Leaf Disease Detection using Raspberry Pi

Leaf Disease Detection using Raspberry Pi
Internal Guide: Mr. A. Srinivas Presented By 1.Kolluru Ramani Priya 2.Khammampati Mounika 3.Maktha Malleshwari 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

Department of ECE, CMR Engineering College, Hyderabad
MOTIVATION India is well known for its agriculture production. Farmers have variety of options to cultivate crops in thE field. Still, the cultivating these crops for best harvest and top quality of production is done in a technical way. So the yield can be increased and quality can be improved by the use of technology. Generally, whenever there is disease to a plant, we can say that leaves are the main indicator of the disease caused to the plant. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

The most important visual property is leafs texture and color. Hence, classification of leaf disease is necessary in evaluating agricultural produce, increasing market value and meeting quality standards. The process will be too slow, If the identification and categorization is done through physical techniques, we need the experts help sometimes it will be error prone and who are less available. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

The labor’s classify based on color, size etc. if these quality methods are recorded into automatic system by using appropriate program design language then the effort will be error free and faster. There is need for developing technique such as automatic plant disease detection and classification using leaf image processing techniques. This will prove useful technique for farmers and will alert them at the right time before spreading of the disease over large area. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

RATIONALE Plant disease detection is an interesting field in India. The plant diseases are such as fungi, bacteria, and viruses. The leaf diseases not only restrict the growth of the plant but also destroy its crop. There is the need of some expert to identify plant diseases but manual identification is time consuming as there are vast lands of agriculture in India and manual detection is highly difficult and time consuming. So, some automatic methods required. We therefore implement the image processing techniques in Python using Raspberry pi which can detect the disease at an early stage and thereby we can implement methods to restrict the spread of dseases as early as possible. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

OBJECTIVE The aim of this study is to design, implement and evaluate an image-processing-based software solution for automatic detection and classification of plant leaf diseases. Studies show that relying on pure naked-eye observation of experts to detect and classify such diseases can be prohibitively expensive, especially in developing countries. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

Providing fast, automatic, cheap and accurate image-processing-based solutions for that task can be of great realistic significance. The methodology of the proposed solution is image-processing-based and is composed of four main phases. In the first phase we create a color transformation structure for the RGB leaf image and then, we apply device-independent color space transformation for the color transformation structure. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

Next, in the second phase, the images at hand are segmented using the K-means clustering technique. In the third phase, we calculate the texture features for the segmented infected objects. Finally, in the fourth phase the extracted features are passed through a pre-trained neural network. Department of ECE, CMR Engineering College, Hyderabad 13/4/2020

ABSTRACT For the detection and prevention of disease of plants from getting spread, this paper discussed a system using raspberry PI. For the image analysis, the k-means clustering algorithm was used. It has many advantages for the use in big farms of crops and thus it automatically detects signs of disease whenever they appear on leaves of the plant. The term disease means the type of damage to the plants. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

In pharmaceutical research of leaf disease detection is necessary and important topic for research because it has advantages in monitoring crops in field at the form and thus it automatically detects symptoms of disease by image processing by k-means clustering algorithm. This paper provides the best method for detection of plant diseases using image processing and alerting about the disease caused by displaying the name of the disease on the monitor display of the owner of the system. Department of ECE, CMR Engineering College, Hyderabad 13/4/2020

To upgrade agricultural products, automatic detection of disease symptoms is useful. The design and implementation of these technologies which is totally automatic and it will significantly help in the chemical application. It will reduce the cost required for the pesticides and other products. This will lead to increase in productivity of the farming. Department of ECE, CMR Engineering College, Hyderabad 13/4/2020

EXISTING METHOD / LITERATURE SURVEY
Edge Detection The Canny Edge Detector is one of the most widely used image processing tools, paying attention to edges in a very robust way. This is used for the purpose of segmentation of the affected area on the leaf. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

Fig: Edge detection using Canny Support Vector Machine After extracting color and texture features, the classification is performed by using Support Vector Machine (SVM). Department of ECE, CMR Engineering College, Hyderabad 13/4/2020

Fig: SVM graph Department of ECE, CMR Engineering College, Hyderabad 13/4/2020

DRAWBACKS OF EXISTING METHOD
Drawbacks of edge detection The Canny algorithm contains a number of adjustable parameters, which can affect the computation time and effectiveness of the algorithm. The size of the Gaussian filter: the smoothing filter used in the first stage directly affects the results of the Canny algorithm. Smaller filters cause less blurring, and allow detection of small, sharp lines. A larger filter causes more 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

blurring, smearing out the value of a given pixel over a larger area of the image. Thresholds: A threshold set too high can miss important information. On the other hand, a threshold set too low will falsely identify irrelevant information (such as noise) as important. It is difficult to give a generic threshold that works well on all images. Complex Computations Department of ECE, CMR Engineering College, Hyderabad 13/4/2020

