Automated Detection of Plant Disease Based on Color Histogram Feature Selection Using Hybrid Random Forest with Adaboost Algorithm

Document Type : Original Article


1 Department of Mathematics, VMKV Engineering College, Salem, P.O. Box: 636308, Tamil Nadu, India.

2 Department of CSE, Mahendra Engineering College, Namakkal, P.O. Box: 636308, Tamil Nadu, India.


Multiple microbes can alter a plant's development and agricultural productivity, which has significant implications for the ecosystem and human life. As a result, timely identification, prevention, and prompt treatment are required. Fundamental methods have some drawbacks to plant disease identification like more time-consuming, accuracy, doesn't support multiple plant detection. This paper introduces a hybrid model that uses a random forest classifier combined with the AdaBoost Classifier to classify plant diseases to overcome the above-said drawbacks. So as to individualize normal and abnormal leaves from data sets, the suggested methodology employs the Random Forest with AdaBoost algorithm. The operational processes in our suggested study are preprocessing, segmentation, feature extraction, training the classifier, and classification. The produced datasets of infected and uninfected leaves are combined and processed using the Random Forest classifier to categorize the infected and uninfected photos. Color Histogram is used to gather features from imagery. KNN, Naive Bayes, and SVM are all used to evaluate our suggested technique.


