International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 08 | Aug 2022
p-ISSN: 2395-0072
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DETECTION OF NUTRIENT DEFICIENCIES IN CROPS USING SUPPORT VECTOR MACHINE (SVM) 1
Mr. Vijay J. Kadam, 2 Prof. Dr. T.B. Mohite-Patil
M.Tech Student Department of Electronics and Telecommunication Engineering, D Y Patil College of Engineering & Technology Kolhapur, India 2 Associate Professor Department of Electronics and Telecommunication Engineering, D Y Patil College of Engineering & Technology, Kolhapur, India ---------------------------------------------------------------------***--------------------------------------------------------------------procedure, principles involved in image based detection, Abstract: In an agricultural country like India, farmers are 1
crop diseases studied.
facing a lot of problems in detecting the causes of diseases& deficiencies in plants. Once the causes are identified then remedies can be found to treat them. With naked-eye observation it is difficult to classify the deficiencies present in leaves of crops. Image processing algorithm can be used to build a model to detect various types of deficiencies in the leaves. The colour and texture features can be used to recognize and classify the deficiencies. The combinations of features can be proving to be very effective in deficiency detection. This proposed system presents an effective method for detection of nutrient deficiencies in leaves using colourtexture analysis and k-means clustering.
[2]T. Rajasekar, M. Arun Kumar, K. Mohamed Ismail, M. Sabarimuthu[2020][2] In this paper author proposed Automated Farming and Nutrition Deficiency Detection using Swarm Bots, automation of farming can be used to get divest of day-today farming hitches. To contribute an elucidation to these glitches, the steered rover for drilling, seed sowing, and detection of victual rift using Artificial Intelligent system. Recovery system has been offered to lessen the human exertion and to speed up the work, henceforth weakening the measure of equipment required for its usage without bargaining the nature of administration. Surveying the leaf using image processing the farmer can easily be notified about the deficiency in the crops through communication protocol.
Key Words:
Nutrient deficiency, Texture, Clustering algorithm, Image processing
1.INTRODUCTION In an agricultural country like India, farmers are facing a lot of problems in detecting the causes of diseases& deficiencies in plants. Once the causes are identified then remedies can be found to treat them. With naked-eye observation it is difficult to classify the deficiencies present in leaves of crops. Image processing algorithm can be used to build a model to detect various types of deficiencies in the leaves. The colour and texture features can be used to recognize and classify the deficiencies. The combinations of features can be prove to be very effective in deficiency detection. This proposed system presents an effective method for detection of nutrient deficiencies in leavesusing colour-texture analysis and kmeans clustering.
[3] Gaganjot Kaur -[2020][3] In this paper author proposed Automated Nutrient Deficiency Detection in Plants, Nutrient deficiency is one such factor included. Different frameworks using digital image processing, computer vision, IOT is used to analyze the deficiency side effects a lot sooner than natural eyes could perceive. This empowers the farmers to implement remedial activity in time. This paper concentrates on the review of different techniques for diagnosing nutrient deficiency in plants.
[4] Amirtha T, Gokulalakshmi T, Umamaheswari P, T Rajasekar [2020][4]
2. LITERATURE REVIEW
In this paper Machine Learning Based Nutrient Deficiency Detection in Crops, This paper aims at designing an automatic robotic vehicle which detects the nutrient deficiency in crops just by simply capturing the image of leaves of the crop plants. The captured image is then processed by using the convolutional neural networks (CNN). This technique uses captured image, processing it by comparing it with the already available dataset. When the input image is matched or partially matched with any one of the existing images in the dataset, it will provide the result
[1] A.K. Ghorai, S. Mukhopadhyay, S. Kundu, S. N. Mandal, A. Roy Barman, M. De Roy, S. Jash2 and S. Dutta. [2021][1] In this paper author discuss about Image Processing Based Detection of Diseases Plants. The different steps of image processing based detection such as image acquisition, image processing, segmentation, feature extraction and classification with a classifier are discussed. The detailed
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