ارزیابی روش‌های مختلف تعیین سطح سایه‌انداز ذرت علوفه‌ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دوره دکتری آبیاری و زهکشی، گروه علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه بین المللی امام خمینی (ره)، قزوین، ایران.

2 عضو هیات علمی، گروه علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه بین المللی امام خمینی (ره)، قزوین، ایران.

3 کارشناس گروه آبیاری و آبادانی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

چکیده

شاخص‌های گیاهی به‌خوبی شرایط گیاه در مزرعه را بیان می‌کنند. از آنجاکه سطح سایه‌انداز (CC) با فعالیت فتوسنتزی گیاه همبستگی دارد، هدف از مطالعه حاضر بررسی دقت دو روش تعیین CC ذرت در طول مراحل مختلف رشد در مزرعه ذرت علوفه‌ای در قزوین توسط نرم‌افزار ENVI و مدل Canopeo و مقایسه نتایج حاصل با مقادیر حاصل از مدل Aquacrop است. در فواصل زمانی مختلف در طول فصل رشد گیاه ذرت تصویر برداری در چهار حالت: 1) از بالا به پایین بدون لنز چشم ماهی، 2) بالا به پایین با لنز چشم ماهی، 3) پایین به بالا بدون لنز چشم ماهی و 4) پایین به بالا با لنز چشم ماهی انجام شد. مقدار CC در تصاویر حاصله توسط سه الگوریتم حداکثر احتمال، حداقل فاصله و روش موازی در ENVI تعیین شد. ابتدا ارزیابی کیفی کلاس‌بندی تصاویر در سه الگوریتم نامبرده صورت گرفت. نتایج بیانگر دقت بیشتر الگوریتم حداکثر احتمال نسبت به دو الگوریتم دیگر بود. ارزیابی آماری نتایج کمی حاصل از ENVI بیانگر دقت بالا مدل در الگوریتم حداکثر احتمال بود( ضریب کاپا > 82/0، دقت کلی > 93، Commision و Omission حداقل مقدار). حداقل مقدار RMSE به ترتیب مربوط به سطح سایه‌انداز برآورد شده توسط نرم‌افزار Canopeo در تصویر برداری از پایین به بالا با لنز(92/9) بود. به طورکلی می‌توان دریافت که تصویر برداری پایین به بالا بدون لنز (Canopeo) (R=0.8 و 11.81=RMSE ) و تصویربرداری از بالا به پایین با لنز (ENVI) (R=0.82 و 13.26=RMSE ) نسبت به سایر حالت‌ها در تعیین سطح سایه‌انداز توانمندتر بوده‌اند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation of different methods of determining the Canopy Cover of Silage Maize

نویسندگان [English]

  • Zahra Partovi 1
  • Abbas Kaviani 2
  • hadi ramezani etedali 2
  • masoud soltani 2
  • leila khosravi 3
1 Water Sci. & Eng. Dept., Faculty of agriculture and natural Res., Imam Khomeini International University, Qazvin, Iran.
2 Water Sci. & Eng. Dept., Faculty of agriculture and natural Res., Imam Khomeini International University, Qazvin, Iran.
3 Staff of Irrigation and reclamation Dept., Faculty of agriculture and natural resources, University of Tehran, Karaj, Iran
چکیده [English]

Vegetation indices effectively represent plant conditions in the field. Since Canopy Cover (CC) correlates with the plant’s photosynthetic activity, this study aimed to evaluate the accuracy of two methods for determining CC in silage maize during different growth stages in a maize field in Qazvin using ENVI software and the Canopeo model and to compare the results with values obtained from the AquaCrop model. Imaging was conducted at different time intervals throughout the maize growing season in four scenarios: 1) top-down without a fisheye lens, 2) top-down with a fisheye lens, 3) bottom-up without a fisheye lens, and 4) bottom-up with a fisheye lens. The CC values in the obtained images were determined using three algorithms: maximum likelihood, minimum distance, and parallel method in ENVI. Initially, a qualitative assessment of image classification was performed using the three mentioned algorithms. The results indicated that the maximum likelihood algorithm had higher accuracy compared to the other two algorithms. The Statistical evaluation of the quantitative results from ENVI demonstrated high model accuracy in the maximum likelihood algorithm (Kappa coefficient > 0.82, overall accuracy > 93%, and minimal Commission and Omission errors). The lowest RMSE value was observed for CC estimated using the Canopeo software with bottom-up imaging with a lens (9.92). In general, it was found that bottom-up imaging without a lens (Canopeo) (R=0.8 and RMSE=11.81) and top-down imaging with a lens (ENVI) (R=0.82 and RMSE=13.26) were more capable in determining CC than the other Scenarios.

