Special lssues
Table of Content

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

Submission Deadline: 31 March 2021 (closed)

Guest Editors

Dr. Irfan Mehmood, University of Bradford, Bradford, UK.
Dr. Ahmed A. Abd El-Latif, Menoufia University, Egypt.
Dr. S. Vimal, National Engineering College, India.

Summary

Significance & Novelty:

Agriculture production is mainly dependent on monsoon. The success of monsoon based agriculture depends on the climate that prevailed during the particular cropping season. Providing real-time weather information to the farmers for making crop management decision can minimize the risk and losses due to extreme climatic condition. Agricultural fields are managed on a field basis and on the recommendation of the research project not considering the differences in spatial and temporal variability of the soil. A farmer goes for a simple blank decision of input such as fertilizer, irrigation facilities and labour which do not produce desirable crop yield. The only alternative is to enhance productivity on a sustainable basis from the limited natural resources at the disposal, without any adverse effect is by maximizing the resource input use efficiency. Precision agriculture, however, answers the clarion call by its focus on effective resource utilization through the management of spatial and temporal variability of the soil and the ecosystem.

The Modern era has seen a significant improvement in the advancement of IoT and various sensor edge connecting devices applied in collecting agricultural data for the farmers in a smart way. Besides farming is adopted with a lot of economical issues that affect the productivity and lack of farmers in sub rural areas. The best approaches to tackle the most challenges the farmers face in the day to day life is mainly due to usage of pesticides, climatic conditions, lack of water supply, lack of resources and quality of soil has to be identified in a smarter way.

Smart Farming is a modern system of doing agriculture and improving cultivation in a sustainable way. The smart IoT devices are connected together with innovative technologies to enhance agriculture. The smart sensors deployment and the smart way of agriculture enhance agriculture with less physical work from farmers and thereby productivity is increased. The smart technologies enrich agriculture with less utilisation of water, less electricity usage and more optimisation with real-time monitoring in humidity and temperature. In IoT based smart farming, the field monitoring is done with multiple sensors like humidity, temperature and soil moisture. There remains a lack of research and development in relation to Smart Sustainable Agriculture (SSA), accompanied by complex obstacles arising from the fragmentation of agricultural processes like the control and operation of IoT/AI machines, data sharing and management, interoperability and large amounts of data analysis and storage.

 

Reason for the choice of topic:

Agriculture has to be more enhanced to make it sustainable in a smart way where the challenges in data acquisition, storage and reliable connection has to be established in remote rural areas. The crop data and production have to be distributed centrally with the support of cloud/edge computing-based architectures, that helps the farmers to analyse the information, make a prediction of the crops in the cultivable land with the support AI/ML algorithms to be geographically diverse.

This Special Issue calls for reports on high quality, Novel solutions and research for smart and AI-based sustainable agriculture, ranging from IoT, WSN, efficient sensing, cloud/edge computing, smart actuators, etc.

 

Objectives:

• To develop a novel Decision Making Analytic Pattern for Precision Agriculture to improve the production

• To provide predicted analytical data for crop cultivation and for agriculture management of the farming community in future generations.

• To recognize a wide variety of learning algorithms and how to apply a variety of those algorithms to data.

• To disseminate the information of new agro-technology to farmers through extension activities.

• To introduce the advancements in the computing field to effectively handle and make inferences from voluminous and heterogeneous farming data.

• State-of-the-art Smart sensor approaches need to be improved in terms of data integration, interpretability, security and temporal modelling to be effectively applied to the Smart agriculture has been focused

 

Topics:

Topics of interest include, but are not limited to, the following scope:

• Smart farming, precision agriculture, and phenotyping

• Smart applications for site-specific crop monitoring and management

• Data processing techniques and related big data problem and solution

• IoT solutions and automation

• Data-aware networking in smart agriculture

• Sensor network deployment for smart agriculture

• Smart sensors, sensing mechanisms and platforms for sustainable agriculture

• Edge computing for smart agriculture;

• Internet of Things (IoT) for smart agriculture;

• Cloud-enabled techniques and innovation for sustainable agriculture

• Big data innovation in sustainable agriculture

• Decision support systems and making (AI, machine learning)

• Real-time monitoring in smart agriculture

• Growing trends of precision agriculture

• Data mining and statistical issues in precision agriculture

• Decision support systems for precision agriculture

• Emerging tools and techniques for precision agriculture


Keywords

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

Published Papers


  • Open Access

    ARTICLE

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

    Haleem Farman, Jamil Ahmad, Bilal Jan, Yasir Shahzad, Muhammad Abdullah, Atta Ullah
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 2073-2089, 2022, DOI:10.32604/cmc.2022.018961
    (This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
    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 >

  • Open Access

    ARTICLE

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

    Jhonn Pablo Rodríguez, David Camilo Corrales, David Griol, Zoraida Callejas, Juan Carlos Corrales
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4725-4743, 2022, DOI:10.32604/cmc.2022.019135
    (This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
    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 >

  • Open Access

    ARTICLE

    Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

    Mohamed Esmail Karar, Omar Reyad, Abdel-Haleem Abdel-Aty, Saud Owyed, Mohd F. Hassan
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4095-4111, 2021, DOI:10.32604/cmc.2021.019059
    (This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
    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 >

  • Open Access

    ARTICLE

    A Novel Framework for Multi-Classification of Guava Disease

    Omar Almutiry, Muhammad Ayaz, Tariq Sadad, Ikram Ullah Lali, Awais Mahmood, Najam Ul Hassan, Habib Dhahri
    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1915-1926, 2021, DOI:10.32604/cmc.2021.017702
    (This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
    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 >

  • Open Access

    ARTICLE

    An AIoT Monitoring System for Multi-Object Tracking and Alerting

    Wonseok Jung, Se-Han Kim, Seng-Phil Hong, Jeongwook Seo
    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 337-348, 2021, DOI:10.32604/cmc.2021.014561
    (This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
    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 >

  • Open Access

    ARTICLE

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

    Inzamam Mashood Nasir, Asima Bibi, Jamal Hussain Shah, Muhammad Attique Khan, Muhammad Sharif, Khalid Iqbal, Yunyoung Nam, Seifedine Kadry
    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1949-1962, 2021, DOI:10.32604/cmc.2020.012945
    (This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
    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 >

  • Open Access

    ARTICLE

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

    Nazeer Muhammad, Rubab, Nargis Bibi, Oh-Young Song, Muhammad Attique Khan, Sajid Ali Khan
    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2199-2216, 2021, DOI:10.32604/cmc.2020.012257
    (This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
    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 >

Share Link