Shengnan Gao, Na Zhang , Yingying Liu, and Juan Pan
Agricultural robotics, multimodal fusion, knowledge extraction, inference process, recurrent patterns
In modern farming, agricultural robotics is essential to improve productivity, mitigate labour shortage and minimise resource consumption. Various fusion methods are applied to improve the farming process, but it has heterogeneity and complexity issues, creating scalability, and accuracy problems. The research difficulties are addressed using a multimodel fusion technique with an advanced deep learning process called Bayesian multimodal attention recurrent network. The introduced approach maximises forecasting accuracy by understanding the time trends and integrating robot data and shared knowledge information. The method derives knowledge from agricultural data to maximise the overall decision-making process during the analysis. Using sensor measurements, manual and autonomous imaging data can be used to holistically analyse crop systems and optimise robotic operations in agriculture, from transplanting to harvesting. Bayesian fusion network is used as a probabilistic logic to model uncertainties and characterise agricultural data linkages for robust decision making. Through self-attention processes, the overarching goal is to improve knowledge extraction and decision making. Using RNNs’ sequential data processing to capture temporal relationships and patterns, simulate complex farming operations, and make accurate predictions using robotics. From these recurrent patterns, knowledge of crop management practices can be extracted, and inferences can be made through attention scores and informed decision making in this agricultural robotics field. The accurate decision making about the inference observed from the proposed model can be evaluated using various metrics.
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