ICTACT Journal on Image and Video Processing

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A Review on Diagnosis of Nutrient Deficiency Symptoms in Plant Leaf Image Using Digital Image Processing


Affiliations
1 Department of Computer Science, Guru Nanak College, India
2 Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, India
     

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Plants, for their growth and survival, need 13 mineral nutrients. Toxicity or deficiency in any one or more of these nutrients affects the growth of plant and may even cause the destruction of the plant. Hence, a constant monitoring system for tracking the nutrient status in plants becomes essential for increase in production as well as quality of yield. A diagnostic system using digital image processing would diagnose the deficiency symptoms much earlier than human eyes could recognize. This will enable the farmers to adopt appropriate remedial action in time. This paper focuses on the review of work using image processing techniques for diagnosing nutrient deficiency in plants.

Keywords

Color Segmentation, Color Space, Mathematical Morphology, Color Feature Extraction, Classifier.
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  • Sanjay B. Patil and Shrikant K. Bodhe, “Betel Leaf Area Measurement using Image Processing”, International Journal on Computer Science and Engineering, Vol. 3, No. 7, pp. 2656-2660, 2011.

  • Parviz Ahmadi Moghaddam, Mohammadali Haddad Derafshi and Vine Shirzad, “Estimation of Single Leaf Chlorophyll Content in Sugar Beet using Machine Vision”, Academic Journals, Vol. 35, No. 6, pp. 563-568, 2011.

  • Jayamala K. Patil and Raj Kumar, “Color Feature Extraction of Tomato Leaf Diseases”, International Journal of Engineering Trends and Technology, Vol. 2, No. 2, pp. 72-74, 2011.

  • Juan-hua Zhu, Ang Wu and Peng Li, “Corn Leaf Diseases Diagnostic Techniques Based on Image Recognition”, Communications and Information Processing, pp. 334-341, 2012.

  • Youwen Tian, Lide Wang and Qiuying Zhou, “Grading Method of Crop Disease based on Image Processing”, Proceedings of International Conference on Computer and Computing Technologies in Agriculture, pp. 427-433, 2012.

  • Sanjay B. Patil and Shrikant K. Bodhe, “Leaf Disease Severity Measurement using Image Processing”, International Journal of Engineering and Technology, Vol. 3, No. 5, pp. 297-301, 2011.

  • Sanjay B. Dhaygude and Nitin P. Khumbhar, “Agriculture Plant Leaf Disease Detection using Image Processing”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, No. 1, pp. 599-602, 2013.

  • Piyush Chaudhary, Anand K. Chaudhari, A.N. Cheeran and Sharda Godara, “Color Transform Based Approach for Disease Spot Detection on Plant Leaf”, International Journal of Computer Science and Telecommunications, Vol. 3, No. 6, pp. 65-70, 2012.

  • Sanjeev S Sannakki, Vijay S Rajpurohit and Sagar J Birje, “Comparison of Different Leaf Edge Detection Algorithms using Fuzzy Mathematical Morphology”, International Journal of Innovations in Engineering and Technology, Vol. 1, No. 2, pp. 15-21, 2012.

  • S.S. Patil, B.V. Dhandra, U.B. Angadi, A.G. Shankar and Neena Joshi, “Web based Expert System for Diagnosis of Micro Nutrients Deficiencies in Crops”, Proceedings of the World Congress on Engineering and Computer Science, Vol. 1, pp. 1-3, 2009.

  • Zhichen Li, Changying Ji and Jicheng Liu, “Leaf Area Calculating based on Digital Image”, Proceedings of International Conference on Computer and Computing Technologies in Agriculture, pp. 1427-1433, 2008.

  • Biasong Chen, Zhou Fu, Yuchun Pan, Jihua Wang and Zhixuan Zeng, “Single Leaf Measurement using Digital Camera Image”, Proceedings of International Conference on Computer and Computing Technologies in Agriculture, pp. 525-530, 2011.

