Linear Modeling of the Impact of Pests and Diseases on the Growth Process of Strawberry Plants in the State Space

Document Type : Research Article

Authors

1 Electrical Control Engineering Department, University of Kashan, Kashan, Iran

2 Electrical and Electronic Engineering Department, Shahed University, Tehran, Iran

3 Department of Horticultural Science, Islamic Azad University, Yasuj Branch, Yasuj, Iran and Sustainable Agriculture and Food Security Research Group, Yasuj Branch, Islamic Azad University, Yasuj, Iran

Abstract

Introduction
 Strawberry is a significant fruit due to its rich sources of vitamins and plenty of antioxidants and grows worldwide. Thus, studies that provide information on topics in strawberry growth are momentous. Strawberry production is often threatened by various pests. Therefore, pest management is one of the most critical points in strawberry cultivation and production that have to be considered. There are several pests that can have detrimental effects on strawberry production. For example, thrips is an important pest for strawberry in a greenhouse condition. There are many ways to deal with greenhouse pests; one of the best ways is to observe greenhouse hygiene and sanitation and use the proper chemical pesticides. Yet, it is impossible to predict the plant's growth stages in the usual ways to predict the time of using chemical pesticides. The time of each growth stage's occurrence can be predicted only by using simulation models and considering the factors affecting plant growth, such as environmental factors, pests, disease, and nutrition. To this end, this study aims to model the growth process of strawberry plants by considering pests and diseases' impact.
Materials and Methods
 Monitoring the growth stages is a critical component of a successful strawberry growth program. To harvest more, it is vital to develop effective predicting tools. There are several approaches to investigate the change in the size and form of planets based on some growth conditions over time. The best-known mechanism of analyzing the growth process mathematically is simulation modeling. Models can be used as an alternative for statistical analysis of harvest of crops. They enhance the inferences about productive behavior and can be used to evaluate experiments. To introduce a desirable model, it is required to identify the essential features of the growth process, such as environment and climate. In this work, the dynamics of growing strawberry, the evolution of their size in time and their forms are modeled. Meanwhile, the plant's reaction to environmental factors such as pests and effective management factors on the environment and climate created for plant growth is investigated.
Results and Discussion
 The use of growth models increases the analysis that can be made about the productive behavior of strawberry plants. Moreover, the proposed model has efficiency in predicting plant growth. This model, including simple relationships and the general concept of growth, is useful for teaching, learning, and researching to analyze plant growth factors. Furthermore, the proposed model can study how plants react to environmental factors such as pests and significant management factors on the environment and climate of plant growth. Besides, the model can help scholars, researchers, and plant producers with the ability to predict the amount and manner of plant growth and the effect of changing the number of pests. In this paper, the amount of disease and pests' damage for different rates in the growth stages is shown by using the plant growth process's modeling to reduce the amount of damage caused by pests by predicting the amount of damage and provide applicable, low cost and practical solutions. According to the results obtained in this study, since the lack of pest control causes only four out of ten healthy fruits to reach the full growth stage, control and repel pests are essential.
Conclusion
 From the results of this study, it can be concluded that greenhouse owners and producers can simulate several growing seasons in a matter of seconds and predict the amount of harvest and loss of their crop by considering the environmental information of the area of study in which they decide to plant and knowing the effective rates in the process of plant growth and having a model. The greenhouse owners can predict the number of healthy and infected plants using the desired model before harvest by using the help of horticultural experts, collecting information about their environment, climate, soil, etc., and using different aspects affecting plant growth in the desired area. This approach can be extended to other crops to investigate the treatment effects and the production behavior throughout the crop cycle. Using this method predicts the crop cycle and leads to fruit production in a shorter period because the producer can use pesticides at the right time of production to decrease the damages to pests.
 

