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http://dx.doi.org/10.3837/tiis.2022.01.008

Pest Prediction in Rice using IoT and Feed Forward Neural Network  

Latif, Muhammad Salman (Department of Computer Science, The Islamia University of Bahawalpur)
Kazmi, Rafaqat (Department of Software Engineering, The Islamia University of Bahawalpur)
Khan, Nadia (Department of Software Engineering, The Islamia University of Bahawalpur)
Majeed, Rizwan (Directorate of Information Technology, The Islamia University of Bahawalpur)
Ikram, Sunnia (Department of Software Engineering, The Islamia University of Bahawalpur)
Ali-Shahid, Malik Muhammad (Department of Computer Science, COMSATS University Islamabad)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.1, 2022 , pp. 133-152 More about this Journal
Abstract
Rice is a fundamental staple food commodity all around the world. Globally, it is grown over 167 million hectares and occupies almost 1/5th of total cultivated land under cereals. With a total production of 782 million metric tons in 2018. In Pakistan, it is the 2nd largest crop being produced and 3rd largest food commodity after sugarcane and rice. The stem borers a type of pest in rice and other crops, Scirpophaga incertulas or the yellow stem borer is very serious pest and a major cause of yield loss, more than 90% damage is recorded in Pakistan on rice crop. Yellow stem borer population of rice could be stimulated with various environmental factors which includes relative humidity, light, and environmental temperature. Focus of this study is to find the environmental factors changes i.e., temperature, relative humidity and rainfall that can lead to cause outbreaks of yellow stem borers. this study helps to find out the hot spots of insect pest in rice field with a control of farmer's palm. Proposed system uses temperature, relative humidity, and rain sensor along with artificial neural network to predict yellow stem borer attack and generate warning to take necessary precautions. result shows 85.6% accuracy and accuracy gradually increased after repeating several training rounds. This system can be good IoT based solution for pest attack prediction which is cost effective and accurate.
Keywords
Internet of things (IoT); Stem Borer Pest Prediction; Artificial Neural Network;
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1 A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, pp. 1097-1105, 2012.
2 S. Azfar et al., "Monitoring, Detection and Control Techniques of Agriculture Pests and Diseases using Wireless Sensor Network: A Review," Int. J. Adv. Comput. Sci. Appl, vol. 9, pp. 424-433, 2018.
3 M. Ntihemuka and M. Inoue, "IoT Monitoring System for Early Detection of Agricultural Pests and Diseases," pp. 1-5, 2018.
4 A. Araby et al., "Smart IoT Monitoring System for Agriculture with Predictive Analysis," in Proc. of 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST), pp. 1-4, 2019.
5 A. Sakhare, T. Patil, P. Giri, and R. Gulame, "Crop Yield Prediction and Disease Detection Using IOT Approach," 2019.
6 S. S. Shinde and M. Kulkarni, "Review paper on prediction of crop disease using IoT and machine learning," in Proc. of 2017 International Conference on Transforming Engineering Education (ICTEE), IEEE, pp. 1-4, 2017.
7 S. Karim and S. Riazuddin, "Rice insect pests of Pakistan and their control: a lesson from past for sustainable future integrated pest management," Pakistan Journal of Biological Sciences (Pakistan), 2, 261-276, 1999.   DOI
8 M. Rahaman, K. Islam, M. Jahan, and M. Mamun, "Relative abundance of stem borer species and natural enemies in rice ecosystem at Madhupur, Tangail, Bangladesh," Journal of the Bangladesh Agricultural University, vol. 12, no. 2, pp. 267-272, 2014.   DOI
9 K. Ashton, "That 'internet of things' thing," RFID journal, vol. 22, no. 7, pp. 97-114, 2009.
10 D. G. Lowe, "Object recognition from local scale-invariant features," in Proc. of he seventh IEEE international conference on computer vision, vol. 2, pp. 1150-1157, 1999.
11 Y. Yan, C.-C. Feng, M. P.-H. Wan, and K. T.-T. Chang, "Multiple regression and artificial neural network for the prediction of crop pest risks," in Proc. of International conference on information systems for crisis response and management in Mediterranean countries, Springer, pp. 73-84, 2015.
12 G. Reji, S. Chander, and K. Kamble, "Predictive zoning of rice stem borer damage in southern India through spatial interpolation of weather-based models," Journal of environmental biology, vol. 35, no. 5, p. 923, 2014.
