Integrating Deep Learning and Spectral Analysis for Multi-Modal Data Fusion in Precision Agriculture for Enhancing Crop Health Monitoring and Yield Prediction

Abstract

The purpose of Precision Agriculture is to incorporate technology into various agricultural processes to increase efficiency and productivity. In fact, Precision Agriculture uses advanced technologies such as sensors and data analytics to improve crop yields. However, a significant challenge in this area is effectively integrating multiple data sources to accurately predict crop health and yield using all available information. This problem arises because traditional models typically use spectral analysis or deep learning techniques independently. Due to this separation, neither method generates the desired results. Researchers propose a solution to this issue by combining spectral analysis and deep learning for multimodal data fusion in precision agriculture. Our integrated approach begins with the collection of multispectral data from drone- or satellite-based sensors to characterise crop types. Spectral analysis will determine each crop type's chlorophyll and water content, which affect plant health. Deep learning will be used to analyse the intricate interconnections between crop yields and derived attributes to understand their relationships better. Integrated use of these two technologies will give us a broader range of data and knowledge about crop variety health and yield than single-use or standalone applications.

Code and data availability

The paper reports plant-phenotyping-style measurements (crop health, yield, spectral/topographic/preprocessing comparisons) but provides no public dataset, code, model, or supplement. Its Data Availability Statement says data are available only upon justified request to the corresponding author, so the paper-specific (

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