R. Punithavathi¹, A. Delphin Carolina Rani², K. R. Sughashini³, Chinnarao Kurangi⁴, M. Nirmala⁵, Hasmath Farhana Thariq Ahmed⁶, S. P. Balamurugan^7,*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2759-2774, 2023, DOI:10.32604/csse.2023.027647 - 01 August 2022

Abstract Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing crop productivity. The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased. Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity, this study presents a novel computer vision and deep learning based weed detection and classification (CVDL-WDC) model for precision agriculture. The proposed CVDL-WDC technique More >

Ashit Kumar Dutta^1,*, Yasser Albagory², Abdul Rahaman Wahab Sait³, Ismail Mohamed Keshta¹

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 899-915, 2022, DOI:10.32604/cmc.2022.026783 - 18 May 2022

Abstract Recently, autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare, agriculture, industrial automation, etc. Among the interesting applications of autonomous systems, their applicability in agricultural sector becomes significant. Autonomous unmanned aerial vehicles (UAVs) can be used for suitable site-specific weed management (SSWM) to improve crop productivity. In spite of substantial advancements in UAV based data collection systems, automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops. The recently developed deep learning (DL) models have… More >

R. Madhumathi^1,*, T. Arumuganathan², R. Shruthi¹

Computer Systems Science and Engineering, Vol.43, No.2, pp. 455-469, 2022, DOI:10.32604/csse.2022.023792 - 20 April 2022

Abstract Precision agriculture is a modern farming practice that involves the usage of Internet of Things (IoT) to provide an intelligent farm management system. One of the important aspects in agriculture is the analysis of soil nutrients and balancing these inputs are essential for proper crop growth. The crop productivity and the soil fertility can be improved with effective nutrient management and precise application of fertilizers. This can be done by identifying the deficient nutrients with the help of an IoT system. As traditional approach is time consuming, an IoT-enabled system is developed using the colorimetry… More >

Khalil Ibrahim Mohammad Abuzanouneh¹, Fahd N. Al-Wesabi², Amani Abdulrahman Albraikan³, Mesfer Al Duhayyim⁴, M. Al-Shabi⁵, Anwer Mustafa Hilal⁶, Manar Ahmed Hamza^6,*, Abu Sarwar Zamani⁶, K. Muthulakshmi⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 109-124, 2022, DOI:10.32604/cmc.2022.022648 - 24 February 2022

Abstract Agriculture 4.0, as the future of farming technology, comprises numerous key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. To achieve effective water resource usage and automated irrigation in precision agriculture, recent technologies like machine learning (ML) can be employed. With this motivation, this paper design an IoT and ML enabled smart irrigation system (IoTML-SIS) for precision agriculture. The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for… More >

P. Suresh^1,*, R. H. Aswathy¹, Sridevi Arumugam², Amani Abdulrahman Albraikan³, Fahd N. Al-Wesabi⁴, Anwer Mustafa Hilal⁵, Mohammad Alamgeer⁶

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1713-1728, 2022, DOI:10.32604/cmc.2022.021789 - 03 November 2021

Abstract In India, water wastage in agricultural fields becomes a challenging issue and it is needed to minimize the loss of water in the irrigation process. Since the conventional irrigation system needs massive quantity of water utilization, a smart irrigation system can be designed with the help of recent technologies such as machine learning (ML) and the Internet of Things (IoT). With this motivation, this paper designs a novel IoT enabled deep learning enabled smart irrigation system (IoTDL-SIS) technique. The goal of the IoTDL-SIS technique focuses on the design of smart irrigation techniques for effectual water… More >

Fahd N. Al-Wesabi^1,2,*, Amani Abdulrahman Albraikan³, Anwer Mustafa Hilal⁴, Majdy M. Eltahir¹, Manar Ahmed Hamza⁴, Abu Sarwar Zamani⁴

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6223-6238, 2022, DOI:10.32604/cmc.2022.021299 - 11 October 2021

Abstract Precision agriculture enables the recent technological advancements in farming sector to observe, measure, and analyze the requirements of individual fields and crops. The recent developments of computer vision and artificial intelligence (AI) techniques find a way for effective detection of plants, diseases, weeds, pests, etc. On the other hand, the detection of plant diseases, particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss. Besides, earlier and precise apple leaf disease detection can minimize the spread of the disease. Earlier works make use of traditional image processing techniques which cannot assure… More >

Mohamed Esmail Karar^1,2, Omar Reyad^1,3,*, 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 - 24 August 2021

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… More >

Inzamam Mashood Nasir¹, Asima Bibi², Jamal Hussain Shah², Muhammad Attique Khan¹, Muhammad Sharif², Khalid Iqbal³, Yunyoung Nam⁴, Seifedine Kadry^5,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1949-1962, 2021, DOI:10.32604/cmc.2020.012945 - 26 November 2020

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… More >

Qi Wang^1,2,3,*, Changsong Yang^2,3,5, Yuxiang Wang^1,2,3, Shao-en Wu⁴

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1433-1442, 2020, DOI:10.32604/iasc.2020.012759 - 24 December 2020

Abstract To improve the positioning accuracy of farming vehicle in precision agriculture, an integrated positioning system is proposed based on Global Navigation Satellite System (GNSS)/Strapdown Inertial Navigation System (SINS)/Wireless Sensor Networks (WSN) with low cost and high reliability. The principles of commonly used localization technologies in vehicle positioning are compared and the Received Signal Strength Indication (RSSI) based measurement method is chosen as the integrated positioning system for information fusion considering the complexity of the algorithm, positioning accuracy and hardware requirements in the application scenario. The research of wireless signal propagation loss model of farmland environment More >