13 A. Laudani, G. M. Lozito, F. Riganti Fulginei, and A. Salvini, "On training efficiency and computational costs of a feed forward neural network: a review," Computational intelligence and neuroscience, vol. 2015, p. 818243, 2015.   DOI
14 N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. of 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), vol. 1, pp. 886-893, 2005.
15 T. Wahyono, Y. Heryadi, H. Soeparno, and B. S. Abbas, "Crop Pest Prediction Using Climate Anomaly Model Based On Deep-Lstm Method," in Proc. of ICIC express letters. Part B, Applications: an international journal of research and surveys, vol.12, pp. 395-401, 2021.
16 C. Szegedy et al., "Going deeper with convolutions," in Proc. of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015.
17 W. S. McCulloch and W. Pitts, "A logical calculus of the ideas immanent in nervous activity," The bulletin of mathematical biophysics, vol. 5, no. 4, pp. 115-133, 1943.   DOI
18 F. Xia, L. T. Yang, L. Wang, and A. Vinel, "Internet of things," International journal of communication systems, vol. 25, no. 9, pp. 1101-1102, 2012.   DOI
19 R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proc. of the IEEE conference on computer vision and pattern recognition, pp. 580-587, 2014.
20 D. Ciresan, U. Meier, J. Masci, and J. Schmidhuber, "A committee of neural networks for traffic sign classification," in Proc. of The 2011 international joint conference on neural networks, pp. 1918-1921, 2011.
21 V. N. D. Prasanna and D. B. K. R., "," Peer Reviewed Journal, vol. 8, no. 1, p. 3, 2019.
22 L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, pp. 5-32, 2001.   DOI
23 D. Sehrawat and N. S. Gill, "Smart sensors: Analysis of different types of IoT sensors," in Proc. of 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), IEEE, pp. 523-528, 2019.
24 M. L. Margosian, K. A. Garrett, J. M. S. Hutchinson, and K. A. With, "Connectivity of the American Agricultural Landscape: Assessing the National Risk of Crop Pest and Disease Spread," BioScience, vol. 59, no. 2, pp. 141-151, 2009.   DOI
25 S. Wagh and S. Datir, "Monitoring and Detection of Agricultural Disease using Wireless Sensor Network," International Journal of Computer Applications, vol. 87, no. 4, pp. 1-5, 01/31 2014.   DOI
26 S. Azfar et al., "Monitoring, Detection and Control Techniques of Agriculture Pests and Diseases using Wireless Sensor Network: A Review," International Journal of Advanced Computer Science and Applications, vol. 9, pp. 424-434, 12/31 2018.
27 K. R. Sharma, S. Raju, D. R. Roshan, and D. K. Jaiswal, "Effect of abiotic factors on yellow stem borer, Scirpophaga incertulas (Walker) and rice leaf folder, Cnaphalocrocis medinalis (Guenee) population," Journal of Experimental Zoology. India, vol. 21, no. 1, pp. 233-236, 2018.
28 I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless sensor networks: a survey," Computer networks, vol. 38, no. 4, pp. 393-422, 2002.   DOI
29 N. Manikandan, J. Kennedy, and V. Geethalakshmi, "Effect of elevated temperature on development time of rice yellow stem borer," Indian Journal of Science and Technology, vol. 6, no. 12, pp. 5563-5566, 2013.
30 M. S. Islam, S. Das, K. S. Islam, A. Rahman, M. N. Huda, and P. K. Dash, "Evaluation of different insecticides and botanical extracts against yellow stem borer, Scirpophaga incertulas in rice field," International journal of biosciences, vol. 3, no. 10, pp. 117-125, 2013.   DOI
31 A. K. Jain, J. Mao, and K. M. Mohiuddin, "Artificial neural networks: A tutorial," Computer, vol. 29, no. 3, pp. 31-44, 1996.   DOI
32 H. Kurdi, A. Al-Aldawsari, I. Al-Turaiki, and A. S. Aldawood, "Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining," Plants, vol. 10, no. 1, p. 95, 2021.   DOI
33 R. M. Saleem, R. Kazmi, I. S. Bajwa, A. Ashraf, S. Ramzan, and W. Anwar, "IOT-Based Cotton Whitefly Prediction Using Deep Learning," Scientific Programming, vol. 2021, 2021.
34 H. Sug, "The effect of training set size for the performance of neural networks of classification," WSEAS Trans Comput, vol. 9, pp. 1297-306, 2010.