  • Sanjay B. Patil and Shrikant K. Bodhe, “Image Processing Method to Measure Sugarcane Leaf Area”, International Journal of Engineering Science and Technology, Vol. 3, No. 8, pp. 6394-6400, 2011.

  • Marlon Marcon, Kleber Mariano, Roberto A. Braga, Carlos M. Paglis, Myriane S, Scalco and Graham W. Horgan, “Estimation of Total Leaf Area in Perennial Plants using Image Analysis”, Revista Brasileira de Engenharia Agricola e Ambiental, Vol. 15, No. 1, pp. 96-101, 2011.

  • Jun Sun, Hanping Mao, Yiquing Yang, “The Research on the Judgement of Paddy Rice’s Nitrogen Deficiency Based on Image”, Proceedings of International Conference on Computer and Computing Technologies in Agriculture, pp. 1049-1054, 2008.

  • Swapnil S Ayane, M.A. Khan and S.M. Agrawal, “Identification of Nitrogen Deficiency in Cotton Plant by using Image Processing”, International Journal of Pure and Applied Research in Engineering and Technology, Vol. 1, No. 8, pp. 112-118, 2013.

  • Piti Auearunyawat, Teerasi Kasetkasem, Audthasit Wongmaneeroj, Akhinori Nishihara and Rachaporn Keinprasit, “An Automatic Nitrogen Estimation Method in Sugarcane Leaves using Image Processing Techniques”, Proceedings of International Conference on Agricultural, Environment and Biological Sciences, pp. 39-42, 2012.

  • Luis M. Contreras-Media, Roque A. Osornio-Rios, Irineo Torres-Pacheco, Rene de J. Romero-Troncoso, Ramon G. Guevara-Gonzalez and Jesus R. Millan-Almaraz, “Smart Sensor for Real-Time Quantification of Common Symptoms Present in Unhealthy Plants”, Sensors, Vol. 12, No. 1, pp. 784-805, 2012.

  • Fan Zhang and Xinhong Zhang. “Classification and Quality Evaluation of Tobacco Leaves Based on Image Processing and Fuzzy Comprehensive Evaluation”, Sensors, Vol. 11, No. 3, pp. 2369-2384, 2011.

  • Charles A. Price, Olga Symonova, Yuriy Mileyko, Troy Hilley, and Joshua S. Weitz, “Leaf Extraction and Analysis Framework Graphical User Interface: Segmenting and Analyzing the Structure of Leaf Veins and Areoles”, Plant Physiology, Vol. 155, pp. 236-245, 2011.

  • H. Muhammed Asraf, Nooritawati Md Tahir, S.B. Shah Rizam, R. Abdullah, “Elaeis Nutritional Lacking Identification based Statistical Analysis And Artificial Neural Network”, Proceedings of Recent Advances in Systems Science and Mathematical Modeling, pp. 144-149, 2012.

  • S. Arivazhagan, R. Newlin Shebiah, S. Ananthi and S. Vishnu Varthini, “Detection of Unhealthy Region of Plant Leaves and Classification of Plant Diseases using Texture Features”, Agricultural Engineering International: CIGR Journal, Vol. 15, No. 1, pp. 211-217, 2013.

  • Sathish Madhogaria, Marek Schikora, Wolfgang Koch and Daniel Cremers. “Pixel Based Classification Method for Detecting Unhealthy Regions in Leaf Images”, Proceedings of Informatik schafft Communities, pp. 1-11, 2011.

  • Shitala Prasad, Piyush Kumar, and Anuj Jain, “Detection of Disease using Block-Based Unsupervised Natural Plant Leaf Color Image Segmentation”, Proceedings of International Conference on Swarm, Evolutionary, and Memetic Computing, pp. 399-406, 2011.

  • J.K. Patil and Raj Kumar, “Feature Extraction of Diseased Leaf Images”, Journal of Signal and Image Processing, Vol. 3, No. 1, pp. 60-63, 2012.