Drawbacks of SVM(Support Vector Machine) If training data is not linearly separable then it is difficult to determine optimal parameters in SVM, which appears as one of its drawbacks. Involves long training time, difficult to understand learned function. large no. of support vectors used from the training set to perform classification task. Department of ECE, CMR Engineering College, Hyderabad 13/4/2020

PROPOSED METHOD k-means clustering for segmentation The K-means clustering algorithm performs segmentation by minimizing the sum of squares of distances between the image intensities and the cluster centroids. K-means clustering algorithm, or Lloyd's algorithm, is an iterative algorithm that partitions the data and assigns n observations to precisely one of k clusters defined by centroids. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

The steps in the algorithm are given below. 1. Choose k initial cluster centers (centroid). 2. Compute point-to-cluster-centroid distances of all observations to each centroid. 3. Assign each observation to the cluster with the closest centroid. 4. Compute the mean of the observations in each cluster to obtain k new centroid locations. 5. Repeat steps 2 through 4 until there is no change in the cluster assignments. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

Fig: k-mean clustering graph Department of ECE, CMR Engineering College, Hyderabad 13/4/2020

k-NN classification The k-nearest neighbors (k-NN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. The k-NN algorithm assumes that similar things exist in close proximity. In other words, similar things are near to each other. Department of ECE, CMR Engineering College, Hyderabad 13/4/2020

Fig: k-NN classification Department of ECE, CMR Engineering College, Hyderabad 13/4/2020

BLOCK DIAGRAM 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

FLOWCHART/ ALGORITHM 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

RESULTS 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

ADVANTAGES Advantages of k-nn classification Very simple implementation. Robust with regard to the search space; for instance, classes don't have to be linearly separable. Classifier can be updated online at very little cost as new instances with known classes are presented. Few parameters to tune: distance metric and k. Advantages of k-mean clustering Ease of implementation HIgh speed and accuracy Measurable nd efficient in large data collection Easily adaptable to new samples 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

APPLICATIONS Applications of k-means clustering Document classification Identifying crime localities in a city Customer segmentation Fantasy league player stat analysis Applications of k-NN classification Agriculture Understanding and managing financial risk Trading futures 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

Credit rating Loan management Bank customer profiling Medicine 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

CONCLUSION The objective of this work is the detection, classification of leaf diseases using image processing tools and all information about the disease is displayed on monitor. To increase the speed and accuracy of detection as well as classification of leaf diseases we using Raspberry pi 3 model B modules. This system will largely contribute in growth in the yield of the farms. As SVM is very complex in calculations and it is not the cost effective testing of each instance and inaccurate to wrong inputs. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

KNN algorithm is effectual classifier would be used to minimize the computational cost. In previous researches it has proved that KNN has high accuracy rate. KNN classifier obtains highest result as compared to SVM. The comparison would be based upon two parameters Accuracy and Detection time. The study reviews and summarizes some techniques have been used for plant disease detection. A novel approach for classification of plant disease has been proposed. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

FUTURE SCOPE The future work can also be dedicated to the automatic estimation of the severity of these diseases. The instant solutions can be made available to the farmers by designing mobile based applications. Online solutions related to plant diseases can be provided by using web portals. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

REFERENCES M. Reyalat,AlHiary, BaniAhmad,Braik and Z. Rahamneh, “Fast and Accurate Detection and Classification of PlantDiseases”. Smith J S., Camargo A., “An imageprocessing based algorithm to automatically identify plant disease visualsymptoms”. Sulaiman, N., Mustapha,“K-means clustering to improve theaccuracy”. 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

4) David Mount,Tapaskanungo, “An Efficient k- means Clustering Algorithm: Analysis and Implementation”. 5) Ford A., Robert, “Colour SpaceConversions”,. C M Procopiuc, P. K. Agarwal, “Exact and Approximation Algorithms forClustering”. 6) Devendra P. Marathe, V. A. Patil, V. D. Chaudhari, “Reliable massage transfer to alternate ways i.e. Wi-Fi, Bluetooth, ZigBee, Internet, GPRS/GSM 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

THANK YOU 13/4/2020 Department of ECE, CMR Engineering College, Hyderabad

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