کلیدواژه‌ها [English]

  • Canopy Cover
  • Canopeo
  • ENVI

EXTENDED ABSTRACT

 

Introduction:

Increasing population, climate change, and reduced access to water resources have intensified the need to increase yield per unit area more than ever. Generally, crop yield is influenced by factors such as the environment, management practices, and genotype. Therefore, evaluating plant conditions in the field to achieve maximum yield is of great importance. Canopy cover, which is directly related to the amount of photosynthesis in the plant, is one of the important indices in the AquaCrop plant model for determining yield and management conditions in the field. Despite AquaCrop's sensitivity to CC, the default values of this index were often considered when running the model.

Purpose:

Given the importance of determining CC and the high sensitivity of the AquaCrop model, the aim of this study was to evaluate the accuracy of CC estimation by the two models, ENVI and Canopeo.

Research method:

Four imaging modes of vegetation cover were considered during the growth of corn. The imaging treatments included: 1) top-down imaging with a fisheye lens, 2) top-down imaging without a fisheye lens, 3) bottom-up imaging with a fisheye lens, and 4) bottom-up imaging without a fisheye lens. it should be pointed out that the nadir images were captured from 9 AM to 12 PM. To select the best image classification method for determining CC using ENVI, three methods were used: maximum likelihood, minimum distance, and parallel. Then the appropriate method was selected by examining the Kappa coefficient, overall accuracy, Commission, and Omission. The AquaCrop model was run under field conditions, and the standard CC value was obtained from the model. Finally, the CC obtained from the three models, AquaCrop, ENVI, and Canopeo, was evaluated using the statistical indices correlation coefficient (CC) and RMSE.

Results:

At first, the three classification algorithms in ENVI were evaluated. In the maximum likelihood method, the Kappa coefficient ranged from 0.82 to 0.97, overall accuracy ranged from 93.69 to 99.14, Commission error ranged from 17.07 to 59.44, and Omission error ranged from 4 to 19.63, indicating that its performance was better than other classification methods. The qualitative evaluation of the results from the Canopeo model also indicated sufficient accuracy in estimating CC. Comparing the results from ENVI and Canopeo with AquaCrop showed that the RMSE had the minimum value in the Canopeo model for bottom-up imaging with a fisheye lens (RMSE = 9.92). The correlation coefficient of the results from the ENVI and Canopeo models with AquaCrop was satisfactory (0.68 to 0.97). Overall, the CC determined by Canopeo for bottom-up imaging without a lens(CC=0.8, RMSE= 11.81) and the CC determined by the maximum likelihood algorithm in ENVI for top-down imaging with a fisheye lens(CC= 0.82, RMSE= 13.26) showed the best performance.

Conclusion:

The accuracy of CC estimation by Canopeo model was higher than that of ENVI ; Also, the determination of CC in Canopeo required less time than ENVI. In the images recorded from the top-bottom with a fish eye lens, the error in CC determination increased due to the increase in the number of objects and distortion. The impact of the mentioned errors were greater in the determination of CC by Canopeo.

Author Contributions

 Conceptualization, Abbas.Kaviani., Zahra.Partovi.; methodology, Abbas.Kaviani., Zahra.Partovi. and Hadi.Ramezani.Etedali.; software, Zahra.Partovi.; validation, Abbas.Kaviani., Zahra.Partovi.;formal analysis, Abbas.Kaviani., Zahra.Partovi.;investigation, Abbas. Kaviani., Zahra.Partovi.; resources, Zahra.Partovi.; data curation, Abbas. Kaviani., Zahra.Partovi., Hadi.Ramezani.Etedali., Masoud.Soltani., Leila. Khosravi.; writing—original draft preparation, Zahra.Partovi.; writing—review and editing, Abbas. Kaviani.; visualization, Zahra.Partovi.; supervision, Abbas. Kaviani., Hadi.ramezani.Etedali.; project administration, Abbas. Kaviani.; funding acquisition, Abbas. Kaviani., Leila Khosravi., All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Due to the nature of the research, due to [ethical/ legal/ commercial] supporting data is not available.

Acknowledgements

The authors feel it necessary to express their gratitude to Hezar Jolfa Agro-Industrial Company and the Iranian Space Research Institute for their sincere cooperation in advancing the present research.

Ethical considerations

The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

The author declares no conflict of interest.

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تحقیقات آب و خاک ایران
دوره 55، شماره 12
اسفند 1403
صفحه 2467-2482

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