  • Yan Li, Zheru Chi and David D. Feng, “Leaf Vein Extraction using Independent Component Analysis”, IEEE Conference on Systems, Man, and Cybernetics, pp. 3890-3894, 2006.

  • X. Zheng and X. Wang, “Leaf Vein Extraction based on Gray-scale Morphology”, International Journal of Image, Graphics and Signal Processing, Vol. 2, pp. 25-31, 2010.

  • V. Katyal and Aviral, “Leaf Vein Segmentation using Odd Gabor Filters and Morphological Operations”, International Journal of Advanced Research in Computer Science, Vol. 3, No. 3, pp. 1-5, 2012.

  • James Clarke, Sarah Barman, Paolo Remagnino, Ken Bailey, Don Kirkup, Simon Mayo and Paul Wilkin, “Venation Pattern Analysis of Leaf Images”, Proceedings of International Symposium on Visual Computing, pp. 427-436, 2006.

  • N. Vinushree, B. Hemalatha and Vishnu Kumar Kaliappan, “Efficient Kernal-based Fuzzy C- Means Clustering for Pest Detection and Classification”, Proceedings of World Congress on Computing and Communication Technologies, pp. 179-181, 2014.

  • V.K. Tewari, Ashok Kumar Arudra, Satya Prakash Kumar, Vishal Pandey and Narendra Singh Chandel, “Estimation of Plant Nitrogen content using Digital Image Processing”, Agricultural Engineering International: CIGR Journal, Vol. 15, No. 2, pp. 78-86, 2013.

  • Sumeet S. Nisale, Chandan J. Bharambe and Vidva N. More, “Detection and Analysis of Deficiencies in Groundnut Plant using Geometric Moments, Proceedings of World Academy of Science, Engineering and Technology, Vol. 5, pp. 512-516, 2011.

  • M. Viraj A. Gulhane and Ajay A. Gurjar, “Detection of Diseases on Cotton Leaves and Its Possible Diagnosis”, International Journal of Image Processing, Vol. 5, No. 5, pp. 590-598, 2011.

  • K. Dang, H. Sun, Jean-Pierre Chanet, J. Gracia-Vidal, J.M. Barcelo-Ordinas, H.L Shi and K.M. Hou, “Wireless Multimedia Sensor Network for Plant Disease Detections”, Proceedings of New Information Communication Science and Technology for Sustainable Development: France-China International Workshop, pp. 1-6, 2013.

  • Keyvan Asefpour Vakilian and Jafar Massah, “Non-linear Growth Modelling of Greenhouse crops with Image Textural Features Analysis”, International Research Journal of Applied and Basic Sciences, Vol. 3, No. 1, pp. 197-202, 2012.

  • Ashish Miyatra and Sheetal Solanki, “Disease and Nutrient Deficiency Detection in Cotton Plant”, International Journal of Engineering Development and Research, Vol. 2, No. 2, pp. 2801-2804, 2014.

  • Vasudev B. Sunagar, Pradeep A. Kattimani, Vimala A. Padasali and Neetha V. Hiremath, “Estimation of Nitrogen Content in Leaves using Image Processing”, Proceedings of International Conference on Advances in Engineering and Technology, pp. 25-28, 2014.

  • Manoj Mukherjee, Titan Pal and Debabrata Samanta, “Damaged Paddy Leaf Detection using Image Processing”, Journal of Global Research in Computer Science, Vol. 3, No. 10, pp. 7-10, 2012.

  • Marek Schikora, Adam Schikora, Karl-Heinz Kogel, Wolgang Koch and Daniel Cremers, “Probabilistic Classification of Disease Symptoms caused by Salmonella on Arabidopsis Plants”, GI Jahrestagung Journal, Vol. 2, No. 10, pp. 874-879, 2010.

  • Y. Sanjana, Ashwath Sivasamy and SriJayanth, “Plant Disease Detection using Image Processing Techniques”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 4, pp. 295-301, 2015.

  • V. Surendrababu, C.P. Sumathi and E. Umapathy, “Detection of Rice Leaf Diseases using Chaos and Fractal Dimension in Image Processing”, International Journal on Computer Science and Engineering, Vol. 6, No. 1, pp. 69-74, 2014.

