research-article

An Application of Machine Learning Technique in Forecasting Crop Disease

Authors:

Francesca Maridina MallociAuthors Info & Claims

ICBDR '19: Proceedings of the 3rd International Conference on Big Data Research

Pages 76 - 82

https://doi.org/10.1145/3372454.3372474

Published: 21 January 2020 Publication History

Abstract

In the recent years, Big Data Analytics and Machine Learning techniques are playing an increasingly key role in the agriculture sector in order to tackle the increasing challenges due to the climate changes which are causing serious damage production. The analysis of environmental, climatic and cultural factors allows to establish the irrigation and nutritional needs of crops, forecast crop disease, improve crop yield, as well as improve the quantity and the quality of agricultural output while using less input. Potato late blight is considered one of the most devasting disease world over, including Sardinia. Unexpected epidemics can result in significant economic and yield losses. In this paper, we describe the test conducted using the DSS LANDS in order to predict potato late blight disease in Sardinia. The object of the study was to investigate if regional weather variables could be used to predict potato late blight risk in southern Sardinia using a Machine Learning approach. The disease severity is predicted using Feed-forward Neural Network and Support Vector Machine Classification based on meteorological parameters provided by ARPAS weather stations. The prediction accuracy for ANN was 96% and for SVM Classification was 98%.

References

[1]

Sivakumar, M. V. K., Ray M., and Das, H.P. 2005.Natural disasters and extreme events in agriculture: Impacts and mitigation. Springer, ISBN -103-540-22490-4 (January 2005), 367. DOI= 10.1007/3-540-28307-2

[2]

The State of the World's Land and Water Resources for Food and Agriculture, Summary Report. FAO Managing systems at risk, ISBN -978-92-5-106614-0 (2011).

[3]

Steduto, P., Hsiao, T., Fereres, E., and Raes, D. 2012. Crop Yield Response to Water. FAO Irrigation and Drainage (2012), 500.

[4]

Pierce, F. and Nowak, P.1999. Aspects of Precision Agriculture. Adv Agron (1999), 67, 1--86.

[5]

Wolfert, S., Ge, L., Verdouw, C., and Bogaardt, M. J. Big data in smart farming--a review. 2017. Agricultural Systems (2017), 153, 69--80.

[6]

Kamilaris, A., Kartakoullis, A., and Prenafeta-Boldù, F. X. 2017. A Review on the Practice of Big Data Analysis in Agriculture. Comput. Electron. Agric. (2017), 143, 23--37.

[7]

Bendre, M.R., Thool, R. C. and Thool, V.R. 2015. Big Data in Precision Agriculture - Weather Forecasting for Future Farming. In Proceedings of Next Generation Computing Technologies (2015), Deharadun, DOI=10.1109/NGCT.2015.7375220

[8]

Oerke, E. C. 2006. Crop losses to pests. Journal of Agricultural Science (2006), 144, 31--43.

[9]

Savary, S., Ficke, A., Aubertot, J.-N and Hollier, C. 2012. Crop losses due to diseases and their implications for global food production losses and food security. Food Security (2012), 4, 519--537.

[10]

Fenu, G., and Malloci, F. M. 2019. An Agricultural Prototype DSS LANDS for monitoring the main crop productions in Sardinia. Proceedings of 5th EmC-ICDSST (2019), Madeira, Portugal.

[11]

Baker, K.M. and Kirk, W.W. 2007.Comparative analysis of models integrating synoptic forecast data into potato late blight risk estimate systems. Comput. Electron. Agric (2007), 57, 23--32.

[12]

Fall, M. L., Tremblay, D. M., Gobeil-Richard, M., Couillard, J., Rocheleau, H., Van der Heyden, H., Carisse, O. 2015. Infection Efficiency of Four Phytophthora infestans Clonal Lineages and DNA-Based Quantification of Sporangia. PloS one (2015). DOI=10.1371/journal.pone.0136312

[13]

Singh, V. K., and Pundhir, V. S. 2013. Forecasting Models For Potato Late Blight Management- A Review. Agricultural Research Communication Centre (2013), 34(2), 87--96.

