Special Issue "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.


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.



• 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 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

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

Published Papers

  • Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils
  • 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
  •   Views:831       Downloads:622        Download PDF

  • A Novel Framework for Multi-Classification of Guava Disease
  • 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
  •   Views:1052       Downloads:827       Cited by:1        Download PDF