K-means Based Automatic Pests Detection and Classification for Pesticides Spraying

Agriculture is the backbone to the living being that plays a vital role to country’s economy. Agriculture production is inversely affected by pest infestation and plant diseases. Plants vitality is directly affected by the pests as poor or abnormal. Automatic pest detection and classification is an essential research phenomenon, as early detection and classification of pests as they appear on the plants may lead to minimizing the loss of production. This study puts forth a comprehensive model that would facilitate the detection and classification of the pests by using Artificial Neural Network (ANN). In this approach, the image has been segmented from the fields by using enhanced K-Mean segmentation technique that identifies the pests or any object from the image. Subsequently, features will be extracted by using Discrete Cosine Transform (DCT) and classified using ANN to classify pests. The proposed approach is verified for five pests that exhibited 94% effectiveness while classifying the pests.

[1]  V. D. Mytri,et al.  A Prediction Model for Population Dynamics of Cotton Pest (Thrips tabaci Linde) using Multilayer-Perceptron Neural Network , 2013 .

[2]  Thomas G. Dietterich,et al.  Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects , 2007, 2007 IEEE Workshop on Applications of Computer Vision (WACV '07).

[3]  Jangmyung Lee,et al.  Vision-based pest detection and automatic spray of greenhouse plant , 2009, 2009 IEEE International Symposium on Industrial Electronics.

[4]  Saeid Minaei,et al.  Vision-based pest detection based on SVM classification method , 2017, Comput. Electron. Agric..

[5]  Gang Liu,et al.  Research on Prediction about Fruit Tree Diseases and Insect Pests Based on Neural Network , 2005, AIAI.

[6]  Jian-Huang Lai,et al.  Face recognition using holistic Fourier invariant features , 2001, Pattern Recognit..

[7]  Alain Clément,et al.  A new colour vision system to quantify automatically foliar discolouration caused by insect pests feeding on leaf cells , 2015 .

[8]  Peter Auer,et al.  Generic object recognition with boosting , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Pornchai Phukpattaranont,et al.  A Novel Feature Extraction for Robust EMG Pattern Recognition , 2009, ArXiv.

[10]  Amirthalingam Ramanan,et al.  Image Classification of Paddy Field Insect Pests Using Gradient-Based Features , 2014 .

[11]  Shahrul Azman Mohd Noah,et al.  Fish recognition based on robust features extraction from color texture measurements using back-propagation classifier , 2010 .

[12]  Zhen Zhang,et al.  Auto-classification of insect images based on color histogram and GLCM , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[13]  C. Kesavadas,et al.  Fuzzy C-Means method for Colour Image Segmentation with L*U*V* Colour transformation , 2011 .

[14]  A. V. Deorankar,et al.  Classification of Agricultural Pests Using DWT and Back Propagation Neural Networks , 2014 .

[15]  Thomas G. Dietterich,et al.  Segmentation of touching insects based on optical flow and NCuts , 2013 .

[16]  Jeremy S. Smith,et al.  An image-processing based algorithm to automatically identify plant disease visual symptoms. , 2009 .

[17]  Vincent Martin,et al.  A cognitive vision approach to early pest detection in greenhouse crops , 2008 .