[14]

Biswas, S., Jagyasi, B., Singh, B. P., and Lal, M. 2014. Severity identification of Potato Late Blight disease from crop images captured under uncontrolled environment. IEEE Canada International Humanitarian Technology Conference-(IHTC) (June 2014).

[15]

Duarte-Carvajalino, J., Alzate, D., Ramirez, A., Santa-Sepulveda, J., Fajardo-Rojas, A., and Soto-Suárez, M. 2018. Evaluating Late Blight Severity in Potato Crops Using Unmanned Aerial Vehicles and Machine Learning Algorithms. Remote Sens. (2018), 10, 1513.

[16]

Islam, M., Dinh, A., Wahid, K., and Bhowmik, P. 2017. Detection of potato diseases using image segmentation and multiclass support vector machine.IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) (2017), 1--4.

[17]

Sarika, D., and Sanjeev, W. 2014. Monitoring and Detection of Agricultural Disease using Wireless Sensor Network. International Journal of Computer Applications (February 2014), 87(4).

[18]

Liakos, K., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. 2018. Machine Learning in Agriculture: A Review. Sensors (2018), 18.

[19]

Dessì, D., Fenu, G., Marras, M., Reforgiato Recupero, D. 2019. Bridging learning analytics and Cognitive Computing for Big Data classification in micro-learning video collections. Computers in Human Behavior (2019), 92, 468--477

[20]

Boratto, L., Carta, S., Fenu, G., Saia, R. 2016. Binary sieves: Toward a semantic approach to user segmentation for behavioral targeting Future Generation Computer Systems (2016) 64, 186--197.

[21]

Toroitich P. K., and Orero, J. 2017. Real-time monitoring model for early detection of crop diseases. In Pan African Conference on Science, Computing and Telecommunications (2017). Nairobi: Strathmore University. Retrieved from https://su-plus.strathmore.edu

[22]

Baker, K.M., and W.W Kirk. 2007. Comparative analysis of models integrating synoptic forecast data into potato late blight risk estimate systems. Computers and electronics in agriculture (2007), 57, 23--32.

[23]

Sharma, P., Singh, B.K., and. Singh, R.P. 2018. Prediction of Potato Late Blight Disease Based Upon Weather Parameters Using Artificial Neural Network Approach. 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (2018), 1--13.

[24]

Gu, Y. H., Yoo, S. J., Park, C. J., Kim, Y. H., Park, S. K., Kim, J. S., and Lim, J. H. 2016. BLITE-SVR: New forecasting model for late blight on potato using support-vector regression. Computers and Electronics in Agriculture (2016), 130, 169--176.

[25]

Hijmans, R. J., Forbes, G. A., & Walker, T. S. 2000. Estimating the global severity of potato late blight with GIS-linked disease forecast models. Plant Pathology (2000), 49, 697--705.

[26]

Henderson, D.K., Williams, C.M., and Miller, J.S. 2007. Forecasting Late Blight in Potato Crops of Southern Idaho Using Logistic Regression Analysis (2007). Plant disease, 91-8, 951--956.

[27]

Singh, B.K., Singh, R.P., Bisen, T., and Kharayat, S. 2018. Disease Manifestation Prediction from Weather Data Using Extreme Learning Machine. 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU) (2018), 1--6.

[28]

McCown, R.L. 2012. A cognitive systems framework to inform delivery of analytic support for farmers' intuitive management under seasonal climatic variability. Agricultural Systems(2012), 105, 7--20.

[29]

Small, I.M., Joseph, L., and Fry, W.E. 2013. Evaluation of the blight decision support system for the integrated management of potato and tomato late blight. Phytopathology (2013), 103, 134--135.

[30]

Fry, W. E., Apple, A.E. and Bruhn, J.A. 1983. Evaluation of potato late blight forecasts modified to incorporate host resistance and fungicide weathering. Phytopathology (1983), 73, 1054--1059.

