Browsing by Author "Barman, Mala Rani"

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A Transfer Learning Approach to the Development of an Automation System for Recognizing Guava Disease Using CNN Models for Feasible Fruit Production
(IEEE, 2023-05-25) Shakil, Rashiduzzaman; Akter, Bonna; Rajbongshi, Aditya; Sara, Umme; Barman, Mala Rani; Dhali, Aditi
Guava (Psidium guava) is one of the most popular fruits which plays a vital role in the world economy. To increase guava production and sustain economic development, early detection and diagnosis of guava disease is important. As traditional recognition systems are time-consuming, expensive, and sometimes their predictions are also inaccurate, farmers are facing a lot of losses because of not getting the proper diagnosis and appropriate cure in time. In this study, an automatic system based on Convolution Neural Networks (CNN) models for recognizing guava disease has been proposed. To make the dataset more efficient, image processing techniques have been employed to boost the dataset which is collected from the local Guava Garden. For training and testing the applied models named InceptionResNetV2, ResNet50, and Xception with transfer learning technique, a total of 2,580 images in five categories such as Phytophthora, Red Rust, Scab, Stylar end rot, and Fresh leaf are utilized. To estimate the performance of each applied classifier, the six-performance evaluation metrics have been calculated where the Xception model conducted the highest accuracy of 98.88% which is good enough compared to other recent relevant works.
Automated classification of diseased cauliflower: a feature-driven machine learning approach
(Scopus, 2023-12-27) Barman, Mala Rani; Biswas, Al Amin; Sultana, Marjia; Rajbongshi, Aditya; Zulfiker, Md. Sabab; Tabassum, Tasnim
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.
Automated Classification of Diseased Cauliflower: A Feature-driven Machine Learning Approach
(UAD Universitas Ahmad Dahlan, 2024-07-15) Barman, Mala Rani; Biswas, Al Amin; Sultana, Marjia; Rajbongshi, Aditya; Zulfiker, Md. Sabab; Tabassum, Tasnim
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.
Sunflower Diseases Recognition Using Computer Vision-Based Approach
(Scopus, 2021) Rajbongshi, Aditya; Biswas, Al Amin; Biswas, Jahanur; Shakil, Rashiduzzaman; Akter, Bonna; Barman, Mala Rani
Sunflower (Helianthus annuus) is a plant categorized as a low to medium drought-sensitive crop. It adds a significant value to the agricultural-based economy. But nowadays worldwide sunflower production is in crisis due to its many diseases. But if proper action is not adopted earlier, many serious diseases will have affect plants. Consequently, it will reduce the productivity, quantity, and quality of sunflower. Manual identification of disease is a very tedious task or perhaps impossible at times. Nowadays, computer vision-based technique has gained its popularity in the field of object recognition. In this paper, we proposed an approach for sunflower disease recognition. A total of 650 images were used to accomplish this work. The image data processing techniques such as resizing, contrast, and color enhancement have also been used. We have used k-means clustering for segmenting the diseases affected region and then extracted features from the segmented images. The classification has performed using five classifiers. We calculated the seven performance evaluation metrics for the performance measurement of each classifier. The highest average accuracy of 90.68% has been obtained for the Random Forest classifier that outperformed others.