one for the parallel drive and the other for the upright drive. While running the system, we observe that the proposal is working correctly, and the reply to the interaction method via IoT is excellent.
Co-Authors Arthur, Seleman Daudi Habib, Md. Tarek Hasan, Nahid Polin, Johora Akter Sharma, Bidyut Vasha, Zannatun Nayem