https://doi.org/10.4081/jae.2022.1366

Monitoring mini-tomatoes growth: A non-destructive machine vision-based alternative

Vol. 53 No. 3 (2022)
Published: 9 September 2022
Deep learning, decision support system, image processing, precision agriculture.
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Authors

Fernando Ferreira Abreu
School of Agricultural Engineering, University of Campinas, Campinas, Brazil.
Luiz Henrique Antunes Rodrigues
lique@unicamp.br
School of Agricultural Engineering, University of Campinas, Campinas, Brazil.

Yield is the most often used metric of crop performance, and it can be defined as the ratio between production, expressed as a function of mass or volume, and the cultivated area. Estimating fruit’s volume often relies on manual measurements, and the procedure precision can change from one person to another. Measuring fruits’ mass will also destroy the samples; consequently, the variation will be measured with different samples. Monitoring fruit’s growth is either based on destructive tests, limited by human labour, or too expensive to be scaled. In this work, we showed that the cluster visible area could be used to describe the growth of mini tomatoes in a greenhouse using image processing in a natural environment with a complex background. The proposed method is based on deep learning algorithms and allows continuous monitoring with no contact with the cluster. The images are collected and delivered from the greenhouse using low-cost equipment with minimal parameterisation. Our results demonstrate that the cluster visible area accumulation is highly correlated (R²=0.97) with growth described by a parameterised Gompertz curve, which is a well-known growth function. This work may also be a starting point for alternative growth monitoring methods based on image segmentation. The proposed U-Net architecture, the discussion about its architecture, and the challenges of the natural environment may be used for other tasks in the agricultural context.

