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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Applied Research and Smart Technology (ARSTech)
Sophian, Ali
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Automotive Engineering Computer Science & IT Engineering Materials Science & Nanotechnology Physics

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Image-based disease detection and classification in Indian apple plant species by using deep learning Wani, Sidrah Fayaz; Ashraf, Arselan; Sophian, Ali
Applied Research and Smart Technology (ARSTech) Vol. 3 No. 1 (2022): Applied Research and Smart Technology
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/arstech.v3i1.1021

Abstract

Plant diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. Traditional farming methods are insufficient to address the impending global food crises. As a result, agricultural productivity growth is critical, and new techniques and methods are required for efficient and sustainable farming practices that balance the supply chain according to customer demand. Even though India is one of the most agriculturally dependent countries, it nevertheless suffers from various agricultural shortages. Plant diseases that go unnoticed and untreated are one such deprivation. Developing an intelligent automated technique for plant disease detection is explored in this research. Deep learning is used to create a smart system for image-based disease detection in Indian apple plant species. Specifically, this study uses a convolution neural network architecture, ResNet-34, to identify diseases in apple plants. Based on 70-30% and 80-20% dataset partition, the proposed model obtained an accuracy of 97.5% and 98.4%, respectively. The results obtained from this study illustrate the productive exploration and utility of the proposed model for future research by implementing various deep learning models and incorporating additional modules that provide cure and preventative measures for the detected diseases.   
Advancing precision in air quality forecasting through machine learning integration Komarudin, Muhamad; Ratna Sulistiyanti, Sri; Suharso, Suharso; Irsyad, Muhammad; Dian Septama, Hery; Yulianti, Titin; Sophian, Ali; Michel, Michel
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2113-2122

Abstract

In an era where environmental concerns are escalating, air quality forecasting emerges. Forecasting is a crucial tool for addressing the adverse impacts of pollution on public health and ecosystems. In urban centers like Bandar Lampung, economic activities intensify pollution levels. This condition leveraging advanced machine learning forecasting methods can significantly mitigate these effects. This study evaluates the precision of long short-term memory (LSTM) and Prophet methods in predicting air quality. This study utilizes data from January 12, 2022 to November 9, 2023. The results reveal a distinct advantage of the LSTM method over the Prophet. The LSTM method showcases superior accuracy across all evaluation metrics. Specifically, the LSTM method achieved an average root mean squared error (RMSE) of 5.38, mean absolute error (MAE) of 3.94, and mean absolute percentage error (MAPE) of 0.07. In contrast, the Prophet method recorded higher error rates, with an average RMSE of 18.48, MAE of 15.61, and MAPE of 0.25. These numbers underscore the LSTM method's robustness and reliability in forecasting air quality. The result highlights its potential as a pivotal resource for environmental monitoring and policymaking to safeguard public health and promote sustainable urban development.
Co-Authors Ashraf, Arselan Hery Dian Septama Michel, Michel Muhamad Komarudin Muhammad Irsyad Ratna Sulistiyanti, Sri Suharso Suharso Titin Yulianti Wani, Sidrah Fayaz