Hybrid Features based Classification of Insect and Leaf Disease of Soybean Plants using Random Forest Classifier

Hybrid Features based Classification of Insect and Leaf Disease of Soybean Plants using Random Forest Classifier

© 2023 by IJETT Journal
Volume-71 Issue-2
Year of Publication : 2023
Author : Chandrakant Mahobiya, Sailesh Iyer, Savita Kolhe
DOI : 10.14445/22315381/IJETT-V71I2P242

How to Cite?

Chandrakant Mahobiya, Sailesh Iyer, Savita Kolhe, "Hybrid Features based Classification of Insect and Leaf Disease of Soybean Plants using Random Forest Classifier," International Journal of Engineering Trends and Technology, vol. 71, no. 2, pp. 408-420, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I2P242

With the growth in the global population, agricultural productivity must expand. Since insects (pests) and crop diseases are among the difficulties farmers encounter, they can cause significant agricultural loss. It is critical to creating solutions that reduce losses in order to boost production. Some of these technologies are environmentally friendly, such as those designed for the automated and early diagnosis of diseases using image processing techniques in conjunction with deep learning computational algorithms. In India, more than 40 species of insect pests of this crop were registered, one of the most relevant being Blue beetle adult/ mrl Larvae/ mrl G.gemma Larvae/mrl, .acuta, Heliothis, Grey weevil adult/ mrl, Stem fly incidence % Plant inf./ mrl Girdle, beetle. The primary goal of this research is to explore the accuracy and efficiency of computational approaches used in the problem of soybean leaf disease detection and insect classification, which are implemented with the utilization of hybrid features. Soybean insect and leaf diseases automatic classification and the prediction model are presented with hybrid features created by extracting deep features from a convolutional neural network (CNN) and texture features (Acquired from Gabor Wavelet and Harris Corner Method). The proposed hybrid features are then classified by a random forest classifier. MATLAB-based simulations exhibit the performance for insects and disease detection and classification.

Convolutional Neural Network, Deep learning, Gabor wavelet, Harris corner, Random forest classifier, Soybean.

