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M Prabhaker, R. Ram (2020)
Real-Time Task Schedulers for a High-Performance Multi-Core SystemAutom. Control. Comput. Sci., 54
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A Performance Aware Security Framework to Avoid Software Attacks on Internet of Things (IoT) Based Patient Monitoring System2018 International Conference on Current Trends towards Converging Technologies (ICCTCT)
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AN INTELLIGENT MULTI- OBJECTIVE EVOLUTIONARY SCHEDULERS TO SCHEDULE REALTIME TASKS FOR MULTICORE ARCHITECTURE BASED AUTOMOTIVE ELECTRONIC CONTROL UNITS
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Smart Farming: Pomegranate Disease Detection Using Image ProcessingProcedia Computer Science, 58
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Mehmet Taştan (2018)
IoT Based Wearable Smart Health Monitoring SystemCelal Bayar Üniversitesi Fen Bilimleri Dergisi
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An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
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Evaluating the Performance of Various MOEA's to Optimize Scheduling Overhead in Homogeneous Multicore Architecture2018 International Conference on Current Trends towards Converging Technologies (ICCTCT)
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In this work, smart farming based on the Internet of Things (IoT) was proposed to reduce the existing link between the information technology sector and agriculture. In agriculture, India’s largest sector, farmers spend a lot of time diagnosing crop diseases. Early detection of various plant diseases can control and prevent major damage through their spread. Moreover, awareness among farmers about the use of technology to increase crop production is low. Therefore, with IoT technology, many solutions can be provided to farmers to increase yields. An IoT-based plant pathogen formation and air quality monitoring system is proposed here, which includes temperature, humidity, air impurity, and rainfall in the environment. Air quality is determined from gases such as carbon dioxide and carbon monoxide. Image capture and processing techniques are used to detect disease in crops. This will benefit the farmers and give them an idea to fix the diseases. Compared to the existing approaches, our approach provides the best solution for diagnosing the disease in plants in a short period of time and at low cost. For the experiment, the tomato leaves were considered and 94.78% of the leaves were diagnosed accurately by the proposed system.
Automatic Control and Computer Sciences – Springer Journals
Published: Apr 1, 2023
Keywords: air quality monitoring; plant disease detection; internet of things; Sensors; Raspberry Pi
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