Early Detection of Banana Leaf Disease Using Novel Deep Convolutional Neural Network

Abstract

One of the most widely grown commercial commodities in India is the banana tree, which has important cultural and gastronomic significance in tropical and subtropical areas where banana leaves are widely used for food delivery and packaging in a variety of cultures. Regrettably, the incidence of diverse ailments that damage banana leaves present a significant risk to total output, therefore having an instant effect on the country’s economy. To meet this issue, more efficient monitoring systems must be put in place, and control techniques for early illness and pest detection must be developed. Using pest indicators makes this proactive strategy easier. With the successful use of these approaches in a variety of industries, recent advances in agricultural technology have seen the incorporation of deep convolutional neural networks (DCNN) for disease identification in numerous crops. This study’s main goal is to put into practice a DCNN that is especially designed to anticipate various illnesses and pest occurrences in banana leaves. Through the use of DCNN, farmers may get vital insights to apply fertilizers sparingly during the early phases, hence preventing the advent of leaf diseases. Remarkably, the suggested approach, which uses a convolutional neural network (CNN) for accurate banana leaf disease detection, exhibits an astounding 99% accuracy when compared to other deep learning techniques. By offering a reliable and precise technique for predicting pest and disease in banana crops, this study advances agricultural practices. The use of state-of-the-art technologies, like CNN and DCNN, highlights the potential revolutionary influence on disease control in banana farming, promoting increased yield and sustainable farming methods. Received: 12 August 2023 | Revised: 20 May 2024 | Accepted: 11 August 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement N. R. Rajalakshmi: Conceptualization, Software, Investigation, Data curation, Writing - original draft. S. Saravanan: Conceptualization, Software, Investigation, Data curation, Writing - original draft. J. Arunpandian: Validation, Formal analysis. Sandeep Kumar Mathivanan: Methodology, Writing - review & editing, Supervision, Project administration. Prabhu Jayagopal: Software, Investigation, Resources. Saurav Mallik: Methodology, Writing - review & editing, Supervision, Project administration. Guimin Qin: Resources, Data curation, Visualization, Supervision, Project administration.

Plant phenotyping relevance

バナナ葉の病害状態を画像からCNNで推定する手法が研究の中心であり、植物病害の表現型判定に該当する。

Code and data availability

The paper's Data Availability Statement explicitly states no new data were created or analyzed, and no public dataset, code, model, or supplement URL is provided by the authors. The banana leaf images shown are sample figures, and all cited datasets belong to prior work.

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