Search

Detecting diseases in apple leaves accurately and efficiently is vital for maintaining healthy crops and ensuring optimal yield. This paper introduces a novel approach that integrates Neutrosophic Logic with the EfficientNetB0 model to enhance the classification of apple leaf diseases. The proposed method significantly improves precision, recall, and F1-scores across multiple disease classes, demonstrating its robustness and effectiveness compared to traditional techniques. [ABSTRACT FROM AUTHOR]

A fuzzy set is a powerful tool to handle uncertainty and ambiguity, and generally, the notions of symmetry and similarity are also exhibited in the fuzzy set theory. The class of (m, n)-rung orthopair fuzzy sets through two universes are more flexible and efficient than the q-rung orthopair fuzzy sets when discussing the symmetry and similarity between multiple objects. This research article comprehensively investigates ten similarity measures that employ cosine and cotangent functions for comparing (m, n)-rung orthopair fuzzy sets, which are a superclass of q-rung orthopair fuzzy sets. Moreover, the proposed weighted similarity measures are applied to real-world problems in building material analysis. A comparative analysis is conducted between the proposed measures and the existing cosine and cotangent measures of q-rung orthopair fuzzy sets, showing that the proposed measures are more efficient than existing ones. Additionally, a numerical example demonstrates the practical and scientific applications of these similarity measures in classifying plant leaf diseases. The sensitivity analysis shows that the existing measures cannot be applied to (m, n)-fuzzy data for distinct values of m and n. The results are supported by graphical interpretations, further illustrating the efficacy of the proposed measures. [ABSTRACT FROM AUTHOR]

Disease detection is basically a principal aspect in ameliorating agricultural production. The presented research concentrates on devising Plant Leaf Disease (PLD) detection together with an identification process intended for larger fields of crop production. Here, an inclusive study on disease recognition together with the classification of plant leaves utilizing Image Processing (IP) methods is performed. Since this technique is unpredictable and inconsistent, the customary manual visual quality examination can't be systematically stated. Furthermore, an extraordinary quantity of expertise is involved in plant disease diagnostics as well as the inconsistent processing times. Therefore, IP is implemented for plant disease recognition. Next, an imperative role is played by the Deep Learning (DL) together with Machine Learning (ML) classifiers in leaf disease classification. Centered upon an assessment of the formerly recommended topnotch techniques, a comprehensive discussion on disease detection together with classification performance is given. Lastly, the challenges and also some prospects for future ameliorations are discussed as well as classified. [ABSTRACT FROM AUTHOR]