WEED DETECTION ON CARROTS USING CONVOLUTIONAL NEURAL NETWORK AND INTERNET OF THING BASED SMARTPHONE

Lintang Patria


Universitas Terbuka (Indonesia)

Aceng Sambas


Universitas Muhammadiyah Tasikmalaya (Indonesia)

Ibrahim Mohammed Sulaiman


Universiti Utara Malaysia (Malaysia)

Mohamed Afendee Mohamed


Universiti Sultan Zainal Abidin (Malaysia)

Volodymyr Rusyn

rusyn_v@ukr.net
Yuriy Fedkovych Chernivtsi National University, Department of Radio Engineering and Information (Ukraine)
https://orcid.org/0000-0001-6219-1031

Andrii Samila


Yuriy Fedkovych Chernivtsi National University (Ukraine)

Abstract

This study proposes a method based on Convolutional Neural Network (CNN) for automated detection of weed in color image format. The image is captured and transmitted to the Internet of Thing (IoT) server following an HTTP request made through the internet which is made available using the GSM based modem connection. The IoT Server save the image inside server drive and the results are displayed on the smartphone (Vision app). The results show that carrot and weed detection can be monitored accurately. The results of the study are expected to provide assistance to farmers in supporting smart farming technology in Indonesia.


Keywords:

weed detection, convolutional neural network, Internet of Thing, smartphone

[1] Asad M. H., Bais A.: Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network. Information Processing in Agriculture 7, 2020, 535–545 [https://doi.org/10.1016/j.inpa.2019.12.002].
DOI: https://doi.org/10.1016/j.inpa.2019.12.002   Google Scholar

[2] Averill K. M. et al.: Effects of Tertill® weeding robot on weed abundance and diversity. Agronomy 12, 2022, 1754 [https://doi.org/10.3390/agronomy12081754].
DOI: https://doi.org/10.3390/agronomy12081754   Google Scholar

[3] Baker H. G.: The evolution of weeds. Annual review of ecology and systematics 5, 1974, 1–24 [https://doi.org/10.1146/annurev.es.05.110174.000245].
DOI: https://doi.org/10.1146/annurev.es.05.110174.000245   Google Scholar

[4] Bakhshipour A., Jafari A.: Evaluation of support vector machine and artificial neural networks in weed detection using shape features. Computers and Electronics in Agriculture 145, 2008, 153–160 [https://doi.org/10.1016/j.compag.2017.12.032].
DOI: https://doi.org/10.1016/j.compag.2017.12.032   Google Scholar

[5] Barrero O. et al.: Weed detection in rice fields using aerial images and neural networks. XXI Symposium on Signal Processing, Images and Artificial Vision – STSIVA, Bucaramanga, Colombia, 1–4, 2016.
DOI: https://doi.org/10.1109/STSIVA.2016.7743317   Google Scholar

[6] Batish D. R. et al.: Crop allelopathy and its role in ecological agriculture. Journal of Crop Production 4, 2001, 121–162 [http://dx.doi.org/10.1300/J144v04n02_03].
DOI: https://doi.org/10.1300/J144v04n02_03   Google Scholar

[7] Bhong V. S. et al.: Design farming robot for weed detection and herbicides applications using image processing. Techno-societal 2018, Springer, Cham, 413–422, 2020.
DOI: https://doi.org/10.1007/978-3-030-16848-3_38   Google Scholar

[8] Bosilj P., Duckett T., Cielniak G.: Analysis of morphology-based features for classification of crop and weeds in precision agriculture. IEEE Robotics and Automation Letters 3, 2018, 2950–2956 [https://doi.org/10.1109/LRA.2018.2848305].
DOI: https://doi.org/10.1109/LRA.2018.2848305   Google Scholar

[9] Brown B. et al.: Improving weed management based on the timing of emergence peaks: a case study of problematic weeds in Northeast USA. Frontiers in Agronomy 4, 2022, 888664 [https://doi.org/10.3389/fagro.2022.888664].
DOI: https://doi.org/10.3389/fagro.2022.888664   Google Scholar

