Efficient-Integrated Classification Model For Blackgram Disease Detection

Nadakuditi Swarna Jyothi; Raavi Satya Prasad

doi:10.69980/redvet.v25i2.2038

Nadakuditi Swarna Jyothi
Raavi Satya Prasad

DOI: https://doi.org/10.69980/redvet.v25i2.2038

Keywords: Blackgram Diseases, EfficientNetV2-L (Large), Vision Transformers (ViT), Faster R- Convolutional Neural Networks (CNNs).

Abstract

Blackgram (Vigna mungo L.), a major leguminous crop, is susceptible to various diseases that can cause significant losses in yield and quality. Disease prediction plays a vital role in the timely and accurate diagnosis of black diseases and in developing proper disease management plans. In this context, the EfficientNetV2-L (Large) is used as a training model to work on the Blackgram diseases dataset. This study focuses on adopting ensemble learning techniques to develop robust prediction models for Blackgram disease intelligence. The proposed Efficient-Integrated method combines the predictions of multiple base models like Vision Transformers (ViT) and Faster R- Convolutional Neural Networks (CNNs) extracted into various channels to improve the models' accuracy in terms of classification while reducing the data's variability. The proposed model improved the performance by adopting the Explainable Artificial Intelligence (XAI) and Grad-CAM (Gradient-weighted Class Activation Mapping) to increase the image classification rate (accuracy) and abnormal regions detection, including plant disease identification, such as in black gram (a type of legume). The dataset collected from several online sources is applied to identify critical predictors. Experiments validate that the proposed ensemble method consistently beats single-model baselines regarding precision, recall, and robustness. Moreover, the framework helps experts make significant decisions by enabling them to understand the major drivers of the disease outbreak. This paper highlights the potential for ensemble learning to transform crop disease prediction and contribute to sustainable agricultural practices.

Author Biographies

Nadakuditi Swarna Jyothi

Research Scholar, Acharya Nagarjuna University, Guntur, AP, India.

Lecturer in Computer Science, Govt. Degree College, Avanigadda, Krishna District, Andhra Pradesh-521121.

Raavi Satya Prasad

Professor and Dean R & D, Department of Computer Science & Engineering, Dhanekula Institute of Engineering & Technology, Ganguru, Vijayawada, A.P., India

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