Ahmed Abu-Khadrah
Abstract
Communications and Computer Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan
School of Information Technology, Skyline University, Sharjah, 1797, UAE
Smart Sensors are used for monitoring, sensing, and actuating controls in small and large-scale agricultural plots. From soil features to crop health and climatic observations, the smart sensors integrate with sophisticated technologies such as the Internet of Things or cloud for decentralized processing and global actuation. Considering this integration, an Amendable Multi-Function Sensor Control (AMFSC) is introduced in this proposal. This proposed method focuses on sensor operations that aid agricultural production improvements. The agriculture hindering features from the soil, temperature, and crop infections are sensed and response is actuated based on controlled operations. The control operations are performed according to the sensor control validation and modified control acute sensor, which helps to maximize productivity. The sensor control and operations are determined using federated learning from the accumulated data in the previous sensing intervals. This learning validates the current sensor data with the optimal data stored for different crops and environmental factors in the past. Depending on the computed, sensed, and optimal (adaptable) data, the sensor operation for actuation is modified. This modification is recommended for crop and agriculture development to maximize agricultural productivity. In particular, the sensing and actuation operations of the smart sensors for different intervals are modified to maximize production and adaptability. The efficiency of the system was evaluated using different parameters and the system maximizes the analysis rate (12.52%), control rate (7%), adaptability (9.65%) and minimizes the analysis time (7.12%), and actuation lag (8.97%)
- Liu, W. (2021). Smart sensors, sensing mechanisms and platforms of sustainable smart agriculture realized through the big data analysis. Cluster Computing, 1-15.Google Scholar
- Dhillon, S. K., Madhu, C., Kaur, D., & Singh, S. (2020). A review on precision agriculture using wireless sensor networks incorporating energy forecast techniques. Wireless Personal Communications, 113(4), 2569-2585.Google ScholarDigital Library
- Taheri, F., D'Haese, M., Fiems, D., & Azadi, H. (2022). The intentions of agricultural professionals towards diffusing wireless sensor networks: Application of technology acceptance model in Southwest Iran. Technological Forecasting and Social Change, 185, 122075.Google ScholarCross Ref
- Kyere, I., Astor, T., Graß, R., & Wachendorf, M. (2020). Agricultural crop discrimination in a heterogeneous low-mountain range region based on multi-temporal and multi-sensor satellite data. Computers and Electronics in Agriculture, 179, 105864.Google ScholarCross Ref
- Hamouda, Y., & Msallam, M. (2020). Variable sampling interval for energy-efficient heterogeneous precision agriculture using Wireless Sensor Networks. Journal of King Saud University-Computer and Information Sciences, 32(1), 88-98.Google ScholarDigital Library
- Zhang, J., & Wang, Y. (2021). Design of remote control device using wireless sensor network and its use in intelligent monitoring of farmland information. EURASIP Journal on Wireless Communications and Networking, 2021(1), 1-13.Google ScholarDigital Library
- He, J., Hu, L., Wang, P., Liu, Y., Man, Z., Tu, T., ... & Luo, X. (2022). Path tracking control method and performance test based on agricultural machinery pose correction. Computers and Electronics in Agriculture, 200, 107185.Google ScholarDigital Library
- Hu, X., Sun, L., Zhou, Y., & Ruan, J. (2020). Review of operational management in intelligent agriculture based on the Internet of Things. Frontiers of Engineering Management, 7(3), 309-322.Google ScholarCross Ref
- Garcia, A. P., Umezu, C. K., Polania, E. C. M., Dias Neto, A. F., Rossetto, R., & Albiero, D. (2022). Sensor-Based Technologies in Sugarcane Agriculture. Sugar Tech, 1-20.Google Scholar
- Abanay, A., Masmoudi, L., & El Ansari, M. (2022). A calibration method of 2D LIDAR-Visual sensors embedded on an agricultural robot. Optik, 249, 168254.Google ScholarCross Ref
- Abbas, I., Liu, J., Faheem, M., Noor, R. S., Shaikh, S. A., Solangi, K. A., & Raza, S. M. (2020). Different sensor based intelligent spraying systems in Agriculture. Sensors and Actuators A: Physical, 316, 112265.Google ScholarCross Ref
- Perales Gómez, Á. L., López-de-Teruel, P. E., Ruiz, A., García-Mateos, G., Bernabé García, G., & García Clemente, F. J. (2022). FARMIT: continuous assessment of crop quality using machine learning and deep learning techniques for IoT-based smart farming. Cluster Computing, 25(3), 2163-2178.Google ScholarDigital Library
- Guillén, M. A., Llanes, A., Imbernón, B., Martínez-España, R., Bueno-Crespo, A., Cano, J. C., & Cecilia, J. M. (2021). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing, 77(1), 818-840.Google ScholarDigital Library
- Alrowais, F., Asiri, M. M., Alabdan, R., Marzouk, R., Hilal, A. M., & Gupta, D. (2022). Hybrid leader based optimization with deep learning driven weed detection on internet of things enabled smart agriculture environment. Computers and Electrical Engineering, 104, 108411.Google ScholarDigital Library
