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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | IDENTIFICATION OF PLANT NUTRIENT DEFICIENCIES USING CNN |
| Authors Name: | M KAPILESHWAR , md imran , p ajay , md amjad pasha |
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IJNRD_220030
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| Published Paper Id: | IJNRD2404874 |
| Published In: | Volume 9 Issue 4, April-2024 |
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| Abstract: | A novel image analysis method for identifying nutrient deficiencies in plant based on its leaf is proposed. First, the proposed method divides an input leaf image into small blocks. Second, each block of leaf pixels is fed to a set of convolutional neural networks (CNNs). Each CNN is specifically trained for a nutrient deficiency and is utilized to decide if a block is presenting any symptom of the corresponding nutrient deficiency. Next, the responses from all CNNs are integrated to produce a single response for the block using a winner-take-all strategy. Finally, the responses from all blocks are integrated into one using a multi-layer perceptron to produce a final response for the whole leaf. Validation of the proposed method was performed on a set of black gram (Vigna mungo) plants grown under nutrient controlled environments. Five types of deficiencies, i.e., Ca, Fe, K, Mg, and N deficiencies, and a group of plants with complete nutrients were studied. A dataset consisting of 3,000 leaf images was collected and used for experimentation. Experimental results indicate the superiority of the proposed method over trained humans in nutrient deficiency identification This experiment sought to analyse the effects of certain nutrient deficiencies over a four week span of growth. It was discovered that nitrogen deficiency, phosphorus deficiency, and complete nutrient deficiency all led to significant differences in growth capacity over the span of the experiment. Standard chlorophyll content also showed significant differences when comparing the nitrogen-deficient and phosphorus deficient treatments with the complete nutrient trMeatment. These results indicated that mobility of nutrients cannot completely offset a deficiency of said nutrient in the environment it can only help the plant attempt to survive the deficiency Keywords - nutrient deficiency leaf, image analysis, machine learning, CNN |
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| Cite Article: | "IDENTIFICATION OF PLANT NUTRIENT DEFICIENCIES USING CNN", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.i651-i656, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404874.pdf |
| Downloads: | 00037 |
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404874 Registration ID: 220030 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: i651-i656 Country: hanamkonda , wgl, TELANGANA, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404874 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404874 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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