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All Journal IAES International Journal of Robotics and Automation (IJRA) Bulletin of Electrical Engineering and Informatics SAINS TANAH - Journal of Soil Science and Agroclimatology
Atiqur Rahman
Dept. Of Computer Science & Engineering
Automotive Engineering Education 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.
Soil hydraulic properties and field-scale hydrology as affected by land-management options Rahman, Atiqur; Amin, M. G. Mostofa
SAINS TANAH - Journal of Soil Science and Agroclimatology Vol 20, No 1 (2023): June
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/stjssa.v20i1.70504

Abstract

Recurring puddling for long-term rice cultivation forms a plow pan at a particular soil depth, which alters soil hydraulic properties, field-scale hydrology, and nutrient persistence in the soil. This experiment aimed to assess the impact of long-term rice cultivation on root-zone soil hydraulic properties and field-scale hydrology. Soil core samples were collected from four land management options namely, rice‒rice, non-rice, rice and non-rice, and field ridge, at two sites, one with loam and another with silt-loam soil. The soil cores were sampled for each 10 cm layer up to 100 cm depth from three locations of each rotation at both sites. Soil hydraulic parameters were estimated using a pedotransfer function based on the measured bulk density and soil texture. A mathematical model named HYDRUS-1D predicted infiltration, percolation, and surface runoff with the estimated hydraulic properties for three extreme rainfall events, i.e., 3.33, 5, and 6.66 cm hr-1, during a 3-hour period. A plow pan was found at 20–30 cm soil depth for all the land management options but not for the field ridge. The plow pan of the rice‒rice rotation had the highest bulk density (1.53 g cm-3) and the lowest hydraulic conductivity (17.56 cm day-1). However, the top 10 cm soil layer in the rice–rice field had the lowest bulk density (0.93 g cm-3). At both sites, the field ridge had higher infiltration and percolation and lower runoff than other rotations. The study reveals that the field-ridge area of a rice field can be the main water loss pathway. Phosphorus concentration in the rice-rice rotation decreased from 7.7 mg kg-1 in the 10-cm soil layer to 2.49 mg kg-1 in the 100-cm layer. These findings will facilitate making better water management decisions.
Co-Authors Amin, M. G. Mostofa Arthur, Seleman Daudi Habib, Md. Tarek Hasan, Nahid Polin, Johora Akter Sharma, Bidyut Vasha, Zannatun Nayem