  • Monica G. Larese, Rafael Namias, Roque M. Craviotto, Miram R. Arango, Carina Gallo, Pablo M. Granitto, “Automatic Classification of Legumes using Leaf Vein Image Features”, Pattern Recognition, Vol. 47, No. 1, pp. 158-168, 2014.

  • Guili Xu, Fengling Zhang, Syed Ghafoor Shah, Yongqiang Ye and Hanping Mao, “Use of Leaf Color Images to Identify Nitrogen and Potassium Deficient Tomatoes”, Pattern Recognition Letters, Vol. 32, No. 11, pp. 1584-1590, 2011.

  • Jianlun Wang, Jianlei He, Yu Han, Chanqui Ouyang and Daoliang Li, “An Adaptive Thresholding Algorithm of Field Leaf Image”, Computers and Electronics in Agriculture, Vol. 96, pp. 23-39, 2013.

  • Ji-Xiang Du, Chuan-Min Zhai and Quing-Ping Wang, “Recognition of Plant Leaf Image based on Fractal Dimension Features”, Neurocomputing, Vol. 116, pp. 150-156, 2013.

  • Jin Kyu Park, Een Jun Hwang and Yunyoung Nam, “Utilizing Venation Features for Efficient Leaf Image Retrieval”, Journal of Systems and Software, Vol. 81, No. 1, pp. 71-82, 2008.

  • H. Muhammad Asraf, M.T. Nooritawati and M.S.B. Shah Rizam, “A comparative Study in Kernal-based Support Vector Machine of Oil Palm Leaves Nutrient Disease”, Procedia Engineering, Vol. 41, pp. 1353-1359, 2012.

  • K. Thangadurai and K. Padmavathy, “Computer Vision Image Enhancement for Plant Leaves Disease Detection”, Proceedings of World Congress on Computing and Communication Technologies, pp. 173-175, 2014.

  • Daisy Shergill, Akashdeep Rana and Harsimran Singh, “Extraction of Rice Disease using Image Processing”, International Journal of Engineering Sciences and Research Technology, Vol. 4, No. 6, pp. 135-143, 2015.

  • Basavaraj Tigadi and Bhavana Sharma, “Banana Plant Disease Detection and Grading using Image Processing”, International Journal of Engineering Science and Computing, Vol. 6, No. 6, pp. 6512-6516, 2016.

  • S. Sridevy and Anna Saro Vijendran, “An Evolving Expert System for Maize Plant Nutrient Deficiency using Image Processing Techniques”, International Journal of Science and Research, Vol. 5, No. 2, pp. 70-77, 2016.

  • Amruta Ambatkar, Ashwini Bhandekar, Avanti Tawale, Chetna Vairagade and Ketaki Kotamkar, “Leaf Disease Detection using Image Processing”, Proceedings of International Conference on Recent Trends in Engineering Science and Technology, Vol. 5, pp. 333 -336, 2017.


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  • A Review on Diagnosis of Nutrient Deficiency Symptoms in Plant Leaf Image Using Digital Image Processing

Abstract Views: 594  |  PDF Views: 9

Authors

S. Jeyalakshmi
Department of Computer Science, Guru Nanak College, India
R. Radha
Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, India

Abstract


Plants, for their growth and survival, need 13 mineral nutrients. Toxicity or deficiency in any one or more of these nutrients affects the growth of plant and may even cause the destruction of the plant. Hence, a constant monitoring system for tracking the nutrient status in plants becomes essential for increase in production as well as quality of yield. A diagnostic system using digital image processing would diagnose the deficiency symptoms much earlier than human eyes could recognize. This will enable the farmers to adopt appropriate remedial action in time. This paper focuses on the review of work using image processing techniques for diagnosing nutrient deficiency in plants.

Keywords


Color Segmentation, Color Space, Mathematical Morphology, Color Feature Extraction, Classifier.

References