Ant Colony Optimization Algorithm for Disease Detection in Maize Leaf using Machine Learning Techniques
Main Article Content
Abstract
Plant diseases have affected the productivity of food in recent years. Because of this productivity loss, humans and animals are also affected, but the whole biodiversity would be affected. So, we should take preventive measures to stop this food destruction. Both humans and animals largely consume the maize crop. Due to some factors, Maize leaf is easily affected by some fungal or other diseases. Farmers could not find out the leaf diseases at the early stages. They need some advanced methods to detect these types of diseases. Early detection of leaf disease helps farmers to increase the Maize yield. In the proposed algorithm, we have used five supervised machine learning algorithms such as K-nearest neighbor (KNN), naive bayes (NB), support vector machine (SVM), random forest (RF), logistic regression (LR) and then these machine learning models have been implemented with the Ant colony optimization (ACO) Algorithm for optimizing the accuracy of disease detection in Maize Leaf. For leaf classification, color and texture features are extracted from an input dataset. Features of a leaf can be described by Hu moments, Haralick texture, and color histogram. After performing all Machine learning classifiers, we have analyzed that Random Forest with Ant Colony Optimization Algorithm gives the highest accuracy of 99.4% for disease detection in Maize Leaf.
Downloads
Download data is not yet available.
Article Details
How to Cite
Kumar, A., & Singh, M. (2022). Ant Colony Optimization Algorithm for Disease Detection in Maize Leaf using Machine Learning Techniques. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 14(01), 31-37. https://doi.org/10.18090/samriddhi.v14i01.5
Section
Research Article
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
[1] Ganatra, N., & Patel, A. (2020). Deep Learning Methods and
Applications for Precision Agriculture. Machine Learning for
Predictive Analysis, 515-527.
[2] Sun, J., Yang, Y., Xiaofei, H., & Xiaohong, W. (2020). Northern
Maize Leaf Blight Detection Under Complex Field Environment
Based on Deep Learning. IEEE Access, 8, 33679 – 33688.
[3] Haggag, M., Abdelhay, S., Mecheter, A., Gowid, S., Musharavati,
F., & Ghani, S. (2019). An intelligent hybrid experimental-based
deep learning algorithm for tomato-sorting controllers. IEEE
Access, 7, 106890–106898.
[4] Ye, X., & Zhu, Q. (2019). Class-incremental learning based on
feature extraction of CNN with optimized softmax and oneclass
classi ers. IEEE Access, 7, 42024–42031.
[5] Jiang, P., Chen, Y., Liu, B., He, D., & Liang, C. (2019). Real-time
detection of apple leaf diseases using deep learning approach
based on improved convolutional neural networks. IEEE Access,
7, 59069–59080.
[6] Revathi, & Hemalatha, M. (2018). Classification of Cotton
Leaf Spot Diseases Using Image Processing Edge Detection
Techniques. IEEE, 169-173.
[7] Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.
8] Khan, M. A., Akram, T., Sharif, M., Awais, M., Javed, K., Ali, H.,
& Saba, T. (2018). CCDF: Automatic system for segmentation
and recognition of fruit crops diseases based on correlation
coe cient and deep CNN features. Computers and Electronics
in Agriculture, 155, 220–236.
[9] Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., & ALRahamneh,
Z. (2017). Fast and Accurate Detection and Classi cation of Plant
Diseases. IJCA, 17(1), 31-38.
[10] Venkata Krishna Bharadwaj Parasaram. (2021). Explainable
Machine Learning Models for Improving Decision Making in
Project Portfolio Management. Darpan International Research
Analysis, 9(1), 12–21. https://doi.org/10.36676/dira.v9.i1.188
[11] Sun, J., Tan, W. J., & Mao, H. P. (2017). Identi cation of plant leaf
diseases based on improved convolutional neural network.
Transactions of the Chinese Society of Agricultural Engineering,
33, 151–162.
[12] Nalluri, S. K., & Parasaram, V. K. B. (2015). Automating
Software Builds with Jenkins: Design Patterns and Failure
Handling. International Journal of Technology, Management
and Humanities, 1(01), 16-33.
https://doi.org/10.21590/ijtmh.01.02.03
[13] Bai, X., Cao, Z., Zhao, L., Zhang, J., Lv, C., Li, C., & Xie, J. (2018).
