Evaluation of Vegetation Index of Greenhouse Tomato and Cucumber using non-destructive Sensors

ماشین های کشاورزی

Article 7, Volume 9, Issue 1 - Serial Number 17, 2019, Pages 87-98   

Document Type: Research Article

DOI: 10.22067/jam.v9i1.67383

Authors

B. Sepehr; H. Mohamadi-Monavar^*

Mechanics of Biosystem Engineering Department,, Bu-ali Sina University, Hamadan, Iran

Abstract

Introduction
One of the most important factors in agricultural production is nitrogen which has a great impact on plant growing, yield performance and plant quality production. The optimum amount of nitrogen fertilizer is varied from fields to fields. There are some time consuming and costly ways to measure the nitrogen content of plants or soil, which are inappropriate for extended field or for a long growing season. Fast and remote optical sensors calculate greenness of plant using reflectance or absorbance of light from green leaves. Measuring chlorophyll with SPAD managed the nitrogen requirement for maize, Poinsettia and Nagoya Red. Whereas SPAD was not a suitable choice for chlorophyll measurement at the end of growing period. Therefore, GreenSeeker was applied as a non-contact to record the NDVI of tomato’s and cucumber’s leaves. The purpose of this research was the evaluation of GreenSeeker ability to estimate nitrogen requirement and then the plant health.
 
Materials and Methods
The study was performed on Matin and Nahid cultivars of tomato and cucumber, respectively. The pots were 291 and filled with 3 kg sieved soil. The bottom layer of each pot was filled with stone for better drainage. Before planting, the soil was analyzed in order to define the ingredients. All pots put in the greenhouse with polycarbonate structure in two floors. Measurements were repeated every week with SPAD and GreensSeeker and fertigation was started 50 days after planting (DAP). In order to provide other nutrient elements, all pots got Humic-acid at 37DAP and the effect was measured in 43rd DAP. Fertigation was continued until 71st DAP and first, second and third treatments were supplemented with extra fertilizer to reach the amount of fertilizer to fifth treatment. To calculate Total Nitrogen (TN), the concentrations of nitrate-N and nitrite-N are determined and added to the total Kjeldahl nitrogen. Chlorophyll meter (SPAD) and GreenSeeker optical sensor have become available for site-specific and need-based N management in greenhouse. The GS was located at 60 cm above the plant and measured the average NDVI. This sensor has red and NIR diodes which reflect and absorb the spectra in 660±15nm and 770±15nm regions, respectively. The SPAD values were recorded by inserting the middle portion of the index leaf in the slit of SPAD meter. As well as, chlorophyll meter can confirm the GreenSeeker output (NDVI). GreenSeeker is a suitable optical sensor because it is not affected by light and temperature variation or wind intensity.
Statistical analyses were performed on the pooled data of both tomato and cucumber using Statistical Product and Service Solutions (SPSS). Regression equations were fitted between fertilizer and the readings recorded with different gadgets at different growth stages.
 
Results and Discussion
Chlorophyll content and NDVI of tomato and cucumber increased during the growing stages except in 71st DAP for cucumber. The percentage of total nitrogen of 1st, 2nd and 3rd treatments were further than two others because of supplementary fertilizer. According to the Kjeldahl result of cucumber, the 3rd treatment had the lowest nitrogen accumulation in fruits. In addition, chlorophyll and NDVI of cucumber almost showed the increasing correlation by fertilizer enhancement while the opposite behavior was seen for tomato. That would be related to different fertilizer needs of them. The linear regression of fertilizer and reading NDVI of 2nd to 5th treatments were ascending. The number of increasing leaves was calculated in all pots every weeks as another studied element. Each pot had new grown leaves every weeks that was more or sometimes less than last weeks. However, accurate correlation coefficient was reported with NDVI in all treatments, whereas chlorophyll did not show a direct relation.
 
Conclusion
The result of the study confirmed the useful GreanSeeker as an accurate and fast technology for prediction of NDVI. Among different fertilizer treatments of cucumber, 3rd one showed the acceptable results. Since tomatoes did not reach to fertility stage, it would not possible to extract the best nitrogen fertilizer treatments. It is obvious that evaluation of pots in complete growth stages reach us to codify manual fertilization.

Keywords

Chlorophyll; GreenSeeker; Nitrogen; Normalized Difference Vegetation Index (NDVI); SPAD

References

1. Basyouni, R., B. Dunn, and C. Goad. 2015. Use of nondestructive sensors to assess nitrogen status in potted poinsettia (Euphorbia pulcherrima L. (Willd. ex Klotzsch)) production. Scientia Horticulture192: 47-53.

2. Campillo, R., C. Jobet, and P. Undurraga. 2010. Effects of nitrogen on productivity, grain quality, and optimal nitrogen rates in winter wheat cv. Kumpa-Inia in Andisoles of southern Chile. Chilean Journal of Agricultural Research 70 (1): 122-131.

3. Dunn, B., A. Shrestha, and C. Goad. 2016. Use of Nondestructive Sensors to Quantify Ornamental Kale Nitrogen Status. Journal of Plant Nutrition 39: 1123-1130

4. Gertsis, A., D. Fountas, I. Arpasanu, and M. Michaloudis. 2013. Precision agriculture applications in a high density olive grove adapted for Mechanical Harvesting in Greece. Procedia Technology 8: 152-6.

5. Gianquinto, G., F. Orsini, M. Fecondini, M. Mezzetti, P. Sambo, and S. Bona. 2011. A methodological approach for defining spectral indices for assessing tomato nitrogen status and yield. Agronomy 35 (3): 135-43.