- Maldaner, L. F., Molin, J. P., Canata, T. F., & Martello, M. (2021). A system for plant detection using sensor fusion approach based on machine learning model. Computers and Electronics in Agriculture, 189, 106382.Google ScholarDigital Library
- Dhillon, S., Madhu, C., Kaur, D., & Singh, S. (2020). A solar energy forecast model using neural networks: Application for prediction of power for wireless sensor networks in precision agriculture. Wireless Personal Communications, 112(4), 2741-2760.Google ScholarDigital Library
- Wu, H., Li, Q., Zhu, H., Han, X., Li, Y., & Yang, B. (2020). Directional sensor placement in vegetable greenhouse for maximizing target coverage without occlusion. Wireless Networks, 26(6), 4677-4687.Google ScholarDigital Library
- Evangelou, E., Stamatiadis, S., Schepers, J. S., Glampedakis, A., Glampedakis, M., Dercas, N., ... & Nikoli, T. (2020). Evaluation of sensor-based field-scale spatial application of granular N to maize. Precision Agriculture, 21(5), 1008-1026.Google ScholarCross Ref
- Vogel, S., Bönecke, E., Kling, C., Kramer, E., Lück, K., Philipp, G., ... & Gebbers, R. (2022). Direct prediction of site-specific lime requirement of arable fields using the base neutralizing capacity and a multi-sensor platform for on-the-go soil mapping. Precision Agriculture, 23(1), 127-149.Google ScholarCross Ref
- Fei, S., Hassan, M. A., Xiao, Y., Su, X., Chen, Z., Cheng, Q., ... & Ma, Y. (2022). UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precision agriculture, 1-26.Google Scholar
- Gheisari, M., Yaraziz, M. S., Alzubi, J. A., Fernández-Campusano, C., Feylizadeh, M. R., Pirasteh, S., ... & Lee, C. C. (2022). An efficient cluster head selection for wireless sensor network-based smart agriculture systems. Computers and Electronics in Agriculture, 198, 107105.Google ScholarDigital Library
- Xie, C., Yang, L., Zhang, D., Cui, T., Zhang, K., He, X., & Du, Z. (2022). Design of smart seed sensor based on microwave detection method and signal calculation model. Computers and Electronics in Agriculture, 199, 107178.Google ScholarDigital Library
- Partel, V., Costa, L., & Ampatzidis, Y. (2021). Smart tree crop sprayer utilizing sensor fusion and artificial intelligence. Computers and Electronics in Agriculture, 191, 106556.Google ScholarDigital Library
- Wongchai, A., Shukla, S. K., Ahmed, M. A., Sakthi, U., & Jagdish, M. (2022). Artificial intelligence-enabled soft sensor and internet of things for sustainable agriculture using ensemble deep learning architecture. Computers and Electrical Engineering, 102, 108128.Google ScholarDigital Library
- Patle, K. S., Panchal, V., Saini, R., Agrawal, Y., & Palaparthy, V. S. (2022). Temperature compensated and soil density calibrated soil moisture profiling sensor with multi-sensing point for in-situ agriculture application. Measurement, 201, 111703.Google ScholarCross Ref
- Bayrakdar, M. E. (2020). Employing sensor network based opportunistic spectrum utilization for agricultural monitoring. Sustainable Computing: Informatics and Systems, 27, 100404.Google ScholarCross Ref
- Duncan, L., Miller, B., Shaw, C., Graebner, R., Moretti, M. L., Walter, C., ... & Udell, C. (2022). Weed Warden: A low-cost weed detection device implemented with spectral triad sensor for agricultural applications. HardwareX, 11, e00303.Google ScholarCross Ref
- Din, A., Ismail, M. Y., Shah, B., Babar, M., Ali, F., & Baig, S. U. (2022). A deep reinforcement learning-based multi-agent area coverage control for smart agriculture. Computers and Electrical Engineering, 101, 108089.Google ScholarDigital Library
- Uyeh, D. D., Akinsoji, A., Asem-Hiablie, S., Bassey, B. I., Osinuga, A., Mallipeddi, R., ... & Park, T. (2022). An online machine learning-based sensors clustering system for efficient and cost-effective environmental monitoring in controlled environment agriculture. Computers and Electronics in Agriculture, 199, 107139.Google ScholarDigital Library
- https://catalog.data.gov/dataset/data-from-a-field-scale-sensor-network-data-set-for-monitoring-and-modeling-the-spatial-anGoogle Scholar
Recommendations
A Research on Information Management in Reliable Logistics Supply Chain of Tropical Agricultural Production
ICETCE '12: Proceedings of the 2012 Second International Conference on Electric Technology and Civil EngineeringAs a primary factor of production and the material serving agriculture directly, agricultural production plays an important role in agriculture. Agricultural production quality and safety is of most importance in ensuring agriculture security, it ...
Combined disaggregation of agricultural land uses, livestock numbers and crops' production: an entropy approach
MACMESE'10: Proceedings of the 12th WSEAS international conference on Mathematical and computational methods in science and engineeringThis paper presents several combined agricultural data disaggregation models in order to recover the farms' land uses, the livestock numbers and main crops' productions. The proposed approach estimates incomplete information at disaggregated level ...
Viable smart sensors and their application in data driven agriculture
Highlights- Overview of viable smart sensors that are used in field cultivation processes, greenhouse automation and hydroponics farming.
AbstractSmart sensors are useful in professional farming approach by which one can use the digital technology to monitor, visualize, generate digital data, to control the application of resources, to improve quality and productivity of ...
Comments