A Hybrid Machine Learning Approach For Disease Detection In Agriculture Plant Leaf

Abstract

Disease detection is crucial to scale back the losses in agricultural product yield and amount.Plant disease analyses include the study of variations found on the farm. Monitoring the security and identification of infections on plants is incredibly vital for organic farming. Manually, plant diseases can not be tracked simply in farms that contain completely different crops. This wants an enormous quantity of labor, expertise in plant diseases, and additionally needs substantial time interval. As a result, image processing is employed for disease detection. Detection of diseases includes measures like image collection, pre-processing of pictures, segmentation of images, detection, and recognition of characteristics. This paper mentioned the techniques used to diagnose plant diseases exploitation pictures of their leaves. This paper also addressed bound segmentation algorithms and extraction functions utilized in the identification of diseases of two completely different plants.

INTRODUCTION

The Indian economy relies on productive agriculture. Agriculture accounts for over 70 percent of rural homes. Farming pays around 17 percent of the total GDP[1] and supplies more than 60 percent of the population with jobs. Thus plant disease detection plays a crucial role in the agricultural environment.Indian farming comprises many plants such as maize, wheat, etc. Even rising sugar cane, corn, potatoes and non-food items such as cocoa, tea, cotton, rubber. Each of these plants is grown centered on leaf and root energy. There are things that cause different diseases for the plant leaves that spoil crops and eventually affect the country & its economy.

The plant disease experiments apply to research on the plants with visibly identifiable trends. Health and disease control of plants plays an important role in effective crop production in the field. In the early days, plant disease tracking and examination were done manually by the person with experience in this area. This needs a tremendous amount of work and considerable processing time as well. The image processing methods may be used in the diagnosis of plant disease and machine learning algorithms can be used to predict the diseases of three different plants. Symptoms of the diseases are found on the leaves, stem, and berries in most instances. The disease detection of the plant leaf is which suggests the symptoms of the disease. The plant disease experiments apply to research on the plants with visibly identifiable trends. In this article, we have performed a survey on various diseases of plants and specific specialized techniques to diagnose these conditions.

LITERATURE SURVEY

In the past few years, various developments within the agriculture field have arisen and that fetched an honest supply of financial gain for the farmers. And one in every of them will be an image process with machine learning algorithms. Pomegranate (Punica granatum) may be a deciduous tree fully grown in arid and semi-arid regions[2]. It develops well in areas of 25-35 degrees temperature and 500-800 millimeter annual downfall. Diseases have resulted in Brobdingnagian declines in developed pomegranate in recent years. Micro-organisms like fungi,microbes, and viruses are sometimes liable for these diseases. microorganism blight, seed stain, plant red, and leaf plot are the diseases[3]. Potato plants are straightforward to grow. they're fully grown virtually all told elements of the planet however many diseases have an effect on potato plants, however the foremost common diseases are a blight, fungus wilt, and Rhizoctonia canker. These diseases are simply known and if treated early enough, the plants could also be saved. If the diseases don't seem to be caught early enough, the complete plant ought to be removed. These diseases are contagious and that they unfold from plant to plant simply. The diseases inflicting substantial yield loss in potato are Phytophthora infestans (late blight) and Alternaria solani (early blight). Early detection of those diseases will permit preventive measures and mitigate economic and production losses[4]. Over the last decades, the foremost practiced approach for detection and identification of disease is optic observation by consultants. however in several cases, this approach proves impracticable to the excessive time interval and inaccessibility of consultants at farms settled in remote areas [5]. Dhakate,M., & Ingole A. B. (2015). Used neural network for identification of the pomegranate plant diseases.

PROPOSED WORK

Implementation and analysis of algorithms were administered based on the use of standard ROI (ROI), reduced boundary (RROI) and planned EROI segmentation processes, 2) the consistency of the extracted characteristics from every ROI through malady identification and sample size preparation, 3) the effectuality of every ROI segmentation using GLCM in yielding finer difference characteristics increased classification potency through means that of classification output.Image dataset The dataset consisted of (early and late blight) potato and pomegranate plant healthy and unhealthy photos, a part of the intensive PlantVillage dataset with a set of already reported and divided (early) leaf footage.Firstly the image acquisition,image improvement technique and segmentation is performed on the leaf to enhance its affected region and to eliminate the noise from the provided image. Finally, some pictures are accustomed to train the neural network and different pictures are used as test pictures to examine the accuracy from the results.The neural network is given with the epoch of 2000 to get better accuracy.And k-means clustering algorithm is used to fetch the required clusters from the neural networks.Fig three provides the diagram for testing within the planned system,step-by-step that starts with the test image, the process of the given image to spot it's sort of plant. And from that, the clustering-based segmentation takes place that then is carried to get the feature extraction using an image process and therefore the ROI of the leaf will be obtained.

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FEATURE EXTRACTION

Feature extraction may be a vital and essential step to extract regions of interest. In our planned methodology the fundamental options are mean, variance, entropy, IDM, RMS, variance, smoothness,skewness, kurtosis, contrast, correlation, energy and homogeneity are calculated and thought of as options values. Then we've created the feature vector for these values. The divided methodology shows totally different values for pictures using Gray Level CO-Occurrence Matrix(GLCM). The Mathematical formulas for the feature extraction are used to calculate the accuracy of the images.[4]

CLASSIFICATION

A Convolutional Neural Network (CNN) is an science paradigm that's galvanized by the means biological nervous systems, like the brain, process data. The key component of this paradigm is that the novel structure of the data process system. It's composed of an oversized variety of extremely interconnected process elements(neurons) operating in unison to resolve specific issues. NNs, like individuals, learn by example. an NN is organized for a particular application, like pattern recognition or information classification, through a learning method. Learning in biological systems involves changes to the conjunction connections that exist between the neurons. within the existing methodology, the shift rule is employed for segmentation and therefore the fuzzy cluster rule is employed for plant disease classification. the prevailing methodology provides Low accuracy. it's not suited to search out the sort of malady during a leaf. Within the planned methodology K-Means technique is employed for leaf segmentation and multiclass KNN is applied to search out the sort of malady in leaf so as to extend the accuracy and cut back the complexness.

ROLE OF CLASSIFICATION

The picture processing system was used to diagnose five pathogens, including early blight,late blight in potato leaves and alternia , anthracnose and bacterial blight affected pomegranate leaf. The K-means were used to cluster photos of the diseased leaf. The clustered photos were then transferred into a classifier NN. The outcome was that the NN classifier was much more accurate . This approach leads greatly to the precise and automated diagnosis of leaf diseases.

RESULTS AND DISCUSSION

The prediction of diseases of both plants using K-means and neural networks is provided in a graph with the correct accuracy obtained using the hybrid algorithms.That are much more likely to provide higher accuracy.

CONCLUSION

A new extended automatic area of hybrid algorithm has been enforced. The results indicated the excellence of the projected k-mean cluster for automatic segmentation of unhealthy symptom regions exhibiting the anatomy element, therefore enhancing its favoured use in catching the mandatory border regions, tailored for finer extraction of difference options with neural network. The present rule, with the utilization of GLCM that implements specific color homogeneity by threshold and morphology, is easy to implement as a part of an entire disease detection system. The k-means clustering algorithm is used in classification of the diseases of both potato and pomegranate plants.And neural network algorithm is used to predict the diseases of both the plants.The epoch of 2000 is given to obtain much more accuracy of disease detection in plants.

REFERENCES

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