[10] Bullock D. G.: Crop rotation. Critical Reviews in Plant Sciences 11, 1992, 309–326 [https://doi.org/10.1080/07352689209382349].
DOI: https://doi.org/10.1080/07352689209382349   Google Scholar

[11] Cheema Z. A., Asim M., Khaliq A.: Sorghum allelopathy for weed control in cotton (Gossypium arboreum L.). International Journal of Agriculture & Biology 2, 2000, 37–40.
  Google Scholar

[12] Da Silva Dias J. C.: Nutritional and health benefits of carrots and their seed extracts. Food and Nutrition Sciences 5, 2014, 2147–2156 [http://dx.doi.org/10.4236/fns.2014.522227].
DOI: https://doi.org/10.4236/fns.2014.522227   Google Scholar

[13] Damalas C. A., Koutroubas S. D.: Weed competition effects on growth and yield of spring-sown white lupine. Horticulturae 8, 2022, 430 [https://doi.org/10.3390/horticulturae8050430].
DOI: https://doi.org/10.3390/horticulturae8050430   Google Scholar

[14] Dilday R. H., Lin J., Yan W.: Identification of allelopathy in the USDA-ARS rice germplasm collection. Australian Journal of Experimental Agriculture 34, 1994, 907–910 [https://doi.org/10.1071/EA9940907].
DOI: https://doi.org/10.1071/EA9940907   Google Scholar

[15] Dyrmann M., Jorgensen R. N., Midtiby H. S.: RoboWeedSupport-Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network. Advances in Animal Biosciences 8, 2017, 842–847 [https://doi.org/10.1017/S2040470017000206].
DOI: https://doi.org/10.1017/S2040470017000206   Google Scholar

[16] Eddy P. et al.: Comparison of neural network and maximum likelihood high resolution image classification for weed detection in crops: applications in precision agriculture. IEEE International Symposium on Geoscience and Remote Sensing, Denver, CO, USA, 2006, 116–119.
DOI: https://doi.org/10.1109/IGARSS.2006.35   Google Scholar

[17] Farooq N. et al.: Allelopathy for weed management. Co-Evolution of Secondary Metabolites. Reference Series in Phytochemistry. Springer, Cham, 2020, 505–519.
DOI: https://doi.org/10.1007/978-3-319-96397-6_16   Google Scholar

[18] Fawakherji M. et al.: Crop and weeds classification for precision agriculture using context-independent pixel-wise segmentation. Third IEEE International Conference on Robotic Computing – IRC, Naples, Italy, 2019, 146–152.
DOI: https://doi.org/10.1109/IRC.2019.00029   Google Scholar

[19] Finch H. J. S., Samuel A. M., Lane G. P. F.: Weeds. Woodhead Publishing Series in Food Science, Technology and Nutrition. Lockhart and Wiseman’s Crop Husbandry Including Grassland (Eighth Edition), Woodhead Publishing, 87–111, 2002.
DOI: https://doi.org/10.1533/9781855736504.1.87   Google Scholar

[20] Garibaldi-Marquez F. et al.: Weed classification from natural corn field-multi-plant images based on shallow and deep learning. Sensors 22, 2022, 3021 [https://doi.org/10.3390/s22083021].
DOI: https://doi.org/10.3390/s22083021   Google Scholar

[21] Guerrero J. M., Ruz J. J., Pajares G.: Crop rows and weeds detection in maize fields applying a computer vision system based on geometry. Computers and Electronics in Agriculture 142, 2017, 461–472 [https://doi.org/10.1016/j.compag.2017.09.028].
DOI: https://doi.org/10.1016/j.compag.2017.09.028   Google Scholar

[22] Karlen D. L.: Crop rotations for the 21st century. Advances in Agronomy 53, 1994, 1–45 [https://doi.org/10.1016/S0065-2113(08)60611-2].
DOI: https://doi.org/10.1016/S0065-2113(08)60611-2   Google Scholar

[23] Kim K. U., Shin D. H.: Progress and prospect of rice allelopathy research. Allelopathy in sustainable agriculture and forestry. Springer, New York, 189–213, 2008.
DOI: https://doi.org/10.1007/978-0-387-77337-7_10   Google Scholar