Keywords

Main Subjects


  1. Abbasi Z., Zamani I., Amiri Mehra A.H., Shafieirad M., and Ibeas A. 2020. Optimal Control Design of Impulsive SQEIAR Epidemic Models with Application to COVID-19. Chaos, Solitons & Fractals 139: 110054. https://doi.org/10.1016/j.chaos.2020.110054.
  2. Amiri Mehra A.H., Zamani I., Abbasi Z., and Ibeas A. 2019. Observer-Based Adaptive PI Sliding Mode Control of Developed Uncertain SEIAR Influenza Epidemic Model Considering Dynamic Population. Journal of Theoretical Biology 482: 109984. https://doi.org/10.1016/j.jtbi.2019.08.015.
  3. Amiri Mehra A.H., Shafieirad M., Abbasi Z., and Zamani I. 2020. Parameter Estimation and Prediction of COVID-19 Epidemic Turning Point and Ending Time of a Case Study on SIR/SQAIR Epidemic Models. Computational and Mathematical Methods in Medicine, 2020. https://doi.org/10.1155/2020/1465923.
  4. Abbasi Z., Zamani I., Amiri Mehra A.H., Ibeas A., and Shafieirad M. 2021. Optimal Allocation of Vaccine and Antiviral Drugs for Influenza Containment over Delayed Multi-Scale Epidemic Model Considering Time-Dependent Transmission Rate. Computational and Mathematical Methods in Medicine, 2021. https://doi.org/10.1155/2021/4348910.
  5. Andarzian B., Bakhshandeh A., Fathi Q., Khalil Alemi S., Banayan M., and Imam Y. 2007. A Model for Simulating the Developmental Stages of Crops. Agriculture and Horticulture 71-79. (In Persian)
  6. Behtri B. 2014. Mathematical Models of Crop Growth and Operation. 228 pages. (In Persian)
  7. Bessonov N., and Volpert V. 2000. Dynamical Models of Plant Growth, Mathematics Subject Classification.
  8. Bessonov N., Morozova N., and Volpert V. 2008. Modeling of Branching Patterns in Plants. Bulletin of Mathematical Biology 70(3): 868-893. https://doi.org/10.1007/s11538-007-9282-1.
  9. Danaeifar A., Gholami M., Mobli M., and Bani Nasab B. The Effect of Paclobutrazol and Calcium Prohexadione on Some Physiological Characteristics and Quality of Strawberry Fruit of Parus Cultivar. Iranian Journal of Horticultural Science and Technology 21(1): 1-10. (In Persian with English abstract). http://dx.doi.org/10.29252/jcpp.9.2.1.
  10. Dihimfard R., Nasiri Mahallati M., and Kouchaki A. 2012. A Simple Model to Simulate the Growth, Development and Yield of Sugar Beet in Terms of Potential and Nitrogen Limitation. Journal of Ecological Agriculture 1-20.
  11. Ghaem Maghami F., Zarei M., Yathribi J., and Eshghi S. 2019. The Effect of Different Levels of Nitrogen, Vermicompost and Nitrogen on Morphological Characteristics. Greenness Index and Strawberry Yield in Greenhouse Conditions, Iranian Journal of Horticultural Sciences and Techniques 251-262. (In Persian). https://civilica.com/doc/1160179/.
  12. Godin C., and Caraglio Y. 1998. A Multiscale Model of Plant Topological Structures. Journal of Theoretical Biology 191(1): 1-46. https://dx.doi.org/10.1006/jtbi.1997.0561.
  13. Hosseini Farahi M., Jamshidi E., Amiri S., Kamyab F., and Radi M. 2020. Quality, Phenolic Content, Antioxidant Activity, and the Degradation Kinetic of Some Quality Parameters in Strawberry Fruit Coated with Salicylic Acid and Aloe vera Journal of Food Processing and Preservation 44(9): 14647. https://doi.org/10.1111/jfpp.14647.
  14. Kuhar T., and Pfeiffer D. 2009. Insect Pests of Strawberries and Their Management, Virginiafruit.ento.vt.edu.
  15. Ikegawa Y., Mori K., Ohasa M., Fujita I., Watanabe T., Ezoe H., and Namba T. 2016. A Theoretical Study on Effects of Cultivation Management on Biological Pest Control: A Spatially Explicit Model. Biological Control 93: 37-48. https://doi.org/10.1016/j.biocontrol.2015.11.008.
  16. Jaeger M., and Reffye D. 1992. Basic Concepts of Computer Simulation of Plant Growth. Journal of Biosciences 17: 275-291. http://dx.doi.org/10.1007/BF02703154.
  17. Salimi F., Ahmadian A., Alipnah M., and Kaveh H. 2016. Strawberries, an Alternative Product for Sustainable Agriculture. The First National Symposium on Small Fruits, Shiraz University 179-185. (In Persian)
  18. Varenne F. 2018. From Models to Simulations. London, UK: Routledge, 244. https://doi.org/10.4324/9781315159904
  19. Zamani I., and Hosseini Farahi M. 2016. A Hybrid State Space Modelling Study on Effects of Cultivation Management on Biological Pest Control for Strawberry Plants. VIII International Strawberry Symposium 1156: 817-820. https://doi.org/10.17660/ActaHortic.2017.1156.120.

 

CAPTCHA Image
  • Receive Date: 13 June 2021
  • Revise Date: 17 July 2021
  • Accept Date: 30 August 2021
  • First Publish Date: 31 August 2021