Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing

Corn is one of major crops in Indonesia. Diseases outbreak could significantly reduce the maize production, causing millions of rupiah in damages. To reduce the risks of crop failure due to diseases outbreak, machine learning methods can be implemented. Naked eyes inspection for plant diseases usually based on the changes in color or the existence of spots or rotten area in the leaves. Based on these observations, In this paper, we investigate several image processing based features for diseases detection of corn. Various image processing features to detect color such as RGB, local features on images such as scale-invariant feature transform (SIFT), speeded up robust features (SURF), and Oriented FAST and rotated BRIEF (ORB), and object detector such as histogram of oriented gradients (HOG). We evaluate the performance of these features on several machine learning algorithms. They are support vector machines (SVM), Decision Tree (DT), Random forest (RF), and Naive Bayes (NB). Our experimental evaluations indicate that the color may be the most informative features for this task. We find that RGB is the feature with the best accuracy for most classifiers we evaluate.

[1]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[2]  Reza Ehsani,et al.  Review: A review of advanced techniques for detecting plant diseases , 2010 .

[3]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning, 2006 , 2012 .

[4]  Irina V. Tsapko,et al.  Object’s Border and Position Allocating in an X-Ray Image , 2015 .

[5]  Mrunalini R. Badnakhe,et al.  An Application of K-Means Clustering and Artificial Intelligence in Pattern Recognition for Crop Diseases , 2011 .

[6]  Hod Lipson,et al.  Image set for deep learning: field images of maize annotated with disease symptoms , 2018, BMC Research Notes.

[7]  M. Mclean,et al.  Improved RNA Extraction from Woody Plants for the Detection of Viral Pathogens by Reverse Transcription-Polymerase Chain Reaction. , 1997, Plant disease.

[8]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[9]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[10]  Mingming Zhang,et al.  Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks , 2018, IEEE Access.

[11]  A V Vlasov,et al.  A machine learning approach for grain crop’s seed classification in purifying separation , 2017 .

[12]  Marcel Salathé,et al.  An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing , 2015, ArXiv.

[13]  Samsuzana Abd Aziz,et al.  Early detection of diseases in plant tissue using spectroscopy – applications and limitations , 2018 .

[14]  Carlo Tomasi,et al.  Histograms of Oriented Gradients , 2015 .

[15]  L. Plümer,et al.  Original paper: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance , 2010 .