Multimodal Deep Learning for Tapioca Yield Prediction Using Field-Collected Sensor and Imagery Data

Abstract

Tapioca is a vital root crop whose yield is influenced by multiple environmental, soil, and physiological factors.Traditional yield estimation methods depend upon statistical models and manual sampling, which often lack real-time accuracy and are time-consuming and labour-intensive.The combination of Internet of Things (IoT) sensor networks and Unmanned Aerial Vehicle (UAV)/satellite imagery, with deep learning (DL) models, gives a promising solution for accurate real-time yield prediction.In this study, sensor data (soil moisture, weather parameters, pH, NPK levels, temperature, and leaf chlorophyll content) are collected using devices like Davis Vantage Pro2, Decagon 5TE and SPAD-502, while image data are captured using DJI Phantom 4 Multispectral UAVs and Sentinel-2 satellite imagery.Pre-processing of sensor data involves missing value imputation, normalization, and feature selection using the Hybrid Frilled Lizard Osprey (HFLO) algorithm, while image data undergo resizing, augmentation, and filtering approach.Image based features are extracted by a position-attention DenseNet-201 model.Also, the features from image data and sensor data are fused using a concatenation mechanism.Finally, fully connected layers and improved support vector regression (ISVR) are used to identify the tapioca yield prediction.The integrated DL methods give higher accuracy than individual modalities for both modalities.The image-based model captures spatial and phenotypic variations, while the sensor-based model captures fine-grained environmental effects.The proposed approach obtained the MAE value of 0.0566, RMSE value of 0.654, and R 2 value of 99.5, demonstrating the potential of multimodal real-time data integration for precision agriculture in tapioca fields.

Plant phenotyping relevance

UAV・衛星画像と環境センサーを統合し、深層学習でキャッサバの収量という植物形質を推定する手法の開発が中心である。

Code and data availability

The supplied article blocks describe field-collected sensor and UAV/satellite imagery data and a multimodal deep learning pipeline for tapioca yield prediction, but contain no data availability statement, no public dataset deposit, no code repository or URL, and no supplement referencing the paper's measurements or the

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