International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
www.irjet.net
Assessment of Chlorophyll and Nitrogen Contents of Leaves Using Image Processing Technique Shradha Sahurkar1, Prof. B. J.Chilke 2 Student (M.Tech), Dept. Of Electronics and comm. Engg, SDCOE ,Wardha, Nagpur University, Maharashtra 2 Assistant Professor (M.Tech), Dept. Of Electronics and comm. Engg, SDCOE ,Wardha, Nagpur University, Maharashtra ---------------------------------------------------------------------***--------------------------------------------------------------------1
Abstract – Leaf color has been commonly used as an index
for crop stress status diagnosis. Leaf colour is usually used as a guide for appraisal of nutrient status and plant health and so to determine nitrogen and chlorophyll contents also. Chlorophyll and Nitrogen are dependent on each other. Assessment of one will detect content of other. Many methods are developed to find these two. These approaches are of two categories- Destructive and Non destructive. However, Image processing technique is proving to be proficient among all these; which come under non destructive method. We have developed a low-cost and nondestructive method that is easy to use to assess the health status of plants, based on the estimation of chlorophyll and nitrogen content of leaves using a portable digital camera..We also proposing a new algorithm with good efficiency.
Key Words: Digital image processing; k -means clustering; GLCM; Nitrogen; Chlorophyll; Support vector machine.
1. INTRODUCTION The main tenure of India is agriculture, Indian soil is comprise of many minerals and organic elements, and inspection has resolved that soil and SVM classifier. All plants require adequate supplies of macronutrients for healthy growth, and nitrogen is a nutrient that is heavily available in Indian agricultural soil and which should not be in restricted supply. It is manual and time devastating. Plants, like all other living things, need food for their growth and development. Plants demand 16 fundamental elements. Carbon, hydrogen, and oxygen are derived from the atmosphere and soil water. The remaining 13 fundamental elements (nitrogen, iron, phosphorus, calcium, magnesium, sulphur, zinc, manganese, potassium, copper, boron, molybdenum, and chlorine) are supplied either from soil minerals and soil organic matter or by organic or inorganic fertilizers. Nitrogen (N) is a major element for plant growth and is a radical part of chlorophyll (Ch), which is primary absorber of light energy needed for photosynthesis. Ch and N affects the green color of plants and ultimately determines their biomass yield and quality. Plants adequately supplied with N are green and healthy, while plants inadequately supplied with N are pale green or yellow in color and remain small and retarded. Hence, leaf color changes have led Š 2017, IRJET
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researchers to exploit this property by using image processing analyses to detect Ch and N status in plants if there is deficiency in the content then proper measures can be taken by farmers to improve the nutrients in crops. Thus it will be helpful in guiding the need of type and the amount of the pesticide which will be very helpful in agriculture industry. Digital image processing is superior to manual process hence we will be able to save time and human error. Computer algorithms are used for texture analysis. Digital image processing is superior to manual process hence we will be able to save time and human error. The quality and quantity of crop yields are related to its nutritional availability. Over-fertilization cause environmental issues while under-fertilization cause yield reduction and poor yield quality. Various image processing tools and approaches were widely used in order to identify and detect various contents in plant leaves. The aim of this paper is to help farmers in predicting the exact value of nitrogen and chlorophyll content of leaves using support vector machine classifier so as to increase the efficiency and prediction accuracy in comparison with the other approaches.
2. RELATED WORK Mr. Dalgade Viren Suryakant [1] had estimated the nitrogen content by evaluating the nitrogen deficiency in pomegranate leaves. They collect different Nitrogen deficient leaves and estimated the chlorophyll content of the collected leaves. They captured the images of collected leaves under the closed environment. These leaves are sent to the chemical analysis for the nitrogen estimation. The captured images are compared with database and then calculated the nitrogen deficiency of leaf. For irrigated crops, plant analysis can be used as an option in Extracting the statistical features of images and creating the database. The purpose of this study [2] is to estimate N of paddy build on leaf reflectance using Artificial Neural Network (ANN). In this study, 45 leaf samples were randomly selected under various environmental conditions. Leaf reflectance was measured by handheld spectrogram diameter while actual leaf N content was determined by Kjeldahl method. Spectral reflectance data in visible band (400�700 nm wavelength region) and actual N content were used as input and target data in ANN model building. K-fold cross validation (k=3) method was applied to select the best model and measure ISO 9001:2008 Certified Journal
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