Main Subjects

  1. Mattihalli C, Gedefaye E, Endalamaw F,  Necho A. Plant leaf diseases detection and automedicine. Inter Thing. 2018; 1: 67-73.
  2. Himani A. An analysis of agriculture sector in Indian economy. IOSR J Human Soc. 2014; 19(1): 47–54. Doi:
  3. Badage A. Crop disease detection using machine learning: Indian agriculture. Inter Res J Eng Technol. 2018; 5(9): 866-869.
  4. Ganatra N, Patel A. A multiclass plant leaf disease detection using image processing and machine learning techniques. Inter J Emer Technol. 2020; 11(2): 1082-1086.
  5. Iqbal Z, Khan MA, Sharif M, Shah J H, Ur Rehman MH, Javed K. An automated detection and classification of citrus plant diseases using image processing techniques: A review. Comp Electron Agr. 2018; 153: 12-32.
  6. Pawar P, Turkar V, Patil P. Cucumber disease detection using artificial neural network. International Conference on Inventive Computation Technologies (ICICT). 2016; 3:1-5. 
  7. Saradhambal G, Dhivya R, Latha S,  Rajesh R. Plant disease detection and its solution using image classification. Inter J Pure Appl Math. 2018; 119: 879-883.
  8. Khairnar K, Rahul D. Disease detection and diagnosis on plant using image processing–a review. Inter J Comp Appl. 2014; 108(13): 36-38.
  9. Narayanan BN, Djaneye-Boundjou O, Kebede TM. Performance analysis of machine learning and pattern recognition algorithms for malware classification. Aeros Electron. Conference (NAECON) , 2016; 338-342.
  10. Luo L, Tang Y, Zou X, Ye M, Feng W, Li G. Vision based extraction of spatial information in grape clusters for harvesting robots. Biosyst Eng. 2016; 151: 90-104. Doi: org/10.1016/j.biosystemseng.2016.08.026.
  11. Raja R, Nguyen TT, Vuong VL, Slaughter DC, Fennimore SA. Rtd-seps: real-time detection of stem emerging points and classification of crop-weed for robotic weed control in producing tomato. Biosyst Eng. 2020; 195: 152-171. Doi: org/10.1016/j.biosystemseng.2020.05.004
  12. Zheng J, Fu H, Li W, Wu W, Yu L, Yuan S, Tao WYW, Pang TK, Kanniah KD. Growing status observation for oil palm trees using unmanned aerial vehicle (uav) images. ISPRS J. Photogramm. Remote Sens. 2021; 173: 95-121. Doi: org/10.1016/j.isprsjprs.2021.01.0088.
  13. Li B, Zhao X, Zhang Y, Zhang S, Luo B. Prediction and monitoring of leaf water content in soybean plants using terahertz time-domain spectroscopy, Comp Electron Agr. 2020; 170: 105239. Doi: org/10.1016/j.compag.2020.105239
  14. Raja R, Nguyen TT, Vuong VL, Slaughter DC, Fennimore SA, Rtd-seps: real-time detection of stem emerging points and classification of crop-weed for robotic weed control in producing tomato, Biosyst Eng. 2020; 195: 152-171. Doi: org/10.1016/j.biosystemseng.2020.05.004.
  15. Iqbal MA, Talukder KH. Detection of potato disease using image segmentation and machine learning. International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), 2020; 43-47. doi: 10.1109/WiSPNET48689.2020.9198563.
  16. Dhingra G, Kumar V, Joshi HD. A novel computer vision based neutrosophic approach for leaf disease identification and classification. Measurement. 2019; 135: 782-794.
  17. Mohan KJ, Balasubramanian M, Palanivel S. Detection and recognition of diseases from paddy plant leaf images. Inter J Comp Appl. 2016; 144(12): 34-41. 
  18. Santanu P, Sil J, Kumar Das A. Classification of rice leaf diseases based on morphological changes. Intern J Info Electron Eng. 2012; 2(3): 460-463. 
  19. Deshmukh R, Deshmukh M. Detection of paddy leaf diseases. Inter J Comp Appl. 2015; 2(3): 8-10.
  20. Al Bashish D., Malik B., Sulieman B. Detection and classification of leaf diseases using K-means-based segmentation and neural network based classification. Info Technol J. 2011; 10(2): 267-275. DOI: 10.3923/itj.2011.267.275.
  21. Kumar SP, Negi B, Bhoi N. Detection of healthy and defected diseased leaf of rice crop using K-means clustering technique. Intern J Comp Appl. 2017; 157(1): 0975-8887. 
  22. Rashedul I, Rafiqul Islam M. An image processing technique to calculate percentage of disease affected pixels of paddy leaf. Intern J Comp Appl. 2015; 123(12): 28-34. 
  23. Nargis P. Detection and recognition of paddy plant leaf diseases using machine learning technique. Inter J Innov Technol Expl Eng. (IJITEE). 2020; 9(5): 634-638. 
  24. Ramesh S, Vydeki D. Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm. Inf Proc Agr. 2020; 7(2): 249-260. 
  25. Marshia Binth-E M, Mehrab Hossain M. Rice doctor: paddy disease detection from leaf image using machine learning, 2020. 
  26. Saleem G, Akhtar M, Ahmed N, Qureshi W. Automated analysis of visual leaf shape features for plant classification. Comp Electron Agri. 2019; 157: 270-280. Doi: org/10.1016/j.compag.2018.12.038
  27. Zhuang J., Luo S., Hou C., Tang Y., He Y., Xue X. Detection of orchard citrus fruits using a monocular machine vision-based method for automatic fruit picking applications. Comp Electron Agr. 2018; 152: 64-73. Doi: org/10.1016/j.compag.2018.07.004.
  28. Zhang S, Wu X, You Z, Zhang L. Leaf image based cucumber disease recognition using sparse representation classification. Comp Electron Agr. 2017; 134: 135-141. Doi: org/10.1016/j.compag.2017.01.014.
  29. Tian K, Li J, Zeng J, Evans A, Zhang L. Segmentation of tomato leaf images based on adaptive clustering number of k-means algorithm. Comp Electron Agr. 2019;165: 104962. Doi:org/10.1016/j.compag.2019.104962.
  30. Garcia-Lamont F, Cervantes J, Lopez A, Rodriguez L. Segmentation of images by color features: a survey. Neurocomputing. 2018; 292: 1-27. Doi: org/ 10.1016/j.neucom.2018.01.091.