35 M. Rafiq, G. Bugmann, and D. Easterbrook, "Neural network design for engineering applications," Computers & Structures, vol. 79, no. 17, pp. 1541-1552, 2001.   DOI
36 F. Pedregosa et al., "Scikit-learn: Machine learning in Python," the Journal of machine Learning research, vol. 12, pp. 2825-2830, 2011.
37 R. Hecht-Nielsen, "Theory of the backpropagation neural network," Neural networks for perception, pp. 65-93, 1992.
38 I. Albatish, M. J. Mosa, and S. S. Abu-Naser, "ARDUINO Tutor: An Intelligent Tutoring System for Training on ARDUINO," 2018.
39 Adafruit. DHT22 temperature-humidity sensor. [Online] Available: https://learn.adafruit.com/dht
40 S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," arXiv 2015, arXiv preprint arXiv:1506.01497.
41 W. Ding and G. Taylor, "Automatic moth detection from trap images for pest management," Computers and Electronics in Agriculture, vol. 123, pp. 17-28, 2016.   DOI
42 M. Rizwan et al., "Measuring rice farmers' risk perceptions and attitude: Evidence from Pakistan," Human and Ecological Risk Assessment: An International Journal, pp. 1-16, 2019.
43 M. Kubo and M. Purevdorj, "The future of rice production and consumption," Journal of Food Distribution Research, vol. 35, no. 856-2016-57064, pp. 128-142, 2004.
44 FAOSTAT, "Crops," Food and Agriculture Organization of the United Nations. [Online] http://www.fao.org/faostat/en/#data/QC/visualize (accessed 11 July, 2020).
45 N. A. Memon, "Rice: Important cash crop of Pakistan," Pak. Food J, vol. 26, no. 7, pp. 21-23, 2013.
46 S. Ahmad, M. Zia-Ul-Haq, M. Imran, S. Iqbal, J. Iqbal, and M. Ahmad, "Determination of residual contents of pesticides in rice (Oryza sativa L.) crop from different regions of Pakistan," Pak. J. Bot, vol. 40, no. 3, pp. 1253-1257, 2008.
47 G. Giraud, "Range and Limit of Geographical Indication Scheme: The case of Basmati rice from Punjab, Pakistan," International Food and Agribusiness Management Review, vol. 11, no. 1030-2016-82704, pp. 51-76, 2008.
48 V. Amsagowri, N. Muthukrishnan, C. Muthiah, M. Mini, and S. Mohankumar, "Biochemical changes in rice yellow stem borer infested rice accessions," Indian Journal of Entomology, vol. 80, no. 3, pp. 926-934, 2018.   DOI
49 I. Goodfellow, Y. Bengio, and A. Courville, Deep learning, MIT press, 2016.
50 M. M. Ghazi, B. Yanikoglu, and E. Aptoula, "Plant identification using deep neural networks via optimization of transfer learning parameters," Neurocomputing, vol. 235, pp. 228-235, 2017.   DOI
51 S.-C. Wang, "Artificial neural network," in Proc. of Interdisciplinary computing in java programming, Springer, pp. 81-100, 2003.
52 A. R. Dhuyo, "Integrated control of yellow rice stem borer Scirpophaga incertulas (Walker)(Lepidoptera: Pyralidae)," University of Sindh, Jamshoro, Pakistan, 2009.
53 M. T. Rahman, M. Khalequzzaman, and M. A. R. Khan, "Assessment of infestation and yield loss by stem borers on variety of rice," Journal of Asia-Pacific Entomology, vol. 7, no. 1, pp. 89-95, 2004.   DOI
54 A. A. Khakwani et al., "Agronomic and morphological parameters of rice crop as affected by date of transplanting," J. Agron, vol. 5, no. 2, pp. 248-250, 2006.   DOI
55 R. A. Khan, J. A. Khan, F. Jamil, and M. Hamed, "Resistance of different basmati rice varieties to stem borers under different control tactics of IPM and evaluation of yield," Pakistan Journal of Botany, vol. 37, no. 2, p. 319, 2005.
56 J. Aitchison and I. R. Dunsmore, Statistical prediction analysis, CUP Archive, 1980.
57 Pakissan, "Introduction to Rice,". [Online] https://www.pakissan.com/2017/01/15/introduction-to-rice/ (accessed August 5, 2020).
58 S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, "Supervised machine learning: A review of classification techniques," Emerging artificial intelligence applications in computer engineering, vol. 160, no. 1, pp. 3-24, 2007.
