Multi-Nutrient Deficiency Identification via Improved LinkNet-SqueezeNet Model and Improved BIRCH based Segmentation
Keywords:
Multi-Nutrient Deficiency Identification, Paddy leaf, I-BIRCH, Improved LinkNet, and MRE-LBP based feature.Abstract
Multi-nutrient deficiencies in rice paddy plant leaves including nitrogen, phosphorus, and potassium shortages, manifest symptoms like discolouration and stunted growth. Conventional identification methods, relying on visual inspection, are time-consuming, subjective, and prone to errors. Advanced models via Machine Learning (ML) and Deep Learning (DL) offer higher accuracy and efficiency but require significant computational resources and high-quality training data. This research proposed a new Multi-Nutrient Deficiency Identification model via Improved LinkNet and SqueezeNet (ILink-SqueezeNet). The model follows a structured methodology that includes preprocessing, segmentation, feature extraction, and identification. At first, the input paddy leaf image undergoes enhancement through Gaussian filtering to refine its quality. The preprocessed image is then segmented using an Improved Balanced Iterative Reducing and Clustering using Hierarchies (I-BIRCH) method. After segmentation, the crucial features like color features, Hierarchy of skeleton-based features and improved MRE-LBP-based features are retrieved from the segmented image. These features are then analysed by the ILink-SqueezeNet model, which identifies nutrient deficiencies in the paddy leaf. Eventually, comprehensive simulations and experimental calculations are shown to assess and validate the efficiency and robustness of the proposed ILink-SqueezeNet model. Therefore, the research outperforms the potential of the ILink-SqueezeNet model to expressively advance nutrient deficiency detection in agricultural applications.
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