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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Marjia Sultana
Begum Rokeya University
Computer Science & IT

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Deep transfer learning based disease detection and classification of tomato leaves - a comparative analysis Munira Akter Lata; Marjia Sultana; Iffat Ara Badhan; Mastura Jahan Maria; Fariha Tasnim Nuha
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26887

Abstract

A wide variety of diseases have a significant impact on tomato plants. To avoid crop quality issues, a prompt and precise diagnosis is crucial. Classifying plant diseases is one of the numerous applications where deep transfer learning models have recently produced remarkable results. This study dealt with fine-tuning by contrasting the most advanced architectures, including Inception V3, ResNet-18, ResNet-50, VGG-16, VGG-19, GoogLeNet, and AlexNet. In the end, a comparison evaluation is conducted. Nine distinct tomato disease classes and one healthy class from PlantVillage make up the dataset used in this study. Precision, recall, F1-score, and accuracy were the basis for a multiclass statistical analysis that assessed the models. The ResNet-50 approach yielded significant results with precision: 82%, recall: 81%, F1-score: 81%, and accuracy: 85%. With this high success rate, it is reasonable to say that mobile applications or IoT-compatible gadgets implemented with the ResNet-50 model can assist farmers in identifying and safeguarding tomatoes against the aforementioned diseases.
Automated classification of diseased cauliflower: a feature-driven machine learning approach Mala Rani Barman; Al Amin Biswas; Marjia Sultana; Aditya Rajbongshi; Md. Sabab Zulfiker; Tasnim Tabassum
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25812

Abstract

Cauliflower is a popular winter crop in Bangladesh. However, cauliflower plants are vulnerable to several diseases that can reduce the cauliflowers’ productivity and degrade their quality. The manual monitoring of these diseases takes a lot of effort and time. Therefore, automatic classification of the diseased cauliflower through computer vision techniques is essential. This study has retrieved ten different statistical and gray-level co-occurrence matrix (GLCM)-based features from the cauliflower image dataset by implementing a variety of image processing techniques. Afterwards, the SelectKBest method with the analysis of variance f-value (ANOVA F-value) has been used to identify the most important attributes for classification of the diseased cauliflower. Based on the ANOVA F-value, the top N (5≤N ≤9) most dominant attributes is used to train and test five machine learning (ML) models for classification of diseased cauliflower. Finally, different performance metrics have been used for evaluating the effectiveness of the employed ML models. The bagging classifier achieved the highest accuracy of 82.35%. Moreover, this model has outperformed other ML classifiers in terms of other performance metrics also.
Development of an IoT based smart potato leaf diseases monitoring and controlling system with image processing Munira Akter Lata; Tania Khatun; Marjia Sultana; Iffat Ara Badhan; Tasniya Ahmed; Md. Rakib Hasan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.25910

Abstract

Potato (Solanum tuberosum L.) is a key crop and a major source of livelihood for a vast population of the world after wheat and rice. However, diseases have diverse effects on potato fields, leading to damage to crops and reducing crop production. The two major diseases that afflict potato plants are referred to as early blight and late blight. Protection of this crop yield from blight diseases is one of the foremost challenges. Therefore, detection of these leaf diseases at appropriate time is very essential to prevent the damage. This study intends an Internet of Things (IoT)-based smart potato leaf diseases monitoring and control system that combines IoT technology with eco-sensing and image processing to identify and categorize these diseases. Studies have found that blight diseases are directly related to temperature and humidity of the planted area. This study measures environmental information using a sensor network installed in the planted area. After sensing and measuring environmental information, acquired values are displayed and farmers get these acquired values via message notification. The system obtains a 97% accuracy rate in recognizing these diseases by using our fine-tuned model of ResNet-50 for image processing.
Co-Authors Aditya Rajbongshi Al Amin Biswas Fariha Tasnim Nuha Iffat Ara Badhan Mala Rani Barman Mastura Jahan Maria Md. Rakib Hasan Md. Sabab Zulfiker Munira Akter Lata Munira Akter Lata Tania Khatun Tasnim Tabassum Tasniya Ahmed