59 J. R. Quinlan, "Induction of decision trees," Machine learning, vol. 1, no. 1, pp. 81-106, 1986.   DOI
60 Y. Freund and R. Schapire, "A tutorial on boosting," 2013.
61 S. Tsunoda and N. Takahashi, Biology of rice, Elsevier, 2012.
62 Y. Le Cun et al., "Handwritten zip code recognition with multilayer networks," in Proc. of 10th International Conference on Pattern Recognition, vol. 2, pp. 35-40, 1990.
63 R. Girshick, "Fast R-CNN object detection with Caffe," Microsoft Research, 2015.
64 M. Irfan, M. Irfan, and M. Tahir, "Modeling the province wise yield of rice crop in Pakistan Using GARCH model," International Journal of Science and Technology, vol. 1, no. 6, pp. 224-228, 2011.
65 W. Bergerud, "Introduction to logistic regression models with worked forestry examples: biometrics information handbook no. 7," Res. Br., BC Min. For., Victoria, BC Work. Pap, vol. 26, p. 1996, 1996.
66 V. Jakkula, "Tutorial on support vector machine (svm)," School of EECS, Washington State University, vol. 37, 2006.
67 Y. V. Prabhu, J. S. Parab, and G. Naik, "Back-Propagation Neural Network (BP-NN) model for the detection of borer pest attack," in Proc. of Journal of Physics: Conference Series, IOP Publishing, vol. 1921, no. 1, p. 012079, 2021.   DOI
68 D. Markovic, D. Vujicic, S. Tanaskovic, B. Dordevic, S. Randic, and Z. Stamenkovic, "Prediction of Pest Insect Appearance Using Sensors and Machine Learning," Sensors, vol. 21, no. 14, p. 4846, 2021.   DOI
69 U. Food and A. Organization, "Crops/Regions/World list/Production Quantity (pick lists), Rice (paddy), 2014," Corporate Statistical Database (FAOSTAT), 2017.
70 S. Nadeem, M. Hamed, and M. Shafique, "Feeding preference and developmental period of some storage insect species in rice products," Pakistan Journal of Zoology, vol. 43, no. 1, 2011.
71 H. Sundmaeker, P. Guillemin, P. Friess, and S. Woelffle, "Vision and challenges for realising the Internet of Things," Cluster of European research projects on the internet of things, European Commision, vol. 3, no. 3, pp. 34-36, 2010.
72 M. Sarwar, "Management of rice stem borers (Lepidoptera: Pyralidae) through host plant resistance in early, medium and late plantings of rice (Oryza sativa L.)," Journal of Cereals and Oilseeds, vol. 3, no. 1, pp. 10-14, 2012.   DOI
73 M. Savela, "Lepidoptera and some other life forms," Retrieved February, vol. 8, p. 2018, 2015.
74 D. Svozil, V. Kvasnicka, and J. Pospichal, "Introduction to multi-layer feed-forward neural networks," Chemometrics and intelligent laboratory systems, vol. 39, no. 1, pp. 43-62, 1997.   DOI
75 G. Guru-Pirasanna-Pandi et al., "Effect of weather parameters on rice yellow stem borer Scirpophagain certulas (walker) population dynamics under shallow low land ecology," Journal of Agrometeorology, vol. 22, no. 1, pp. 89-91, 2020.   DOI
76 A. Kakde and K. Patel, "Seasonal Incidence of rice yellow stem borer (Scirpophaga incertulas Wlk.) in relation to conventional and sri methods of planting and its correlation with weather parameters," Journal of Agriculture and Veterinary Science, vol. 7, no. 6, pp. 05-10, 2014.
77 H. Lee, A. Moon, K. Moon, and Y. Lee, "Disease and pest prediction IoT system in orchard: A preliminary study," in Proc. of 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 525-527, 2017.
78 S. Datir and S. Wagh, "Monitoring and detection of agricultural disease using wireless sensor network," International Journal of Computer Applications, vol. 87, no. 4, 2014.
79 T. Wahyono, Y. Heryadi, H. Soeparno, and B. S. Abbas, "Crop Pest Prediction Using Climate Anomaly Model Based On Deep-Lstm Method," ICIC express letters. Part B, Applications: an international journal of research and surveys, vol. 12, no. 4, pp. 395-401, 2021.
80 S. S. Kalgapure, P. V. Birajdar, R. C. Biradar, and M. G. Kadam, "Iot Based Monitoring System And Smart Agriculture Using Raspberry Pi," International Journal, vol. 5, no. 12, 2021.
81 D. Li et al., "A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network," Sensors, vol. 20, no. 3, p. 578, Jan 21 2020.   DOI