Development of a Multimodal Deep Learning Framework for Crop Disease Detection

Abstract

Agricultural crop diseases can greatly reduce production quality and overall farm output, making early identification important for sustainable farming. This study introduces a smart agricultural rover that applies a multimodal deep learning approach for real-time crop disease monitoring in field environments. The proposed system gathers RGB images, thermal information, and environmental measurements such as temperature, humidity, and soil moisture through integrated sensors connected to a Raspberry Pi 4. For on-device analysis, a lightweight TensorFlow Lite (TFLite) model is utilized to classify crop diseases efficiently at the edge. To improve detection performance under different illumination conditions, the system evaluates both original and CLAHE-enhanced images using a dualinference mechanism supported by entropy and confidence-based decision metrics. The rover is implemented on a mobile robotic platform equipped with motor control and battery support to enable autonomous movement in agricultural fields. By combining sensor fusion, edge intelligence, and robotic mobility, the developed system supports accurate identification of diseases such as Powdery Mildew and Rust, helping farmers take preventive action and improve crop management practices

Plant phenotyping relevance

RGB・熱画像とセンサ融合、エッジ推論、画像強調による作物病害検出システムを開発しており、植物の病害状態を推定する方法が中心である。

Code and data availability

The article describes a multimodal crop-disease detection rover (RGB + thermal, TFLite on Raspberry Pi) but contains no data availability statement, no public dataset, no deposited images or sensor data, and no author code/model release with a public URL. The TFLite model is only described as a pre-trained 'Microsoft'-

No evidence-backed public reproduction asset is currently recorded.