IDENTIFICATION OF PLANT NUTRIENT DEFICIENCIES USING CNN

Registration ID: IJNRD_220030

Published ID: IJNRD2404874

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M KAPILESHWAR , md imran , p ajay , md amjad pasha

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

Published Paper   E-Certificate

M KAPILESHWAR, md imran , p ajay, & md amjad pasha (April-2024). IDENTIFICATION OF PLANT NUTRIENT DEFICIENCIES USING CNN. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), i651-i656. https://ijnrd.org/papers/IJNRD2404874.pdf

Volume 9 Issue 4, April-2024

Pages : i651-i656

Paper Reg. ID: IJNRD_220030

Published Paper Id: IJNRD2404874

Downloads: 000121985

Research Area: Computer Science & Technology 

Country: hanamkonda , wgl, TELANGANA, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404874.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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