Irrigation decision method for winter wheat growth period in a supplementary irrigation area based on a support vector machine algorithm

Shen, Hongzheng; Jiang, Kongtao; Sun, Weiqian; Xu, Yue

doi:10.1016/j.compag.2021.106032

Cited by 18 publications

(3 citation statements)

References 21 publications

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“…In order not to lose the yield of winter wheat in extreme weather conditions (drought), it is necessary to select drought-resistant varieties and introduce elements of biologization with the use of fertilizers and integrated plant protection growth regulators, as well as biological products into the cultivation technology. Our research has shown that the use of growth regulators and biopreparations improves biometrics and winter wheat yield structures, thus improving yields [23][24][25][26].…”

Section: Discussionmentioning

confidence: 99%

Ways to Improve the Adaptability of Winter Wheat in the Eastern Part of the Northern Steppe of Ukraine

Vinyukov¹,

Chuhrii²,

Gyrka³

et al. 2022

ujar

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The research was conducted to study the impact of new nutrient complexes on improving the yield of winter wheat and its adaptability in the eastern part of the Northern Steppe of Ukraine. The article aimed to determine the adaptive properties of winter wheat plants of various breeding centers in the eastern Northern Steppe of Ukraine, as well as to improve the technology of growing winter wheat through the use of new nutrient complexes. To achieve this goal, an algorithm of graphical analysis was used. It is established that the introduction of a new nutrient Complex 3 into the winter wheat cultivation technology, which includes: Sizam (250 g/t), Phosphoenterin (133.3 g/t), Diazophyte (133.3 g/t) Biopolicide (133.3 g/t) in the conditions of the eastern part of the Northern Steppe of Ukraine provides a yield increase on a mineral (N 30 Р 30 К 30 ) nutrition background of 1.22 t/ha, on organo-mineral (N 15 Р 15 К 15 +biohumus (250 kg/ha)) -0.78 t/ha and organic (biohumus -250 kg/ha) nutrition background compared to the control. There was also a significant improvement in biometrics and the structure of winter wheat yields. A method for analysing the elements of productivity and plasticity of winter wheat is proposed, which allows the selection of drought-resistant varieties suitable for cultivation in the Northern Steppe of Ukraine. Also, it is determined that using a patented graphical algorithm for analyzing the level of ecological plasticity of cereal varieties due to the uniformity of environmental factors on the productivity of cereals allows for determining the most adapted plants. Thus, the varieties of winter wheat Bohynia and Oleksiivka were identified as the most flexible.

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Section: Discussionmentioning

confidence: 99%

Ways to Improve the Adaptability of Winter Wheat in the Eastern Part of the Northern Steppe of Ukraine

Vinyukov¹,

Chuhrii²,

Gyrka³

et al. 2022

ujar

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“…In Shen et al [14], the water saving irrigation scheme for winter wheat depending upon the DSSAT module and GA is improved for distinct historical years . Hence, a decision making technique to defining either for irrigating development phase of winter wheat was established by SVM method depending upon quantity of precipitations in the initial phase of winter wheat and the quantity of irrigations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For algal cells of smaller algal colonies in an evolution procedure, algal cell of main algal colony is equated by Eqs. ( 12)- (14).…”

Section: Population Of Algal Colonymentioning

confidence: 99%

Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

Abuzanouneh¹,

Al‐Wesabi²,

Albraikan³

et al. 2022

Computers, Materials &Amp; Continua

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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 irrigation. The proposed IoTML-SIS model involves different IoT based sensors for soil moisture, humidity, temperature sensor, and light. Besides, the sensed data are transmitted to the cloud server for processing and decision making. Moreover, artificial algae algorithm (AAA) with least squares-support vector machine (LS-SVM) model is employed for the classification process to determine the need for irrigation. Furthermore, the AAA is applied to optimally tune the parameters involved in the LS-SVM model, and thereby the classification efficiency is significantly increased. The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

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Optimal land allocation and irrigation scheduling to maximize the economic utility

Kahkhamoghaddam,

Ziaei,

Davary

et al. 2024

Int. J. Plant Prod.

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Attaining sustainable agriculture requires water consumption management. A water allocation optimization model was developed for the Moghan irrigation network (northwest of Iran) based on the AquaCrop plug-in model. The genetic algorithm was applied to optimize water allocation for ve main crops, including wheat, rst-cultivation maize, second-cultivation maize, soybeans, and alfalfa. The heuristic economic utility (EU) function was used as the objective function to optimize water allocation. In this function, drained water salinity was applied as a penalty factor to the total bene t, and soil salinity deterioration due to irrigation was also considered as a factor in each crop's bene t. The results showed that the optimal allocated water depth was 17% less than the normal water consumption. Moreover, the application of soil water salinity coe cients did not affect the ratio of EU to EB (economic bene ts) for wheat and alfalfa. However, rst-cultivation maize, second-cultivation maize, and soybeans cultivation led to a reduction in EU within the study area. A combination of the crops cultivation led to a change in river water quality and an 8.2% reduction in the ratio of EU to EB function.

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Irrigation decision method for winter wheat growth period in a supplementary irrigation area based on a support vector machine algorithm

Cited by 18 publications

References 21 publications

Ways to Improve the Adaptability of Winter Wheat in the Eastern Part of the Northern Steppe of Ukraine

Ways to Improve the Adaptability of Winter Wheat in the Eastern Part of the Northern Steppe of Ukraine

Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

Optimal land allocation and irrigation scheduling to maximize the economic utility

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