Pest detection system with artificial intelligent agricultural forecasting techniques

Transcription

1 Pest detection system with artificial intelligent agricultural forecasting techniques M.Malathi Student, M.E Applied Electronics IFET College of Engineering Villupuram, India S.Mohamed Nizar Associate Professor, Department of ECE IFET College of Engineering Villupuram, India ABSTRACT-The major challenge in the agriculture field is early detection of pest. Insect pests, diseases and weeds cause enormous losses to the production of agricultural products. At the same time, there is a rising public concern chemical pesticide that has an adverse effect on the human health, environment and biodiversity. Finally in order to have pest control measure that is how much pesticide should be used, we have to study how much time does pest takes to replicate and how much it replicates though counting pest. The infected leaf images are acquired from the field. The acquired images are processed by various image processing technique. For segmentation Tensor field segmentation technique is used. Extracted feature is classified by neural network. Key words: Pest, Neural Network, Tensors, leaf. I.INTRODUCTION The economy of developing countries like India is greatly depend on agriculture. The quantity and quality of agricultural product is reduced due to plant disease and pest. Plant disease is caused by micro-organism like fungi and bacteria.the lifecycle of microorganism is unable to predict.some disease do not have visibility during early stage it appear only at the mature stage. The plant disease and pest detection by naked eye is used in practice but results are subjective and extent of disease is not measured precisely.nowadays automatic detection of plant disease and pest detection is an important research topic and thus detects the diseases automatically from the symptoms that appear on the plant leaves. Pawan p.warne & et.al [1] in this paper Detection of disease on cotton leaves using k-means clustering method describes the approach to prevent the crops from heavy loss by careful detection of disease. In cotton, diseases in leaf are critical issue because it reduces the production of cotton. The region of interest is leaf because most of diseases occur in leaf only. The diseases that occur in cotton leaf are Alternaria, Cercospora and Red Leaf Spot. Histogram equalization is used to preprocess the input image to increase the contrast in low contrast image, K-means clustering algorithm is used for segmentation which classifies objects based on a set of features into K number of classes and finally classification is performed using Neural-network. Thus image processing technique is used for detecting diseases on cotton leaves early and accurately. It is used to analyze the cotton diseases which will be useful to farmers. Daisy shergill & et.al[2] in this paper extraction of rice disease using image processing describes a approach is useful in crop protection especially large area farms, which is based on computerized image processing techniques that can detect diseased leaves using color information of leaves. It can be summarized by capturing an image of a certain plant leaf followed by extracting feature from the captured image then convert rgb to gray image & resize it, Create stem, stirs, canny edge detection, apply various comparison techniques, which would decide the disease and would also detect the type of plants diseases at early stages and enables early control and protection measures. Malvika Ranjan & et.al [3] in this paper detection and classification of leaf disease using artificial neural network describes a diagnosis process that is inherently visual and requires intuitive judgment as well as the use of scientific methods. The work begins with capturing the images. Color feature like HSV features are extracted from the result of E-ISSN : Page 120

2 segmentation and Artificial neural network (ANN) is then trained by choosing the feature values that could distinguish the healthy and diseased samples appropriately. Experimental results showed that classification performance by ANN taking feature set is better with an accuracy of 80%. Renuka Rajendra Kajale [4] in this paper Detection & recognition of plant leaf diseases using image processing and android o.s presented a software solution for automatic detection and computation of texture statistics for plant leaf diseases. The processing system consists of four main steps, first a color transformation structure for the input RGB image is created, then the green pixels are masked and removed using specific threshold value, then the image is segmented and the useful segments are extracted, finally the texture statistics is computed. From the texture statistics, the diseases, if present on the plant leaf are evaluated. II. PROPOSED METHODOLOGY The block diagram of the proposed methodology is shown in figure 2.1 A. IMAGE ACQUISITION The first and foremost step is image acquisition. Infected leaf images are acquired from the agricultural field. In order to process any image, the image must be acquired so as to perform the necessary. The illumination energy is transmitted or reflected based on the nature of the source. B. IMAGE PREPROCESSING The second step is image preprocessing. Preprocessing is a noise removal technique. Images are acquired with various resolutions. In order to make them unique preprocessing is done. To restore pixel brightness transformation image smoothing and Gaussian noise removal is done. C. IMAGE ENHANCEMENT The next step is image enhancement. After preprocessing the may be the possible edge and quality distortion. The preprocessed image is enhanced using histogram equalization technique. This technique transform the preprocessed image into function T, the grey values in output are equally distributed in [0, 1]. Let us assume that the input image to be enhanced has continuous gray values, with r = 1 representing white and r = 0 representing black. Grey value transformation s = T(r), based on the input image histogram, which will enhance the image. Where r = T -1 (s) denote the inverse transformation. The above two conditions is satisfied by inverse transformation. We consider that in the input image and output image grey value as random variables in the interval [0, 1]. Figure 2.1 Block diagram of proposed methodology The six major steps are image acquisition, image preprocessing, image enhancement, image segmentation, feature extraction, and classifier. The description for each step is given below. D. IMAGE SEGMENTATION The next step is image segmentation. Image segmentation is used to separate an image into several meaningful parts. The enhanced image is segmented using tensor field segmentation. Tensors are the mathematical object that describes the physical properties like scalar and vector. Tensors are the extensions to scalar and vector. It represents the linear quantity in form of n-dimensional array. Many algorithms have been developed for gray scale images. In this E-ISSN : Page 121