[1] Andreas Kamilaris, and Francesc X. Prenafeta-Boldú, “Deep Learning in Agriculture: A Survey,” Computers and electronics in agriculture, vol. 147, pp. 70-90, 2018. Crossref, https://doi.org/10.1016/j.compag.2018.02.016
[2] Justine Boulent et al., “Convolutional Neural Networks for the Automatic Identification of Plant Diseases,” Frontiers in plant science, vol. 10, 2019. Crossref, https://doi.org/10.3389/fpls.2019.00941
[3] Hiago de O. Gomes et al., “A Socio-environmental Perspective on Pesticide use and Food Production,” Ecotoxicology and Environmental Safety, vol. 197, p.110627, 2020. Crossref, https://doi.org/10.1016/j.ecoenv.2020.110627
[4] Antonio Monteiro, Sergio Santos, and Pedro Gonçalves, “Precision Agriculture for Crop and Livestock Farming—Brief Review,” Animals, vol. 11, no. 8, p. 2345, 2021. Crossref, https://doi.org/10.3390/ani11082345
[5] R.S. Latha et al., “Automatic Detection of Tea Leaf Diseases using Deep Convolution Neural Network,” 2021 International Conference on Computer Communication and Informatics (ICCCI), pp. 1-6, 2021. Crossref, https://doi.org/10.1109/ICCCI50826.2021.9402225
[6] Sachin B. Jadhav, Vishwanath R. Udupi, and Sanjay B. Patil, “Identification of Plant Diseases using Convolutional Neural Networks,” International Journal of Information Technology, vol. 13, pp. 2461-2470, 2021. Crossref, https://doi.org/10.1007/s41870-020-00437-5
[7] Zhou Libo, Huang Tian, and Guan Chunyun, “Wireless Multimedia Sensor Network for Rape Disease Detections,” EURASIP Journal on Wireless Communications and Networking, 2019. Crossref, https://doi.org/10.1186/s13638-019-1468-3
[8] Uday Pratap Singh et al., “Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease,” IEEE Access, vol. 7, pp. 43721-43729, 2019. Crossref, https://doi.org/10.1109/ACCESS.2019.2907383
[9] Siddharth Singh Chouhan, Uday Pratap Singh, and Sanjeev Jain, “Applications of Computer Vision in Plant Pathology: A Survey,” Archives of Computational Methods in Engineering, vol. 27, pp. 611-632, 2020. Crossref, https://doi.org/10.1007/s11831-019-09324-0
[10] Luiz F.S. Coletta et al., “Novelty Detection in UAV Images to Identify Emerging Threats in Eucalyptus Crops,” Computers and Electronics in Agriculture, vol. 196, p. 106901. 2022. Crossref, https://doi.org/10.1016/j.compag.2022.106901
[11] Marwan Adnan Jasim, and Jamal Mustafa Al-Tuwaijari, “Plant Leaf Diseases Detection and Classification Using Image Processing and Deep Learning Techniques,” 2020 International Conference on Computer Science and Software Engineering (CSASE), pp. 259-265, 2020. Crossref, https://doi.org/10.1109/CSASE48920.2020.9142097
[12] Sumit Kumar, Veerendra Chaudhary, and Supriya Khaitan Chandra, “Plant Disease Detection using CNN,” Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 12, pp. 2106-2112, 2021.
[13] Lili Li, Shujuan Zhang, and Bin Wang, “Plant Disease Detection and Classification by Deep Learning—A Review,” IEEE Access, vol. 9, pp. 56683-56698, 2021. Crossref, https://doi.org/10.1109/ACCESS.2021.3069646
[14] S. Vieira, W.H.L. Pinaya, and A. Mechelli, Introduction to Machine Learning, Machine learning, Academic Press, 2020.
[15] Qiufeng Wu, Keke Zhang, and Jun Meng, “Identification of Soybean Leaf Diseases via Deep Learning,” Journal of The Institution of Engineers (India): Series A, vol. 100, pp. 659-666, 2019. Crossref, https://doi.org/10.1007/s40030-019-00390-y
[16] Laercio Zambolim et al., “How to Cope with the Vulnerability of Site Specific Fungicides on the Control of Asian Soybean Rust,” International Journal of Research in Agronomy, vol. 4, no. 1, pp.14-25, 2021. Crossref, http://dx.doi.org/10.33545/2618060X.2021.v4.i1a.44
[17] U. Shruthi, V. Nagaveni, and B.K. Raghavendra, “A Review on Machine Learning Classification Techniques for Plant Disease Detection,” 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), pp. 281-284, 2019. Crossref, https://doi.org/10.1109/ICACCS.2019.8728415
[18] Heng-An Lin, Maria B. Villamil, and Santiago X. Mideros, “Characterization of Septoria Brown Spot Disease Development and Yield Effects on Soybean in Illinois,” Canadian Journal of Plant Pathology, vol. 43, no. 1, pp. 62-72, 2021. Crossref, https://doi.org/10.1080/07060661.2020.1755366
[19] M.D.C.Ferreira, F. Griesang, and A.F. Barreto, “Effects of Nozzle Types, Adjuvants and Spray Volumes on the Spray Application Quality and Control of Asian Soybean Rust,” Aspects of Applied Biology, vol. 137, pp. 167-174, 2018.
[20] Gustavo C. Beruski et al., “Performance and Profitability of Rain-based Thresholds for Timing Fungicide Applications in Soybean Rust Control,” Plant Disease, vol. 104, no. 10, pp. 2704-2712, 2020. Crossref, https://doi.org/10.1094/PDIS-01-20-0210-RE
[21] Vipendra Parmar et al., “Relative Efficacy of Bio Pesticides in Managment of Dry Root Rot and Collar Rot in Soyabean and Chickpea,” International Journal of Chemical Studies, vol. 6, no. 2, pp. 83-86, 2018.
[22] Munmi Borah, “Identification of Soybean Diseases in Assam,” International Journal of Recent Scientific Research, vol. 10, pp .34154- 34159, 2019.
