Enhancing production of Zea mays genotypes by K application in Peshawar, Pakistan

Ahmad, Uzair; Begum, Unab

doi:10.18805/ijare.v51i03.7923

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…In the face of these problems, the traditional PID control gradually reveals its shortcomings and cannot obtain the expected control effect. Therefore, combining an intelligent optimization algorithm with PID technology to improve the performance of traditional PID systems and effectively solve the problem of precise control in integrated water and fertilizer control systems is of great significance to improve the quality of field crops, increase farmers' income, and improve economic benefits as well as improve the soil environment and realize the ecologically sustainable development of field crop production [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Research and Design of Hybrid Optimized Backpropagation (BP) Neural Network PID Algorithm for Integrated Water and Fertilizer Precision Fertilization Control System for Field Crops

Zhu

Zhang

et al. 2023

Agronomy

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China’s field crops such as cotton, wheat, and tomato have been produced on a large scale, but their cultivation process still adopts more traditional manual fertilization methods, which makes the use of chemical fertilizers in China high and causes waste of fertilizer resources and ecological environmental damage. To address the above problems, a hybrid optimization of genetic algorithms and particle swarm optimization (GA–PSO) is used to optimize the initial weights of the backpropagation (BP) neural network, and a hybrid optimization-based BP neural network PID controller is designed to realize the accurate control of fertilizer flow in the integrated water and fertilizer precision fertilization control system for field crops. At the same time, the STM32 microcontroller-based precision fertilizer application control system for integrated water and fertilizer application of large field crops was developed and the performance of the controller was verified experimentally. The results show that the controller has an average maximum overshoot of 5.1% and an average adjustment time of 68.99 s, which is better than the PID and PID control algorithms based on BP neural network (BP–PID) controllers; among them, the hybrid optimization of PID control algorithm based on BP neural network by particle swarm optimization and genetic algorithm(GA–PSO–BP–PID) controller has the best-integrated control performance when the fertilizer application flow rate is 0.6m3/h.

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Section: Introductionmentioning

confidence: 99%

Research and Design of Hybrid Optimized Backpropagation (BP) Neural Network PID Algorithm for Integrated Water and Fertilizer Precision Fertilization Control System for Field Crops

Zhu

Zhang

et al. 2023

Agronomy

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“…To improve water use efficiency, it is important to monitor particular factors that influence crop growth and development [15][16][17]. Monitoring in the perspective of smart irrigation also entails real-time data acquisition related to soil, plant, and weather parameters.…”

Section: Introductionmentioning

confidence: 99%

Solar Fertigation: A Sustainable and Smart IoT-Based Irrigation and Fertilization System for Efficient Water and Nutrient Management

2022

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The agricultural sector is one of the major users of water resources. Water is an important asset that needs to be preserved using the latest available technologies. Modern technologies and digital tools can transform the agricultural domain from being manual and static to intelligent and dynamic leading to higher production with lesser human supervision. This study describe the agronomic models that should be integrated with the intelligent system which schedule the irrigation and fertilization according to the plant needs, and monitors and maintains the desired soil moisture content via automatic watering. Solar fertigation is a fertigation support system based on photovoltaic solar power energy and an IoT system for precision irrigation purposes. The system monitors the temperature, radiation, humidity, soil moisture, and other physical parameters. An agronomic DSS platform based on the integration of soil, weather, and plant data and sensors was described. Furthermore, a three-year study on seven ETo models, such as three temperature-, three radiation-, and a combination-based models were tested to evaluate the sustainable ETo estimation and irrigation scheduling in a Mediterranean environment. Results showed that solar fertigation and Hargreaves–Samani (H-S) equation represented a nearby correlation to the standard FAO P–M and does offer a small increase in accuracy of ETo estimates. Furthermore, the hybrid agronomic DSS is suitable for smart fertigation scheduling.

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Enhancing production of Zea mays genotypes by K application in Peshawar, Pakistan

Cited by 2 publications

References 1 publication

Research and Design of Hybrid Optimized Backpropagation (BP) Neural Network PID Algorithm for Integrated Water and Fertilizer Precision Fertilization Control System for Field Crops

Research and Design of Hybrid Optimized Backpropagation (BP) Neural Network PID Algorithm for Integrated Water and Fertilizer Precision Fertilization Control System for Field Crops

Solar Fertigation: A Sustainable and Smart IoT-Based Irrigation and Fertilization System for Efficient Water and Nutrient Management

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