Classification of powdery mildew disease symptoms on sandalwood using machine learning techniques

Year 2024, Volume: 10 Issue: 2, 84 - 91, 13.12.2024

A. Muthu Kumar , J. Cruz Antony , V. Soundararajan

https://doi.org/10.33904/ejfe.1415402

Abstract

Powdery mildew (Oidium sp.) is a fungal disease that infects plants by creating white powdery spots on plants and trees, reducing in yield. Powdery mildew is often influenced by changes in climatic conditions with cloud factors, humidity, and temperature playing major roles. This study focuses on building a Machine learning model to classify powdery mildew disease symptoms on sandalwood trees based on abiotic features like soil moisture, temperature, humidity, and cloud factors. Various machine learning algorithms such as Decision Tree, Logistic Regression, Random Forest, Support Vector Machine, and K-Nearest Neighbors were used on the dataset, and the model with the highest accuracy was chosen for building a powdery mildew prediction web application on the cloud platform. This web application helps in the prediction of the disease incidence/intensity and thereby enlightens the farming community to adopt appropriate management strategies.

Keywords

IWST, Machine learning, Powdery mildew, Sandalwood

References

• Abdu, A.M., Mokji, M.M., Sheikh, U.U. 2020. Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning. IAES International Journal of Artificial Intelligence, 9:670-683.
• Anderson, P.K., Cunningham, A.A., Patel, N.G., Morales, F.J., Epstein, P.R., Daszak, P. 2004. Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends in ecology & evolution, 19(10):535-544.
• Annabel, L.S.P., Annapoorani, T. and Deepalakshmi, P. 2019. Machine Learning for Plant Leaf Disease Detection and Classification–A Review. In 2019 International Conference on Communication and Signal Processing (ICCSP) (pp. 0538-0542). IEEE.
• Antony, J.C. and Pratheepa, M. 2017. Study of population dynamics of soybean semi-looper Gesonia gemma Swinhoe by using rule induction model in Maharashtra, India. Legume Research-An International Journal, 40(2):369-373.
• Ashwin, N., Adusumilli, U.K., Kemparaju, N., Kurra, L. 2021. A machine learning approach to prediction of soybean disease. International Journal of Scientific Research in Science, Engineering and Technology, 9:78-88.
• Babita, M., Sandeep, C., Sushant, A., Sruthi, S., Syam, V. 2018. Assessment of heartwood and oil content of Santalum album Linn. in natural and naturalized populations across contrasting edapho-climatic conditions in India. Indian Forester, 144(7):675-685.
• Banjare, P., Matore, B., Singh, J., Roy, P.P. 2021. In silico local QSAR modeling of bioconcentration factor of organophosphate pesticides. In Silico Pharmacology, 9(1):1-13.
• Bankar, P., Kadam, V., Bhosale, A., Shitole, S., Wagh, S., Chandankar, S., Chitale, R. and Kanade, M.B. 2019. Powdery mildew fungi from Phaltan Area of Satara District, Maharashtra. Int J Curr Microbiol App Sci, 8(7):2181-6.
• Basavaiah, J., and Arlene Anthony, A. 2020. Tomato leaf disease classification using multiple feature extraction techniques. Wireless Personal Communications, 115(1):633-651.
• Bhatia, A., Chug, A. and Singh, A.P. 2020. February. Hybrid SVM-LR classifier for powdery mildew disease prediction in tomato plant. In 2020 7th International conference on signal processing and integrated networks (SPIN) (pp. 218-223). IEEE.
• Bhatia, A., Chug, A., Singh, A.P. 2021. Statistical analysis of machine learning techniques for predicting powdery mildew disease in tomato plants. International Journal of Intelligent Engineering Informatics, 9(1):24-58.
• Biau, G. and Scornet, E. 2016. A random forest guided tour. Test, 25(2):197-227.
• Bisong, E. 2019. Matplotlib and Seaborn. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, Berkeley, CA.
• Borah, R.K., Gogoi, J., Gogoi, B., Sharma, G.S. 2012. New record of Powdery Mildew on Acacia mangium Willd. In India. Journal of Plant Protection Research, 52(1):64-66.
• Braun, U. and Cook, R.T.A. 2012. Taxonomic manual of Erysiphales (powdery mildews). CBS Biodiversity Series, 11.