[23] D. Sheema et al., “The Detection and Identification of Pest-FAW Infestation in Maize Crops Using Iot-Based Deep-Learning Algorithm,” SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 12, pp. 180-188, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I12P116
[24] Eftekhar Hossain, Md. Farhad Hossain, and Mohammad Anisur Rahaman, “A Color and Texture Based Approach for the Detection and Classification of Plant Leaf Disease Using KNN Classifier,” 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1-6, 2019. Crossref, https://doi.org/10.1109/ECACE.2019.8679247
[25] Y.M. Oo, and N.C. Htun, “Plant Leaf Disease Detection and Classification using Image Processing,” International Journal of Research and Engineering, vol. 5, no. 9, pp. 516-523, 2018.
[26] K.B. Prakash, and G.R. Kanagachidambaresan, “Pattern Recognition and Machine Learning,” Programming with TensorFlow (pp. 105- 144). Springer, Cham.
[27] Shima Ramesh et al., “Plant Disease Detection using Machine Learning,” 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), pp. 41-45, 2018. Crossref, https://doi.org/10.1109/ICDI3C.2018.00017
[28] Silky Sachar, and Anuj Kumar, “Survey of Feature Extraction and Classification Techniques to Identify Plant Through Leaves,” Expert Systems with Applications, vol. 167, p. 114181, 2021. Crossref, https://doi.org/10.1016/j.eswa.2020.114181
[29] Adnan Mohsin Abdulazeez et al., “Leaf Identification Based on Shape, Color, Texture and Vines Using Probabilistic Neural Network,” Computación Y Sistemas, vol. 25, no. 3, pp. 617-631, 2021. Crossref, https://doi.org/10.13053/cys-25-3-3470
[30] Gargi Sharma, and Gourav Shrivastava, “Crop Disease Prediction using Deep Learning Techniques - A Review,” SSRG International Journal of Computer Science and Engineering, vol. 9, no. 4, pp. 23-28, 2022. Crossref, https://doi.org/10.14445/23488387/IJCSEV9I4P104
[31] R. Manavalan, “Automatic Identification of Diseases in Grains Crops Through Computational Approaches: A Review,” Computers and Electronics in Agriculture, vol. 178, p. 105802, 2020. Crossref, https://doi.org/10.1016/j.compag.2020.105802
[32] Abirami Devaraj et al., “Identification of Plant Disease using Image Processing Technique,” 2019 International Conference on Communication and Signal Processing (ICCSP), pp. 749-753, 2019. Crossref, https://doi.org/10.1109/ICCSP.2019.8698056
[33] Sushil R. Kamlapurkar, “Detection of Plant Leaf Disease using Image Processing Approach,” International Journal of Scientific and Research Publications, vol. 6, no. 2, pp. 73-76, 2018.
[34] P. Perumal, “Guava Leaf Disease Classification using Support Vector Machine,” Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 7, pp. 1177-1183, 2021. Crossref, https://doi.org/10.17762/turcomat.v12i7.2728
[35] Sukhvir Kaur, Shreelekha Pandey, and Shivani Goel, “Semi‐automatic Leaf Disease Detection and Classification System for Soybean Culture,” IET Image Processing, vol. 12, no. 6, pp. 1038-1048, 2018. Crossref, https://doi.org/10.1049/iet-ipr.2017.0822
[36] Sambuddha Ghosal et al., “An Explainable Deep Machine Vision Framework for Plant Stress Phenotyping,” Proceedings of the National Academy of Sciences, vol. 115, no. 18, pp. 4613-4618, 2018. Crossref, https://doi.org/10.1073/pnas.1716999115
[37] Everton Castelao Tetila et al., “Automatic Recognition of Soybean Leaf Diseases using UAV Images and Deep Convolutional Neural Networks,” IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 5, pp. 903-907, 2020. Crossref, https://doi.org/10.1109/LGRS.2019.2932385
[38] Wei Lu et al., “Soybean Yield Preharvest Prediction Based on Bean Pods and Leaves Image Recognition Using Deep Learning Neural Network Combined with GRNN,” Frontiers in Plant Science, vol. 12, 2021. Crossref, https://doi.org/10.3389/fpls.2021.791256
[39] V. Brindha Devi et al., “An Efficient and Robust Random Forest Algorithm for Crop Disease Detection,” 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT), pp. 1-4, 2022. Crossref, https://doi.org/10.1109/IC3IOT53935.2022.9767937
[40] Everton Castelao Tetila et al., “Detection and Classification of Soybean Pests Using Deep Learning with UAV Images,” Computers and Electronics in Agriculture, vol. 179, p. 105836, 2020. Crossref, https://doi.org/10.1016/j.compag.2020.105836
[41] Ashutosh Kumar Singh et al., “Hybrid Feature-based Disease Detection in Plant Leaf using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier,” Journal of Food Quality, 2022. Crossref, https://doi.org/10.1155/2022/2845320
[42] Lidong Dong et al., “Genetic Basis and Adaptation Trajectory of Soybean from its Temperate Origin to Tropics,” Nature Communications, vol. 12, 2021. Crossref, https://doi.org/10.1038/s41467-021-25800-3
[43] Mohammad Keivani et al., “Automated Analysis of Leaf Shape, Texture, and Color Features for Plant Classification,” Traitement du Signal, vol. 37, no. 1, pp. 17-28, 2020. Crossref, http://dx.doi.org/10.18280/ts.370103