Improving farming by quickly detecting muskmelon plant diseases using advanced ensemble learning and capsule networks

Abstract

In modern agriculture, ensuring plant health is essential for high crop yields and quality. Plant diseases pose risks to economies, communities, and the environment, making early and accurate diagnosis crucial. The internet of things (IoT) has revolutionized farming by enabling real-time crop monitoring and using drones and cameras for early disease detection. This technology helps farmers address challenges with precision and sustainability. This research propose an ensemble learning model incorporating multi-class capsule networks (MCCN) and other pre-trained model with majority voting system is implemented to predict plant diseases and pests early. The research aims to develop a robust MCCN-based ensemble prediction model for timely disease identification. To evaluate the performance of the ensemble model, various key metrics, including accuracy, and loss value, are assessed. Furthermore, a comparative analysis is conducted, benchmarking the MCCN model against other well-known pre-trained models such as residual network-101 (ResNet101), visual geometry group-19 (VGG19), and GoogleNet. This research signifies a substantial stride towards the realization of IoT-driven precision agriculture, where advanced technology and machine learning contribute to the early detection and mitigation of plant diseases, ultimately enhancing crop yield and environmental sustainability.

Plant phenotyping relevance

植物病害を画像等から識別するアンサンブル学習モデルの開発・比較が中心であり、罹病状態という植物表現型を推定する方法研究に該当する。

Code and data availability

The paper describes a custom muskmelon leaf disease image dataset and an MCCN ensemble model, but provides no public dataset, code, or model deposit. The DATA AVAILABILITY statement explicitly says no new data were created or analyzed, and no author URL or repository for assets is given.

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