[24] Le V. N. T. et al.: A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators. GigaScience 9, 2022, giaa017 [https://doi.org/10.1093/gigascience/giaa017].
DOI: https://doi.org/10.1093/gigascience/giaa017   Google Scholar

[25] Liebman M., Dyck E.: Crop rotation and inter-cropping strategies for weed management. Ecological Applications 3, 1993, 92–122 [https://doi.org/10.2307/1941795].
DOI: https://doi.org/10.2307/1941795   Google Scholar

[26] Mahe I. et al.: Deciphering field-based evidences for crop allelopathy in weed regulation. A review. Agronomy Sustainable Development 42, 2022, 50 [https://doi.org/10.1007/s13593-021-00749-1].
DOI: https://doi.org/10.1007/s13593-021-00749-1   Google Scholar

[27] Mao W., Wang Y., Wang Y.: Real-time detection of between-rowweeds using machine vision. ASAE Annual Meeting. American Society of Agricultural and Biological Engineers 1, 2003.
  Google Scholar

[28] McFadyen R. E. C.: Biological control of weeds. Annual review of entomology 43, 1998, 369–393 [https://doi.org/10.1146/annurev.ento.43.1.369].
DOI: https://doi.org/10.1146/annurev.ento.43.1.369   Google Scholar

[29] Milioto A., Lottes P., Stachniss C.: Real-time blob-wise sugar beets vs weeds classification for monitoring fields using convolutional neural networks. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4, 2017, 41–48 [https://doi.org/10.5194/isprs-annals-IV-2-W3-41-2017].
DOI: https://doi.org/10.5194/isprs-annals-IV-2-W3-41-2017   Google Scholar

[30] Narwal S. S., Haouala R.: Role of allelopathy in weed managementfor sustainable agriculture. Allelopathy. Springer, Heidelberg 1–517, 2013.
DOI: https://doi.org/10.1007/978-3-642-30595-5_10   Google Scholar

[31] Naylor R. E. L.: Weed seed biology. Encyclopedia of Applied Plant Sciences. Academic Press, Oxford, 1500–1508, 2003.
DOI: https://doi.org/10.1016/B0-12-227050-9/00159-9   Google Scholar

[32] Naylor R. E. L.: Weed seed biology. Encyclopedia of Applied Plant Sciences, 2nd ed., Oxford: Academic Press, 485–492, 2017.
DOI: https://doi.org/10.1016/B978-0-12-394807-6.00028-9   Google Scholar

[33] Pena J. M. et al.: Quantifying efficacy and limits of Unmanned Aerial Vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors 153, 2015, 5609–5626 [https://doi.org/10.3390/s150305609].
DOI: https://doi.org/10.3390/s150305609   Google Scholar

[34] Potena C., Nardi D., Pretto A.: Fast and accurate crop and weed identification with summarized train sets for precision agriculture. International Conference on Intelligent Autonomous Systems. Springer, Cham, 2016, 105–121.
DOI: https://doi.org/10.1007/978-3-319-48036-7_9   Google Scholar

[35] Sathishkumar A. et al.: Role of allelopathy in weed management: a review. Agricultural Reviews 41, 2020, 380–386 [https://doi.org/10.18805/ag.R-2031].
DOI: https://doi.org/10.18805/ag.R-2031   Google Scholar

[36] Scarano A. et al.: Phytochemical analysis and antioxidant properties in colored tiggiano carrots. Agriculture 8, 2018, 102 [https://doi.org/10.3390/agriculture8070102].
DOI: https://doi.org/10.3390/agriculture8070102   Google Scholar

[37] Stowe L.: Allelopathy and its influence on the distribution of plants in an Illinois old-field. Journal of Ecology 67, 1979, 1065–1085 [https://doi.org/10.2307/2259228].
DOI: https://doi.org/10.2307/2259228   Google Scholar

[38] Tang J., Wang D., Zhang Z., He L., Xin J., Xu Y.: Weed identification based on K-means feature learning combined with convolutional neural network. Computers and electronics in agriculture 135, 2017, 63–70 [https://doi.org/10.1016/j.compag.2017.01.001].
DOI: https://doi.org/10.1016/j.compag.2017.01.001   Google Scholar