3 proposed work tensor field segmentation is used to extract the each particle and high speed of convergence. The three pest in leaf image. important steps are The segmentation process consists of two steps: Initialization of each particle. 1. Integral topology graph is extracted based on both Calculate fitness value. minor and major eigen vector fields. Thereby, the aim Update position and velocity. is not to show the correct topological structure but to The flow chart of PSO algorithm is shown in figure 2.1. The use it as a basic frame. pbest value is calculated by the following equation. 2. An adaptive segmentation workflow using the Yi(t+1)= Yi(t) if f ( Xi(t+1)) f( Xi(t)) (1) Eigen value fields to coarsen and subdivide the initial Yi(t+1)= Xi(t+1) if f ( Xi(t+1)) < f( Yi(t)) (2) segmentation. The set of building blocks in workflow The global best value is calculated by using the following which can be flexibly combined to meet specific equation needs of the user or application. Ỹ(t) {Y0,Y1..,Ys}= min{ f(y0(t)), f(y1(t)), f(ys(t))} (3) The position and velocity of each particle is calculated by the E.FEATURE EXTRACTION following equation The next step is feature extraction. The main objective Vi,j (t+1) = Wvi,j (t)+c1r1,j(t ) (Yi,j(t) - Xi,j(t)) (4) of feature extraction is to find meaningful low dimensional representations of high dimensional pattern such that the inherent data structures and relations are revealed. The segmented image is given to the feature extraction. The feature is extracted by using grey level co-occurrence matrix and Gabor filter. A co-occurrence matrix or co-occurrence distribution is a distribution or matrix that is defined over an image to be the distribution of co-occurring values at a given offset. Mathematically, a co-occurrence matrix C is defined over an n m image I, parameterized by an offset ( Δx, Δy), any matrix or pair of matrices can be used to generate a co-occurrence matrix, measuring the texture of images is the main applicability of the grey level co-occurrence matrix. F. CLASSIFIER Neural network is used to classify the pest depending on feature extracted from the input image. Particle swarm optimization is used for training the neural network. PSO techniques work with high dimensional datasets and mixed attribute data. PSO algorithm is responsible for optimum solution for parameter selection. It is one of population based iterative learning algorithm. The main advantages of PSO are ease of its implementation, memory for Figure 2.1 Flow chart of PSO Back propagation, an abbreviation for "backward propagation of errors", is a common method of training E-ISSN : Page 122

4 artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of a loss function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the loss function. The steps involved in back propagation neural network is shown in fig 2.2. Figure 3.1 Segmentation Result Figure 2.2 Flow diagram of Back propagation neural network Figure 3.2 Classification Result III.EXPERIMENTAL RESULTS The leaf image is taken from the agricultural field is given as input to the following steps. The input is shown below in figure 3.1. The preprocessed and segmented image is shown in fig 3.1. The classification result is shown in fig 3.2. Prediction analysis for the detected pest is shown in fig 3.3. It shows the pest count and type of pesticide for a particular pest. Figure3.3 Prediction Analysis CONCLUSION Automatic pest detection in plant leaf is one of the important factors in agricultural field. Here, pest in plant leaf is detected with the help of algorithm such as neural network. The E-ISSN : Page 123

5 real time images are preprocessed to remove the noise and it is applied for segmentation. The preprocessed image is segmented using tensor field segmentation. Segmented image is applied for feature extraction. The feature is extracted with the help of Gray level co-occurrence matrix algorithm. Next for the classification process support vector machine and neural network is applied (i.e.) Feed forward and Back propagation neural network is used here as classifier. This classifier classifies the images efficiently and produces the desired result. [4] Renuka Rajendra Kajale, Detection & recognition of plant leaf diseases using image processing and android o.s International Journal of Engineering Research and General Science Volume 3, Issue 2, Part 2, March-April, ISSN [5] Prakash M. Mainkar, Shreekant Ghorpade, Mayur Adawadkar, Plant Leaf Disease Detection and Classification Using Image Processing Techniques, International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 4, 2015, [6] Mr. Sachin B. Jagtap, Mr. Shailesh M. Hambarde, Agricultural Plant Leaf Disease Detection and Diagnosis Using Image Processing Based on Morphological Feature Extraction, IOSR Journal of VLSI and Signal Processing (IOSR-JVSP) Volume 4, Issue 5, Ver. I (Sep-Oct. 2014), PP References [1] PawanP.Warne, Dr.S.R. Ganorkar Detection of Diseases on Cotton Leaves Using K-Mean Clustering Method, International Research Journal of Engineering and Technology (IRJET) Volume: 02 Issue: 04 July-2015, [2] Daisy Shergill, Akashdeep Rana, Harsimran Singh Extraction of rice disease using image processing, International Journal Of Engineering Sciences & Research technology, June, 2015, [3] Malvika Ranjan, Manasi Rajiv Weginwar, Neha Joshi, Prof.A.B. Ingole, detection and classification of leaf disease using artificial neural network, International Journal of Technical Research and Applications e-issn: , Volume 3, Issue 3 (May-June 2015), PP [7] Niket Amoda, Bharat Jadhav, Smeeta Naikwadi, Detection And Classification Of Plant Diseases By Image Processing, International Journal of Innovative Science, Engineering & Technology, Vol. 1 Issue 2, April [8] Smita Naikwadi, Niket Amoda, Advances In Image Processing For Detection of Plant Diseases, International Journal of Application or Innovation in Engineering & Management (IJAIEM) Volume 2, Issue 11, November [9] Anand.H.Kulkarni, AshwinPatil R. K., Applying image processing technique to detect plant disease International Journal of Modern Engineering Research (IJMER) Vol.2, Issue.5, Sep-Oct pp ISSN: E-ISSN : Page 124