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All Journal International Journal of Electrical and Computer Engineering CORD
Karim, A. N. M. Rezaul
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Agriculture, Biological Sciences & Forestry Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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Perceptions of Farming Community in Relation to Problems in Farming and Prospects of Coconut Mite Management in Bangladesh Islam, M. Nazirul; Rahman, M. Sayedur; Islam, M. Ishaqul; Samsunnahar, M.; Karim, A. N. M. Rezaul; Azad, A. K.
International Coconut Community Journal Vol 34 No 1 (2018): CORD
Publisher : International Coconut Community

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (446.528 KB) | DOI: 10.37833/cord.v34i1.25

Abstract

A study was conducted to analyze community perception on homestead agro-biodiversity and conservation of coconut genetic resources at Bagharpara Upazila (Sub district) of Jashore district, Bangladesh in October 2011. Tools and techniques of Participatory Rural Appraisals (PRA) were utilized to identify the socio-economic factors and agronomic practices influencing homestead agro-biodiversity. The participants identified coconut as a leading species in the homesteads. Communities suspected that the wave (electro-magnetic) generating from mobile phone towers was the cause of damaging coconut in their villages. Being disheartened with continuous yield loss, the farmers have resorted to fell down their coconut trees and shifted to cultivating fruit trees or suitable field crops. The research team used the matrices of PRA to develop a problem tree, which marked mite infestation in coconut as the focal problem. The developed problem-tree was transposed into an objective tree. Based on the objective tree, the research team was able to develop and implement a three-year research project on mite management in coconut involving farmers as implementers. The intervention stimulated community knowledge and skills towards mite management and conservation of unique traditional coconut varieties.
GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection Islam, Saiful; Akhtar, Md. Nasim; Hassan, M. Mahadi; Karim, A. N. M. Rezaul; Habib, Israt Binteh
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1307-1318

Abstract

Early and accurate identification of rice leaf diseases is essential for sustainable crop management; however, many existing convolutional neural networks (CNNs) based solutions struggle with class imbalance and limited robustness when applied to real-field data. In this work, a generative adversarial network (GAN) augmented vision transformer (ViT) framework is introduced to overcome these limitations. A deep size representative samples for underrepresented disease categories, resulting in a more balanced training dataset and achieving a Fréchet inception distance (FID) score of 18.6. The balanced dataset is then used to train a vision transformer model that leverages self-attention to capture global contextual features of rice leaf images. Experimental evaluation across ten disease classes shows that the proposed approach attains an overall classification accuracy of 96.5%, exceeding the performance of several established CNN architectures. Additionally, the model demonstrates strong generalization capability on an external field dataset, achieving 94.8% accuracy. To validate real-world applicability, the trained model is deployed on a Jetson Nano edge device, where it delivers efficient inference performance suitable for practical agricultural applications. The findings indicate that combining GAN-based data augmentation with transformer-based learning provides a reliable and scalable solution for rice leaf disease detection.
Co-Authors Akhtar, Md. Nasim Azad, A. K. Habib, Israt Binteh Hassan, M. Mahadi Islam, M. Ishaqul Islam, M. Nazirul Islam, Saiful Rahman, M. Sayedur Samsunnahar, M.