An Effect of Nutrient Deficiency on Yield Estimation
Sushila Shidnal1, Mrityunjaya V Latte2
1Sushila Shidnal, Assistant Professor, Department of Computer Science & Engineerng, Sir M Visvesvaraya Institute of Technology, Bangalore, 562157 India.
2Mrityunjaya V Latte, Principal, JSS Academy of Technical Education, Bangalore, 560060 India.
Manuscript received on 10 September 2022. | Revised Manuscript received on 15 September 2022. | Manuscript published on 30 September 2022. | PP:7-18 | Volume-8 Issue-3, September 2019 | Retrieval Number: C3857098319/19©BEIESP | DOI: 10.35940/ijrte.C3857.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: By taking corrective measures to improve the farming quality, agricultural sector need a thoroughly explained and systematic theory for crop yield prediction. Any yield of the crop is usually depending on the crop unhealthy and healthy conditions. These conditions mainly occur due to major nutrients like nitrogen, Phosphorus and Potassium (NPK). Nitrogen deficiency will make the fields in some parts look Yellowish. Potassium deficiency may lead to have spots in the leaf and Phosphorous will make the fields some part look brownish. Hence segmenting this defected area is the major challenge to evaluate the total yield in the input paddy field image. The proposed model focus on segmentation of these regions using an efficient hierarchical model. This model uses segmentation methods like FCM and Color segmentation techniques there by improving the accuracy of the system and comparing with the ground truth values.
Keywords: Fuzzy C Means (FCM), GLCM, HSV Histogram, Hierarchical Colour Based Segmentation, NPK Regions.
Scope of the Article: Fuzzy Logics