Hamna Waheed
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Hamna Waheed has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Noureen Zafar
University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46000, Pakistan
2 shared publications - Saif Ul Islam
University of Warwick, UK
2 shared publications - Waseem Akram
Khalifa University
2 shared publications - Abdul Hadi
University Institute of Information Technology, Pir Mehr Ali Shah, Arid Agriculture University—PMAS AAUR, Rawalpindi 46000, Pakistan
1 shared publication - Abdullah Gani
University of Malaya
1 shared publication - Awais Manzoor
Affiliations ADAPT Centre, Dublin, Ireland, Faculty of Business, eXplainable Analytics Group, Technological University Dublin, Dublin, Ireland
1 shared publication
Latest Publications
- Open Access
- 21 February 2023
- Published by MDPI AG in Future Internet
- Vol. 15 (3) , 86
A Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning
The agriculture sector plays a crucial role in supplying nutritious and high-quality food. Plant disorders significantly impact crop productivity, resulting in an annual loss of 33%. The early and accurate detection of plant disorders is a difficult task for farmers and requires specialized knowledge, significant effort, and labor. In this context, smart devices and advanced artificial intelligence techniques have significant potential to pave the way toward sustainable and smart agriculture. This paper presents a deep learning-based android system that can diagnose ginger plant disorders such as soft rot disease, pest patterns, and nutritional deficiencies. To achieve this, state-of-the-art deep learning models were trained on a real dataset of 4,394 ginger leaf images with diverse backgrounds. The trained models were then integrated into an Android-based mobile application that takes ginger leaf images as input and performs the real-time detection of crop disorders. The proposed system shows promising results in terms of accuracy, precision, recall, confusion matrices, computational cost, Matthews correlation coefficient (MCC), mAP, and F1-score.
- 21 February 2023
- Published by MDPI AG in Future Internet
- Vol. 15 (3) , 86
Open AccessA Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning
The agriculture sector plays a crucial role in supplying nutritious and high-quality food. Plant disorders significantly impact crop productivity, resulting in an annual loss of 33%. The early and accurate detection of plant disorders is a difficult task for farmers and requires specialized knowledge, significant effort, and labor. In this context, smart devices and advanced artificial intelligence techniques have significant potential to pave the way toward sustainable and smart agriculture. This paper presents a deep learning-based android system that can diagnose ginger plant disorders such as soft rot disease, pest patterns, and nutritional deficiencies. To achieve this, state-of-the-art deep learning models were trained on a real dataset of 4,394 ginger leaf images with diverse backgrounds. The trained models were then integrated into an Android-based mobile application that takes ginger leaf images as input and performs the real-time detection of crop disorders. The proposed system shows promising results in terms of accuracy, precision, recall, confusion matrices, computational cost, Matthews correlation coefficient (MCC), mAP, and F1-score.
- Open Access
- 24 May 2022
- Published by MDPI AG in Agriculture
- Vol. 12 (6) , 742
Deep Learning Based Disease, Pest Pattern and Nutritional Deficiency Detection System for “Zingiberaceae” Crop
Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of . For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy (). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets.- 24 May 2022
- Published by MDPI AG in Agriculture
- Vol. 12 (6) , 742
Open AccessDeep Learning Based Disease, Pest Pattern and Nutritional Deficiency Detection System for “Zingiberaceae” Crop
Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of . For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy (). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets.