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Research Article

BEST PAPER 2021

Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT


Submission Deadline: 31 March 2021 (closed)

Abstract

This article has no abstract.

Keywords

Smart Precision agriculture, Artificial Intelligence, IoT, Agri Analytics, Time series data, Crop Prediction.
  • Research Article

    BEST PAPER 2021

    EfficientNet-Based Robust Recognition of Peach Plant Diseases in Field Images

    Haleem Farman1, Jamil Ahmad1,*, Bilal Jan2, Yasir Shahzad3, Muhammad Abdullah1, Atta Ullah4 CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 2073-2089, 2022, DOI:10.32604/cmc.2022.018961
    Abstract Plant diseases are a major cause of degraded fruit quality and yield losses. These losses can be significantly reduced with early detection of diseases to ensure their timely treatment, particularly in developing countries. In this regard, an expert system based on deep learning model where the expert knowledge, particularly the one acquired by plant pathologist, is recursively learned by the system and is applied using a smart phone application for use in the target field environment, is being proposed. In this paper, a robust disease detection method is developed based on convolutional neural network (CNN), where its powerful features extraction… More >

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  • Research Article

    BEST PAPER 2021

    A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production

    Jhonn Pablo Rodríguez1,*, David Camilo Corrales1,2, David Griol3, Zoraida Callejas3, Juan Carlos Corrales1 CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4725-4743, 2022, DOI:10.32604/cmc.2022.019135
    Abstract Coffee plays a key role in the generation of rural employment in Colombia. More than 785,000 workers are directly employed in this activity, which represents the 26% of all jobs in the agricultural sector. Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities, and resources (number of workers, required infrastructures), anticipating negotiations, estimating, price, and foreseeing losses of coffee production in a specific territory. These important processes can be affected by several factors that are not easy to predict (e.g., weather variability, diseases, or plagues.). In this paper, we propose a… More >

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  • Research Article

    BEST PAPER 2021

    Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

    Mohamed Esmail Karar1,2, Omar Reyad1,3,*, Abdel-Haleem Abdel-Aty4, Saud Owyed5, Mohd F. Hassan6 CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4095-4111, 2021, DOI:10.32604/cmc.2021.019059
    Abstract Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural processes. Therefore, Internet of Things (IoT) technology can be applied to monitor and detect harmful insect pests such as red palm weevils (RPWs) in the farms of date palm trees. In this paper, we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier, namely InceptionResNet-V2. The sound sensors, namely TreeVibes devices are carefully mounted on each palm trunk to setup wireless sensor networks in the farm. Palm trees are labeled based on the sensor node number to identify the… More >

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  • Research Article

    BEST PAPER 2021

    A Novel Framework for Multi-Classification of Guava Disease

    Omar Almutiry1, Muhammad Ayaz2, Tariq Sadad3, Ikram Ullah Lali4, Awais Mahmood1,*, Najam Ul Hassan5, Habib Dhahri1 CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1915-1926, 2021, DOI:10.32604/cmc.2021.017702
    Abstract Guava is one of the most important fruits in Pakistan, and is gradually boosting the economy of Pakistan. Guava production can be interrupted due to different diseases, such as anthracnose, algal spot, fruit fly, styler end rot and canker. These diseases are usually detected and identified by visual observation, thus automatic detection is required to assist formers. In this research, a new technique was created to detect guava plant diseases using image processing techniques and computer vision. An automated system is developed to support farmers to identify major diseases in guava. We collected healthy and unhealthy images of different guava… More >

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  • Research Article

    BEST PAPER 2021

    An AIoT Monitoring System for Multi-Object Tracking and Alerting

    Wonseok Jung1, Se-Han Kim2, Seng-Phil Hong3, Jeongwook Seo4,* CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 337-348, 2021, DOI:10.32604/cmc.2021.014561
    Abstract Pig farmers want to have an effective solution for automatically detecting and tracking multiple pigs and alerting their conditions in order to recognize disease risk factors quickly. In this paper, therefore, we propose a novel monitoring system using an Artificial Intelligence of Things (AIoT) technique combining artificial intelligence and Internet of Things (IoT). The proposed system consists of AIoT edge devices and a central monitoring server. First, an AIoT edge device extracts video frame images from a CCTV camera installed in a pig pen by a frame extraction method, detects multiple pigs in the images by a faster region-based convolutional… More >

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  • Research Article

    BEST PAPER 2021

    Deep Learning-Based Classification of Fruit Diseases: An Application for Precision Agriculture

    Inzamam Mashood Nasir1, Asima Bibi2, Jamal Hussain Shah2, Muhammad Attique Khan1, Muhammad Sharif2, Khalid Iqbal3, Yunyoung Nam4, Seifedine Kadry5,* CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1949-1962, 2021, DOI:10.32604/cmc.2020.012945
    Abstract Agriculture is essential for the economy and plant disease must be minimized. Early recognition of problems is important, but the manual inspection is slow, error-prone, and has high manpower and time requirements. Artificial intelligence can be used to extract fruit color, shape, or texture data, thus aiding the detection of infections. Recently, the convolutional neural network (CNN) techniques show a massive success for image classification tasks. CNN extracts more detailed features and can work efficiently with large datasets. In this work, we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases. A fine-tuned,… More >

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  • Research Article

    BEST PAPER 2021

    Severity Recognition of Aloe vera Diseases Using AI in Tensor Flow Domain

    Nazeer Muhammad1, Rubab2, Nargis Bibi3, Oh-Young Song4, Muhammad Attique Khan5,*, Sajid Ali Khan6 CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2199-2216, 2021, DOI:10.32604/cmc.2020.012257
    Abstract Agriculture plays an important role in the economy of all countries. However, plant diseases may badly affect the quality of food, production, and ultimately the economy. For plant disease detection and management, agriculturalists spend a huge amount of money. However, the manual detection method of plant diseases is complicated and time-consuming. Consequently, automated systems for plant disease detection using machine learning (ML) approaches are proposed. However, most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data. To address the issue, this article proposes a fully… More >

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