[31]

Vapnik, V.N. 1999. An overview of statistical learning theory. IEEE transactions on neural networks(1999).10(5), 988--999.

Digital Library

[32]

Su, Y. X., Xu, H., & Yan, L. J. 2017. Support vector machine-based open crop model (SBOCM): Case of rice production in China. Saudi journal of biological sciences(2017), 24(3), 537--547.

[33]

Khairunniza-Bejo, S., Mustaffha, S., and Ismail, W. I. W. 2014. Application of artificial neural network in predicting crop yield: A review. Journal of Food Science and Engineering (2014). 4(1), 1.

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G JCastelino EMahadevu PKitturmath MLohithaswa HKarjagi CD D(2025)Smart solutions for maize farmers: Machine learning-enabled web applications for downy mildew management and enhanced crop yield in IndiaEuropean Journal of Agronomy10.1016/j.eja.2024.127441164(127441)Online publication date: Mar-2025
https://doi.org/10.1016/j.eja.2024.127441
Fenu GMalloci FOnorato MGerardi MScano A(2025)Plant Disease Detection Using Deep Learning: A Proof of Concept on Pear Leaf Disease DetectionMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74633-8_17(271-279)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-74633-8_17
Yao SLin JBai H(2024)DPP: A Novel Disease Progression Prediction Method for Ginkgo Leaf Disease Based on Image SequencesInformation10.3390/info1507041115:7(411)Online publication date: 16-Jul-2024
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Index Terms

An Application of Machine Learning Technique in Forecasting Crop Disease
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics

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ICBDR '19: Proceedings of the 3rd International Conference on Big Data Research

November 2019

192 pages

ISBN:9781450372015

DOI:10.1145/3372454

Copyright © 2019 ACM.

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Shandong Univ.: Shandong University
The University of Versailles Saint-Quentin: The University of Versailles Saint-Quentin, Versailles, France

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Published: 21 January 2020

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ICBDR 2019: 2019 The 3rd International Conference on Big Data Research

November 20 - 22, 2019

Cergy-Pontoise, France

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Cited By

G JCastelino EMahadevu PKitturmath MLohithaswa HKarjagi CD D(2025)Smart solutions for maize farmers: Machine learning-enabled web applications for downy mildew management and enhanced crop yield in IndiaEuropean Journal of Agronomy10.1016/j.eja.2024.127441164(127441)Online publication date: Mar-2025
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Fenu GMalloci FOnorato MGerardi MScano A(2025)Plant Disease Detection Using Deep Learning: A Proof of Concept on Pear Leaf Disease DetectionMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74633-8_17(271-279)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-74633-8_17
Yao SLin JBai H(2024)DPP: A Novel Disease Progression Prediction Method for Ginkgo Leaf Disease Based on Image SequencesInformation10.3390/info1507041115:7(411)Online publication date: 16-Jul-2024
https://doi.org/10.3390/info15070411
Guo ZChen XLi MChi YShi D(2024)Construction and Validation of Peanut Leaf Spot Disease Prediction Model Based on Long Time Series Data and Deep LearningAgronomy10.3390/agronomy1402029414:2(294)Online publication date: 29-Jan-2024
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Ahmed IAhmad MGhazouani HBarhoumi WJeon G(2024) Intelligent Computing for Crop Monitoring in CIoT : Leveraging AI and Big Data Technologies Expert Systems10.1111/exsy.13786Online publication date: 18-Nov-2024
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Kaur ARandhawa GAbbas FAli MEsau TFarooque ASingh R(2024)Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic ReviewIEEE Access10.1109/ACCESS.2024.351045612(193902-193922)Online publication date: 2024
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Gasratova NPashkova D(2024)Application of a modified mathematical model “consumer – resource” to justify the periods of treatment of potato late blight with fungicidesE3S Web of Conferences10.1051/e3sconf/202448603012486(03012)Online publication date: 7-Feb-2024
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