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Crossref
Scopus
Google Scholar
Europe PMC
Abreu F.F., Rodrigues L.H.A. 2022. MTIL - Mini tomato image library. Repositório de Dados de Pesquisa da Unicamp. Available from: https://doi.org/10.25824/redu/3CP9NK
Afonso M., Fonteijn H., Fiorentin F.S., Lensink D., Mooij M., Faber N., Polder G., Wehrens R. 2020. Tomato fruit detection and counting in greenhouses using deep learning. Front. Plant Sci. 11:571299.
Agrawal A., Mittal N. 2020. Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy. Visual Comput. 36:405-12. DOI: https://doi.org/10.1007/s00371-019-01630-9
Ali M., Gilani S.O., Waris A., Zafar K., Jamil M. 2020. Brain tumour image segmentation using deep networks. IEEE Access 8:153589-98. DOI: https://doi.org/10.1109/ACCESS.2020.3018160
Bragagnolo L., Da Silva R.V., Grzybowski J.M.V. 2021. Amazon forest cover change mapping based on semantic segmentation by U-Nets. Ecol. Inf. 62:101279. DOI: https://doi.org/10.1016/j.ecoinf.2021.101279
Chen J., Shen Y. 2017. The effect of kernel size of CNNs for lung nodule classification. pp. 340-344 in Proc. 9th International Conference on Advanced Infocomm Technology (ICAIT), Chengdu, China. DOI: https://doi.org/10.1109/ICAIT.2017.8388942
Chen S.W., Shivakumar S.S., Dcunha S., Das J., Okon E., Qu C., Taylor C.J., Kumar V. 2017. Counting apples and oranges with deep learning: a data-driven approach. IEEE Robot. Autom. Lett. 2:781-8. DOI: https://doi.org/10.1109/LRA.2017.2651944
Chollet F. 2018. Deep learning with Python. Manning Publications Co., Shelter Island, New York, NY, USA.
Faurobert M., Mihr C., Bertin N., Pawlowski T., Negroni L., Sommerer N., Causse M. 2007. Major proteome variations associated with cherry tomato pericarp development and ripening. Plant Physiol. 143:1327-46. DOI: https://doi.org/10.1104/pp.106.092817
Fayad J.A., Fontes P.C.R., Cardoso A.A., Finger F.L., Ferreira F.A. 2001. Crescimento e produção do tomateiro cultivado sob condições de campo e de ambiente protegido. Hortic. Brasil. 19:365-70. DOI: https://doi.org/10.1590/S0102-05362001000300016
Fukui R., Schneider J., Nishioka T., Warisawa S., Yamada I. 2017. Growth measurement of tomato fruit based on whole image processing. pp. 153-158 in Proc. IEEE International Conference on Robotics and Automation (ICRA), Singapore. DOI: https://doi.org/10.1109/ICRA.2017.7989020
Ganesh P., Volle K., Burks T.F., Mehta S.S. 2019. Deep orange: mask R-CNN based orange detection and segmentation. IFAC-PapersOnLine 52:70-5. DOI: https://doi.org/10.1016/j.ifacol.2019.12.499
Hall D.O., Scurlock J.M.O., Bolhàr-Nordenkampf H.R., Leegood R.C., Long S.P. (Eds.). 2013. Photosynthesis and production in a changing environment: a field and laboratory manual. Springer, Amsterdam, The Netherlands.
Hemming S., Zwart F., Elings A., Petropoulou A., Righini I. 2020. Cherry tomato production in intelligent greenhouses - sensors and AI for control of climate, irrigation, crop yield, and quality. Sensors 20:6430. DOI: https://doi.org/10.3390/s20226430
Heuvelink E. (Ed.). 2005. Tomatoes. CABI Publishing, Wallingford, UK - Cambridge, MA. DOI: https://doi.org/10.1079/9780851993966.0000
Jha D., Riegler M.A., Johansen D., Halvorsen P., Johansen H.D. 2020. DoubleU-Net: a deep convolutional neural network for medical image segmentation. pp. 558-564 in Proc. IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA. DOI: https://doi.org/10.1109/CBMS49503.2020.00111
Johansen K., Morton M.J.L., Malbéteau Y., Aragon B., Al-Mashharawi S., Ziliani M.G., Ángel Y., Fiene G., Negrão S., Mousa M.A.A., Tester M.A., McCabe M.F. 2020. Predicting biomass and yield in a tomato phenotyping experiment using UAV imagery and random forest. Front. Artif. Intellig. 3:28. DOI: https://doi.org/10.3389/frai.2020.00028
Khoshnam F., Tabatabaeefar A., Ghasemi-Varnamkhasti M., Borghei A. 2007. Mass modeling of pomegranate (Punica granatum L.) fruit with some physical characteristics. Sci. Hortic. 114:21-6. DOI: https://doi.org/10.1016/j.scienta.2007.05.008
Lawal M.O. 2021. Tomato detection based on modified YOLOv3 framework. Sci. Rep. 11:1447. DOI: https://doi.org/10.1038/s41598-021-81216-5
Liu X., Zhao D., Jia W., Ji W., Ruan C., Sun Y. 2019. Cucumber fruits detection in greenhouses based on instance segmentation. IEEE Access 7:139635-42. DOI: https://doi.org/10.1109/ACCESS.2019.2942144
Ngugi L.C., Abdelwahab M., Abo-Zahhad M. 2020. Tomato leaf segmentation algorithms for mobile phone applications using deep learning. Comput. Electron. Agric. 178:105788. DOI: https://doi.org/10.1016/j.compag.2020.105788
Oswell N.J., Amarowicz R., Pegg R.B. 2019. Fruits and fruit products. pp. 428-435 in Reference module in chemistry - Molecular sciences and chemical engineering. Encyclopedia of Analytical Science (Third Edition). Elsevier, Amsterdam, The Netherlands. DOI: https://doi.org/10.1016/B978-0-12-409547-2.14525-1
Öztürk S., Özkaya U., Akdemir B., Seyfi L. 2018. Convolution Kernel size effect on convolutional neural network in histopathological image processing applications. pp. 1-5 in Proc. International Symposium on Fundamentals of Electrical Engineering (ISFEE). Bucharest, Romania. DOI: https://doi.org/10.1109/ISFEE.2018.8742484
Ronneberger O., Fischer P., Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation. In: Navab N., Hornegger J., Wells W., Frangi A. (Eds.), Medical image computing and computer-assisted intervention - MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Berlin, Germany. DOI: https://doi.org/10.1007/978-3-319-24574-4_28
Santos T.T., Souza L.L., Santos A.A., Avila S. 2020. Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association. Comput. Electron. Agric. 170:105247. DOI: https://doi.org/10.1016/j.compag.2020.105247
Solanke A.U., Kumar P.A. 2013. Phenotyping of tomatoes. In: Panguluri S., Kumar A. (Eds.), Phenotyping for plant breeding. Springer, New York, NY, USA, pp. 169-204. DOI: https://doi.org/10.1007/978-1-4614-8320-5_6
Soltani M., Alimardani R., Omid M. 2011. Modeling the main physical properties of banana fruit based on geometrical attributes. Int. J. Multidiscipl. Sci. Engine. 2:1-6.
Song Z., Fu L., Wu J., Liu Z., Li R., Cui Y. 2019. Kiwifruit detection in field images using Faster R-CNN with VGG16. IFAC-PapersOnLine 52:76-81. DOI: https://doi.org/10.1016/j.ifacol.2019.12.500
Su J., Yi D., Su B., Mi Z., Liu C., Hu X., Xu X., Guo L., Chen W.-H. 2021. Aerial visual perception in smart farming: field study of wheat yellow rust monitoring. IEEE Trans. Ind. Inf. 17:2242-9. DOI: https://doi.org/10.1109/TII.2020.2979237
Tabatabaeefar A., Rajabipour A. 2005. Modeling the mass of apples by geometrical attributes. Sci. Hortic. 105:373-82. DOI: https://doi.org/10.1016/j.scienta.2005.01.030
Taheri-Garavand A., Rafiee S., Keyhani A. 2011. Study on some morphological and physical characteristics of tomato used in mass models to characterize best post harvesting options. Austr. J. Crop Sci. 5:433-8.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, I., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., SciPy 1.0 Contributors. 2019. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17:261-72. DOI: https://doi.org/10.1038/s41592-020-0772-5
Wan S., Goudos S. 2020. Faster R-CNN for multi-class fruit detection using a robotic vision system. Comput. Netw. 168:107036. DOI: https://doi.org/10.1016/j.comnet.2019.107036
Xie S., Girshick R., Dollár P., Tu Z., He K. 2021. Aggregated residual transformations for deep neural networks. pp. 5987-5995 in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.
Zafar K., Gilani S.O., Waris A., Ahmed A., Jamil M., Khan M.N., Kashif A.S. 2020. Skin lesion segmentation from dermoscopic images using convolutional neural network. Sensors 20:1601. DOI: https://doi.org/10.3390/s20061601
Zhou Z., Siddiquee M.M.R., Tajbakhsh N., Liang J. 2020. UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39:1856-67. DOI: https://doi.org/10.1109/TMI.2019.2959609

How to Cite

Ferreira Abreu, F. and Antunes Rodrigues, L. H. . (2022) “Monitoring mini-tomatoes growth: A non-destructive machine vision-based alternative”, Journal of Agricultural Engineering, 53(3). doi: 10.4081/jae.2022.1366.
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