• Chirame, B.B. 2018. First report of powdery mildew of Ageratum houstonianum, Mentha and Sandalwood in Maharashtra (India). Electronic International Interdisciplinary Research Journal, 7(12):12-14.
• Christopher, J.S., Jamie, M.B., Rolf, C. 2017. Cloud cover effect of clear-sky index distributions and differences between human and automatic cloud observations, Solar Energy, 144:10-21, ISSN 0038-092X, https://doi.org/10.1016/j.solener.2016.12.055.
• Das, S.C. and Jagatpati, T. 2013. Effect of GA3 on seed germination of sandal (Santalum album L.). International Journal of Current Science, 8:79-84.
• Das, S.C. 2021, Diseases and Insect Pests of Sandalwood. In: Pullaiah, T., Das, S.C., Bapat, V.A., Swamy, M.K., Reddy, V.D., Murthy, K.S.R. (eds) Sandalwood: Silviculture, Conservation and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-16-0780-6_9
• Fox, J.E. 2000. Sandalwood: the royal tree. Biologist (London, England), 47(1):31-34.
• Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., Aryal, J. 2019. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing, 11(2):196.
• Huang, S., Cai, N., Pacheco, P.P., Narrandes, S., Wang, Y., Xu, W. 2018. Applications of support vector machine (SVM) learning in cancer genomics. Cancer genomics & proteomics, 15(1):41-51.
• Jiang, L., Cai, Z., Wang, D., Jiang, S. 2007. August. Survey of improving k-nearest-neighbor for classification. In Fourth international conference on fuzzy systems and knowledge discovery (FSKD 2007) (Vol. 1, pp. 679-683). IEEE.
• Jiang, G., Wang, N., Zhang, Y. et al., 2021. The relative importance of soil moisture in predicting bacterial wilt disease occurrence. Soil Ecology Letters, 3:356–366. https://doi.org/10.1007/s42832-021-0086-2 Liakos, K.G., Busato, P., Moshou, D., Pearson, S., Bochtis, D. 2018. Machine learning in agriculture: A review. Sensors, 18(8):2674.
• Liao, Y. and Vemuri, V.R. 2002. Use of k-nearest neighbor classifier for intrusion detection. Computers & Security, 21(5):439-448.
• Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., Villa, P., Stroppiana, D., Boschetti, M., Goulart, L.R., Davis, C.E. 2015. Advanced methods of plant disease detection. A review. Agronomy for Sustainable Development, 35(1):1-25.
• Mckinney, W. 2015. Pandas—Powerful Python Data Analysis Toolkit, 1625.
• Mousavizadegan, M. and Mohabatkar, H. 2016. An evaluation on different machine learning algorithms for classification and prediction of antifungal peptides. Medicinal Chemistry, 12(8):795-800.
• Mucherino, A., Papajorgji, P.J., Pardalos, P.M. 2009. K-nearest neighbor classification. In Data mining in agriculture (pp. 83-106). Springer, New York, NY.
• Muthu Kumar, A., Soundararajan, V., Rekha J., Divya Bharathi, M., Mamatha N. 2021a. Foliar blight disease of Indian Sandalwood (Santalum album l.) trees caused by the pathogen Pestalotiopsis guepinii (desm.) Steyaert. Asian Journal of Microbiology, Biotechnology & Environmental Sciences, 23(3):96-100.
• Muthu Kumar, A., Soundararajan, V., Manoj Kumar P.E., Abhilash A., Rekha J., Vijayalakshmi, G., Mamatha N. 2021b. Impact of change in microclimatic factors for incidence and prevalence of powdery mildew disease in Indian sandalwood (Santalum album L.). Ecology, Environment and Conservation, 28(1):271-273.
• Nagaveni, H.C., Sundararaj, R., Vijayalakshmi, G. 2014. First report of canker disease on Indian sandalwood (Santalum album Linn.) in India. Journal on New Biological Reports, 3(2):120-124.
• Pallavi, A. and Patel, A. 2015. Return of scented wood. Down to Earth. https://www.downtoearth.org.in/coverage/forests/return-of-scented-wood-48569
• Panigrahi, K.P., Das, H., Sahoo, A.K., Moharana, S. C. 2020. Maize leaf disease detection and classification using machine learning algorithms. In Progress in Computing, Analytics and Networking: Proceedings of ICCAN 2019 (pp. 659-669). Springer Singapore.
• Parker, A., Heflin, A., Jones, L.C. 2021. Analyzing University of Virginia Health publications using open data, Python, and Streamlit. Journal of the Medical Library Association: JMLA, 109(4):688.
• Patel, R.P., Pandey, G.N., Tiwari, G., Patidar, H., Patidar, D.K. 2015. First report of Powdery mildew fungi on Sandal wood in Madhya Pradesh. Int J Curr Res, 7(6):16705-16708.
