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.netYuriy 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, smartphoneReferences
[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
Authors
Lintang PatriaUniversitas Terbuka Indonesia
Authors
Aceng SambasUniversitas Muhammadiyah Tasikmalaya Indonesia
Authors
Ibrahim Mohammed SulaimanUniversiti Utara Malaysia Malaysia
Authors
Mohamed Afendee MohamedUniversiti Sultan Zainal Abidin Malaysia
Authors
Volodymyr Rusynrusyn_v@ukr.net
Yuriy Fedkovych Chernivtsi National University, Department of Radio Engineering and Information Ukraine
https://orcid.org/0000-0001-6219-1031
Authors
Andrii SamilaYuriy Fedkovych Chernivtsi National University Ukraine
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