[39] Tellaeche A. et al.: A computer vision approach for weeds identification through support vector machines. Applied Soft Computing 11, 2011, 908–915 [https://doi.org/10.1016/j.asoc.2010.01.011].
DOI: https://doi.org/10.1016/j.asoc.2010.01.011   Google Scholar

[40] Tellaeche A. et al.: A Vision-based hybrid classifier for weeds detection in precision agriculture through the bayesian and fuzzy k-means paradigms. Innovations in Hybrid Intelligent Systems. Springer, Berlin, Heidelberg, 72–79, 2007.
DOI: https://doi.org/10.1007/978-3-540-74972-1_11   Google Scholar

[41] Tesio F., Ferrero A.: Allelopathy, a chance for sustainable weed management. International Journal of Sustainable Development & World Ecology 17, 2010, 377–389 [https://doi.org/10.1080/13504509.2010.507402].
DOI: https://doi.org/10.1080/13504509.2010.507402   Google Scholar

[42] Tong P. S., Lim T. M.: Weed composition and maize yield in a former tinmining area: A case study in Malim Nawar, Malaysia, Open Agriculture 7, 2022, 478–485 [https://doi.org/10.1515/opag-2022-0117].
DOI: https://doi.org/10.1515/opag-2022-0117   Google Scholar

[43] Torres-Sospedra J., Nebot P.: Two-stage procedure based on smoothed ensembles of neural networks applied to weed detection in orange groves. Biosystems Engineering 123, 2014, 40–55 [https://doi.org/10.1016/j.biosystemseng.2014.05.005].
DOI: https://doi.org/10.1016/j.biosystemseng.2014.05.005   Google Scholar

[44] Veeranampalayam Sivakumar A. N. V. et al.: Comparison of object detection and patch-based classification deep learning models on mid-to late-season weed detection in UAV imagery. Remote Sensing 12, 2020, 2136 [https://doi.org/10.3390/rs12132136].
DOI: https://doi.org/10.3390/rs12132136   Google Scholar

[45] Wang A., Zhang W., Wei X.: A Review on weed detection using ground-based machine vision and image processing techniques. Computers and Electronics in Agriculture 158, 2019, 226–240 [https://doi.org/10.1016/j.compag.2019.02.005].
DOI: https://doi.org/10.1016/j.compag.2019.02.005   Google Scholar

[46] Wu H. et al.: Quantitative trait loci and molecular markers associated with wheat allelopathy. Theoretical and Applied Genetics 107, 2003, 1477–1481 [https://doi.org/10.1007/s00122-003-1394-x].
DOI: https://doi.org/10.1007/s00122-003-1394-x   Google Scholar

[47] Yu J. et al.: Weed detection in perennial ryegrass with deep learning convolutional neural network. Frontiers in Plant Science 10, 2019, 1422 [https://doi.org/10.3389/fpls.2019.01422].
DOI: https://doi.org/10.3389/fpls.2019.01422   Google Scholar

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Published
2024-09-30

Cited by

Patria, L., Sambas, A., Sulaiman, I. M., Mohamed, M. A., Rusyn, V., & Samila, A. (2024). WEED DETECTION ON CARROTS USING CONVOLUTIONAL NEURAL NETWORK AND INTERNET OF THING BASED SMARTPHONE. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(3), 96–100. https://doi.org/10.35784/iapgos.5968

Authors

Lintang Patria 

Universitas Terbuka Indonesia

Authors

Aceng Sambas 

Universitas Muhammadiyah Tasikmalaya Indonesia

Authors

Ibrahim Mohammed Sulaiman 

Universiti Utara Malaysia Malaysia

Authors

Mohamed Afendee Mohamed 

Universiti Sultan Zainal Abidin Malaysia

Authors

Volodymyr Rusyn 
rusyn_v@ukr.net
Yuriy Fedkovych Chernivtsi National University, Department of Radio Engineering and Information Ukraine
https://orcid.org/0000-0001-6219-1031

Authors

Andrii Samila 

Yuriy Fedkovych Chernivtsi National University Ukraine

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