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An Image Processing-Based Statistical Method for Estimating Nutrient Deficiencies in Grape Plants During the Growing Season

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Argitaratua izan da:Informatica vol. 49, no. 1 (Mar 2025), p. 105
Egile nagusia: Macriga, G Adiline
Beste egile batzuk: Subbiah, Sankari, Sudha, G, Saranya, S
Argitaratua:
Slovenian Society Informatika / Slovensko drustvo Informatika
Gaiak:
Tamil Nadu India
India
Software
Digital cameras
Agricultural production
Regression analysis
Nitrogen
Nutrition
Measurement techniques
Raw materials
Potassium
Agriculture
Image processing
Statistical analysis
Production costs
Phosphorus
Farmers
Grapes
Analytical chemistry
Flowering
Pesticides
Farms
Fertilizers
Statistical methods
Pruning
Nutrients
International trade
Chlorophyll
Estimation
Image processing systems
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Laburpena:Agriculture plays a very important role in the provision of food surplus to expanding population, contribution to capital formation, providing raw material to industries, market for industrial products and major contribution in international trade. To enhance the quality and quantity of the agriculture product there is a need to adopt the new technology. Image processing approach is non-invasive technique which provides consistent, reasonably accurate, less time consuming and cost-effective solution for farmers to manage fertilizers and pesticides. The objective of this study is to analyze the nutrition of grape plant using Wavelength algorithmand statistical method regression analysis is used in our work for the nutrient estimation. The relative requirements of nitrogen, phosphorus and potassium in grapes vary with the growth stages of grapes. There is a high-level requirement of N during the vegetative growth stage, P requirement is high during flowering stage and K requirement is more in crop maturity stage. So pruning was carried out in two stages i.e., in April and in October. This study is carried out in grape farms of Theni District of Tamil Nadu for estimating the macro nutrition nitrogen (N), phosphorus (P) and potassium (K) to analyze the yield. The results showed that the overall identification accuracies ofNPK deficiencies were 86.15, 87.69, 90.00 and 89.23% for the two pruning stages.
ISSN:0350-5596
1854-3871
DOI:10.31449/inf.v49il.5500
Baliabidea:Advanced Technologies & Aerospace Database