6. He, F., Q. Chen, R. Jiang, X. Chen, and F. Zhang. 2007. Yield and nitrogen balance of greenhouse tomato (Lycopersicum esculentum Mill.) with conventional and site-specific nitrogen management in Northern China. Nutrient Cycling in Agroecosystems 77: 1-14.

7. Hasibi, P. 2007. Physiological study of the effect of cold stress on seedling stage of different genotypes of rice. PhD thesis, Faculty of Agriculture. Shahid Chamran University, Ahvaz. Iran.

8. Hunt Jr, E. R., P. C. Doraiswamy, J. E. McMurtrey, S. T. C. Daughtry, M. E. Perry, and B. Akhmedov. 2013. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geo-information 21: 103-112.

9. Institute of Standards and Industrial Research of Iran (ISIRI Number 7832), Soil quality–Determination of total nitrogen–Modified kjeldahl method. 1st revision.

10. Kitchen, N. R., K. A. Sudduth, S. T. Drummond, P. C. Scharf, H. L. Palm, D. F. Roberts, and E. D. Vories. 2010. Ground-based canopy reflectance sensing for variable-rate nitrogen corn fertilization. Agronomy 102: 71-84.

11. Kordi, S., A. Fadavi, M. Eskandari, M. Berari, M. Rafiee, and A. Ashraf Mehrabi. 2014. Effect of Urea fertilization and moisture of harvesting time on final mechanical properties of dried corn. Journal of Agricultural Machinery 4 (1): 1-10.

12. Miao, Y., D. J. Mulla, J. A. Hernandez, M. Wiebers, and P. C. Robert. 2007. Potential impact of precision nitrogen management on corn yield, protein content, and test weight. Soil Science Society of America Journal 71: 1490-1499.

13. Muñoz-Huerta, R. F., R. G. Guevara-Gonzalez, L. M. Contreras-Medina, I. Torres-Pacheco, J. Prado-Olivarez, and R. V. Ocampo-Velazquez. 2013. A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances. Sensors 13 (8): 10823-10843.

14. Padilla, F. M., M. T. Peña-Fleitas, M. Gallardo, and R. B. Thompson. 2014. Evaluation of optical sensor measurements of canopy reflectance and of leaf flavones and chlorophyll contents to assess crop nitrogen status of muskmelon. Agronomy 58: 39-52.

15. Padilla, F. M., M. T. Peña-Fleitas, M. Gallardo, and R. B. Thompson. 2016. Proximal optical sensing of cucumber crop N status using chlorophyll fluorescence indices. Agronomy 73: 83-97.

16. Padilla, F. M., M. T. Peña-Fleitas, M. Gallardo, and R. B. Thompson. 2017. Determination of sufficiency values of canopy reflectance vegetation indices for maximum growth and yield of cucumber. Agronomy 84: 1-15.

17. Prost, L., and M. H. Jeuffroy. 2007. Replacing the nitrogen nutrition index by the chlorophyll meter to assess. Agronomy for Sustainable Development 27 (4): 321-330.

18. Rambo, L., B. L. Ma, Y. Xiong, and P. Regis Ferreira da Silvia. 2010. Leaf and canopy optical characteristics as crop‐N‐status indicators for field nitrogen management in corn. Plant Nutrition and Soil Science 173 (3): 434-443.

19. Ruiz, J. M., and L. Romero. 2000. Nitrogen metabolism and yield response of cucumber (Cucumis sativus L cv Brunex) plants to phosphorus fertilization. The Science of Food and Agriculture 80 (14): 2069-2073.

20. Salehi, M., A. Kouchaki, and M. Nasiri Mahalati. 2005. Leaf nitrogen and chlorophyll content as indicators for salt stress. Iranian Journal of Field Crops Research 2 (1): 25-33.

21. Samborski, S. M., D. Gozdowski, M. Stępień, O. S. Walsh, and E. Leszczyńska. 2016. On-farm evaluation of an active optical sensor performance for variable nitrogen application in winter wheat. European Journal of Agronomy 74: 56-67.

22. Tremblay, N., M. Bouroubi, P. Vigneault, and C. Belec. 2011. Guidelines for in-season nitrogen application for maize (Zea mays L.) based on soil and terrain properties. Field Crops Research 122: 273-283.

23. Unlu, H. O., U. Husnu, and Y. Karakurt. 2011. Changes in fruit yield and quality in response to foliar and soil humic acid application in cucumber. Scientific Research and Essays 6 (13): 2800-2803.

24. Usha, K., and B. Singh. 2013. Potential applications of remote sensing in horticulture, a review. Scientia Horticulture 153: 71-83.

25. Vouillot, M. O., P. Huet, and P. Boissard. 1998. Early detection of N deficiency in a wheat crop using physiological and radiometric methods. Agronomie 18: 117-130.

26. Wu, C., Z. Niu, and S. Gao. 2012. The potential of satellite derived green chlorophyll index for estimating midday light use efficiency in maize, coniferous forest and grassland. Ecological Indicators 14: 66-73

27. Yu, K., V. Lenz-Wiedemann, X. Chenand, and G. Bareth. 2014. Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects. ISPRS Journal of Photogrammetry and Remote Sensing 97: 58-77.

28. Zhang, Y., L. Zheng, H. Sun, and W. Yang. 2015. An optical detector for determining chlorophyll and nitrogen concentration based on photoreaction in apple tree leaves. Intelligent Automation and Soft Computing 21 (3): 409-421.