Classification and Detection of Cabbage Leaf Diseases from Images Using Deep Learning Methods
Myna A. N.,
Manasvi K.,
Pavan J. K.,
Rakshith H. S.,
Yuktha D. Jain
Issue:
Volume 11, Issue 1, March 2023
Pages:
1-7
Received:
26 December 2022
Accepted:
30 January 2023
Published:
4 March 2023
Abstract: The presented work uses Deep learning methods to detect diseases in cabbage leaves. The plant disease detection is constrained by human visual capabilities. Because, most of the early symptoms detected are microscopic. This process is tedious, time consuming and prediction is a challenging task. Hence, there is a need for developing a methodology that automatically recognizes, classifies and detects plant infection symptoms. Five major types of diseases namely Leaf miner, Diamond backmoth, Blackrot, Maggvots and Downy mildew are considered. Initially, the input images are classified into two types as healthy and diseased. Further, the diseased images are categorized into five different varieties. Around 3000 images of cabbage leaves are used containing healthy and infected leaves. Different phases namely preprocessing, feature extraction, training, testing and classification are used the proposed methodology. The accuracies of 93.5% and 90.5% are achieved for healthy and diseased leaf images. Classification accuracies for different types of diseased images are 89.9, 89.5, 91.8, 90.5 and 90.8 for Leaf miner, Diamond backmoth, Blackrot, Maggvots and Downy mildew respectively. The overall classification accuracy of 92% is attained. The developed methodology is found to provide good classification accuracy. The developed model finds its applications in APMCs, online purchase, Agricultural departments etc.
Abstract: The presented work uses Deep learning methods to detect diseases in cabbage leaves. The plant disease detection is constrained by human visual capabilities. Because, most of the early symptoms detected are microscopic. This process is tedious, time consuming and prediction is a challenging task. Hence, there is a need for developing a methodology t...
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Developing a Robust Emergency Information System for Natural Disasters
Konstantinos Papatheodosiou,
Chrissanthi Angeli
Issue:
Volume 11, Issue 1, March 2023
Pages:
8-14
Received:
14 February 2023
Accepted:
7 March 2023
Published:
16 March 2023
Abstract: A lot of Emergency Information Systems have been developed to inform people about impending emergencies. Most of them are part of an integrated system, collaborating with a respective warning system. No matter which technology is employed in the development process, an Emergency Information System aspires to deliver emergency content accurately and rapidly. Most of Emergency Information Systems transmit the emergency content in a predetermined geographical area. In parallel, a typical DRM Emergency Information System doesn’t vouch for transmission accuracy in the case of a possible communication loss or a power outage. This paper presents a DRM-based Emergency Information System that works well in remote areas with rough geographical terrain, offsetting a possible communication loss or a possible power outage. An efficient broadcast selection algorithm has been developed to answer this purpose. The paper also indicates the maximum coverage of the underlying system, in a mountainous area (Vigla), taking advantage of a standard methodology called " LEGBAC”. Specific software was used to come up with the wide area coverage map. The results proved that our system achieved maximum coverage in any antenna calibration (99% approximately) and the value of the respective signal strength was not significantly altered by the increase in the number of kilometers away from the transmission center, indicating the robustness of our system and the competency of the broadcast selection algorithm.
Abstract: A lot of Emergency Information Systems have been developed to inform people about impending emergencies. Most of them are part of an integrated system, collaborating with a respective warning system. No matter which technology is employed in the development process, an Emergency Information System aspires to deliver emergency content accurately and...
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Prospects and Challenges of Large Language Models in the Field of Intelligent Building
Wu Yang,
Wang Junjie,
Li Weihua
Issue:
Volume 11, Issue 1, March 2023
Pages:
15-20
Received:
3 April 2023
Accepted:
8 May 2023
Published:
18 May 2023
Abstract: At the end of November 2022, the ChatGPT released by OpenAI Inc. performed excellently and quickly became popular worldwide. Despite some shortcomings, Large Language Models (LLM) represented by Generative Pre-trained Transformer (GPT) is here to stay, leading the way for the new generation of Natural Language Processing (NLP) technique. This commentary presents the potential benefits and challenges of the applications of large language models, from the viewpoint of intelligent building. We briefly discuss the history and current state of large language models and their shortcomings. We then highlight how these models can be used to improve the daily maintenance of intelligent building. With regard to challenges, we address some vital problems to be solved before deployment and argue that large language models in intelligent building require maintenance staff to develop sets of competencies and literacies necessary to both understand the technology as well as the maintenance and maneuver of intelligent building. In addition, a clear strategy within intelligent building troops with a strong focus on AI talents construction and training dataset annotation are required to integrate and take full advantage of large language models in the daily maintenance. We conclude with recommendations for how to address these challenges and prepare for further applications of LLM in the field of intelligent building in the future.
Abstract: At the end of November 2022, the ChatGPT released by OpenAI Inc. performed excellently and quickly became popular worldwide. Despite some shortcomings, Large Language Models (LLM) represented by Generative Pre-trained Transformer (GPT) is here to stay, leading the way for the new generation of Natural Language Processing (NLP) technique. This comme...
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