• Patro, V.M. and Patra, M.R. 2014. Augmenting weighted average with confusion matrix to enhance classification accuracy. Transactions on Machine Learning and Artificial Intelligence, 2(4):77-91.
• Pertot, I., Kuflik, T., Gordon, I., Freeman, S., Elad, Y. 2012. Identificator: A web-based tool for visual plant disease identification, a proof of concept with a case study on strawberry. Computers and electronics in agriculture, 84:144-154.
• Prathusha, P., Murthy, K.S., Srinivas, K. 2019. Plant Disease Detection Using Machine Learning Algorithms. In International Conference on Computational and Bio-Engineering (pp. 213-220). Springer, Cham.
• Rai, S.N. 1990. Status and cultivation of sandalwood in India. In: Hamilton, Lawrence; Conrad, C. Eugene, technical coordinators. Proceedings of the Symposium on Sandalwood in the Pacific; April 9-11, 1990; Honolulu, Hawaii. Gen. Tech. Rep. PSW-GTR-122. Berkeley, CA: Pacific Southwest Research Station, Forest Service, US Department of Agriculture, 122: 66-71 .
• Rajula, H. S. R., Verlato, G., Manchia, M., Antonucci, N., Fanos, V. 2020. Comparison of conventional statistical methods with machine learning in medicine: diagnosis, drug development, and treatment. Medicina, 56(9):455.
• Ramesh, S., Hebbar, R., Niveditha, M., Pooja, R., Shashank, N., Vinod, P.V. 2018 April. Plant disease detection using machine learning. In 2018 International conference on design innovations for 3Cs compute communicate control (ICDI3C) (pp. 41-45). IEEE.
• Ranganathan, P., Pramesh, C.S. and Aggarwal, R. 2017. Common pitfalls in statistical analysis: logistic regression. Perspectives in clinical research, 8(3):148.
• Romero, F., Cazzato, S., Walder, F., Vogelgsang, S., Bender, S.F., Van Der Heijden, M.G.A. 2022. Humidity and high temperature are important for predicting fungal disease outbreaks worldwide. New Phytologist, 234:1553–1556.
• Sannakki, S.S., Rajpurohit, V.S., Nargund, V.B., Kumar, A.R. and Yallur, P.S. 2011. A hybrid intelligent system for automated pomegranate disease detection and grading. Int. J. Mach. Intell, 3(2):36-44.
• Saputra, R.A., Wasiyanti, S., Saefudin, D.F., Supriyatna, A., Wibowo, A. 2020. Rice leaf disease image classifications using KNN based on GLCM feature extraction. In Journal of Physics: Conference Series (Vol. 1641, No. 1, p. 012080). IOP Publishing.
• Sharma, R.C., Duveiller, E., Ortiz-Ferrara, G. 2007. Progress and challenge towards reducing wheat spot blotch threat in the Eastern Gangetic Plains of South Asia: is climate change already taking its toll? Field Crops Research, 103(2):109-118.
• Singh, B.K., Delgado-Baquerizo, M., Egidi, E. et al., 2023, Climate change impacts on plant pathogens, food security and paths forward. Nature Reviews Microbiology, 21:640–656. https://doi.org/10.1038/ s41579-023-00900-7.
• Song, Y.Y. and Ying, L.U. 2015. Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2):130.
• Srinivasan, V.V., Sivaramakrishnana, V.R., Rangaswamy, C.R., Anathapadmanabha, H.S., Shankaranarayana, K.H. 1992. Sandal- (Santalum album Linn.). Published by Institute of Wood Science and Technology, Indian Council of Forestry Research and Education, Dehra Dun, India, p.233.
• Subasinghe, S.M.C.U.P. 2013, Sandalwood research: a global perspective. Journal of Tropical Forestry and Environment, 3(1):1-8.
• Tahmooresi, M., Afshar, A., Rad, B.B., Nowshath, K.B., Bamiah, M.A. 2018. Early detection of breast cancer using machine learning techniques. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(3-2):21-27.
• Vaishnnave, M.P., Devi, K.S., Srinivasan, P., Jothi, G.A.P. 2019. Detection and classification of groundnut leaf diseases using KNN classifier. In 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1-5). IEEE.
• Viswanath, S. 2014. Sandalwood, An unexplored treasure. Inside Fact, National monthly, 1(2):10-13.
• Viswanath, S., and Chakraborty., 2022. Indian Sandalwood Cultivation Prospects in India. In: Arunkumar, A.N., Joshi, G., Warrier, R.R., Karaba, N.N. (Eds.) Indian Sandalwood. Materials Horizons: From Nature to Nanomaterials. Springer, Singapore.
• Yang, X. and Guo, T. 2017. Machine learning in plant disease research. European Journal of BioMedical Research, 3(1):6-9.