Rice heading stage automatic observation by multi-classi er
cascade-based rice spike detection method. Agricultural and
Forest Meteorology, 259, 260–270.
[14] Patricio, D. I., & Rieder, R. (2018). Computer vision and arti cial
intelligence in precision agriculture for grain crops: A systematic
review, Computers and Electronics in Agriculture, 153, 69–81.
[15] Lin, Z., Mu, S., Shi, A., Pang, C., & Sun, X. (2018). A novel
method of maize leaf disease image identi cation based on a
multichannel convolutional neural network, Transactions of the
ASABE (American Society of Agricultural and Biological Engineers),
61(5), 1461–1474.
Applications for Precision Agriculture. Machine Learning for
Predictive Analysis, 515-527.
[2] Sun, J., Yang, Y., Xiaofei, H., & Xiaohong, W. (2020). Northern
Maize Leaf Blight Detection Under Complex Field Environment
Based on Deep Learning. IEEE Access, 8, 33679 – 33688.
[3] Haggag, M., Abdelhay, S., Mecheter, A., Gowid, S., Musharavati,
F., & Ghani, S. (2019). An intelligent hybrid experimental-based
deep learning algorithm for tomato-sorting controllers. IEEE
Access, 7, 106890–106898.
[4] Ye, X., & Zhu, Q. (2019). Class-incremental learning based on
feature extraction of CNN with optimized softmax and oneclass
classi ers. IEEE Access, 7, 42024–42031.
[5] Jiang, P., Chen, Y., Liu, B., He, D., & Liang, C. (2019). Real-time
detection of apple leaf diseases using deep learning approach
based on improved convolutional neural networks. IEEE Access,
7, 59069–59080.
[6] Revathi, & Hemalatha, M. (2018). Classification of Cotton
Leaf Spot Diseases Using Image Processing Edge Detection
Techniques. IEEE, 169-173.
[7] Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.
8] Khan, M. A., Akram, T., Sharif, M., Awais, M., Javed, K., Ali, H.,
& Saba, T. (2018). CCDF: Automatic system for segmentation
and recognition of fruit crops diseases based on correlation
coe cient and deep CNN features. Computers and Electronics
in Agriculture, 155, 220–236.
[9] Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., & ALRahamneh,
Z. (2017). Fast and Accurate Detection and Classi cation of Plant
Diseases. IJCA, 17(1), 31-38.
[10] Venkata Krishna Bharadwaj Parasaram. (2021). Explainable
Machine Learning Models for Improving Decision Making in
Project Portfolio Management. Darpan International Research
Analysis, 9(1), 12–21. https://doi.org/10.36676/dira.v9.i1.188
[11] Sun, J., Tan, W. J., & Mao, H. P. (2017). Identi cation of plant leaf
diseases based on improved convolutional neural network.
Transactions of the Chinese Society of Agricultural Engineering,
33, 151–162.
[12] Nalluri, S. K., & Parasaram, V. K. B. (2015). Automating
Software Builds with Jenkins: Design Patterns and Failure
Handling. International Journal of Technology, Management
and Humanities, 1(01), 16-33.
https://doi.org/10.21590/ijtmh.01.02.03
[13] Bai, X., Cao, Z., Zhao, L., Zhang, J., Lv, C., Li, C., & Xie, J. (2018).
Rice heading stage automatic observation by multi-classi er
cascade-based rice spike detection method. Agricultural and
Forest Meteorology, 259, 260–270.
[14] Patricio, D. I., & Rieder, R. (2018). Computer vision and arti cial
intelligence in precision agriculture for grain crops: A systematic
review, Computers and Electronics in Agriculture, 153, 69–81.
[15] Lin, Z., Mu, S., Shi, A., Pang, C., & Sun, X. (2018). A novel
method of maize leaf disease image identi cation based on a
multichannel convolutional neural network, Transactions of the
ASABE (American Society of Agricultural and Biological Engineers),
61(5), 1461–1474.