Development of an online prediction system for soil organic matter and soil moisture content based on multi-modal fusion

Li, Hao; Song, Yamei; Wang, Zhaoyang; Li, Minzan; Yang, Wei

doi:10.1016/j.compag.2024.109514

Computers and Electronics in Agriculture

2024

DOI: 10.1016/j.compag.2024.109514

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Development of an online prediction system for soil organic matter and soil moisture content based on multi-modal fusion

Hao Li,

Yamei Song,

Zhaoyang Wang

et al.

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Cited by 2 publications

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A multivariate soil temperature interval forecasting method for precision regulation of plant growth environment

Yin,

Wu,

Huang

et al. 2024

Front. Plant Sci.

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Foliage plants have strict requirements for their growing environment, and timely and accurate soil temperature forecasts are crucial for their growth and health. Soil temperature exhibits by its non-linear variations, time lags, and coupling with multiple variables, making precise short-term multi-step forecasts challenging. To address this issue, this study proposes a multivariate forecasting method suitable for soil temperature forecasting. Initially, the influence of various environmental factors on soil temperature is analyzed using the gradient boosting tree model, and key environmental factors are selected for multivariate forecasting. Concurrently, a point and interval forecasting model combining the Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS) and Gaussian likelihood function is proposed, providing stable soil temperature forecasting for the next 20 to 120 minutes. Finally, a multi-objective optimization algorithm is employed to search for optimal initial parameters to ensure the best performance of the forecasting model. Experiments have demonstrated that the proposed model outperforms common models in predictive performance. Compared to Long Short-Term Memory (LSTM) model, the proposed model reduces the Mean Absolute Error (MAE) for forecasting soil temperatures over the next 20, 60, and 120 minutes by 0.065, 0.138, and 0.125, respectively. Moreover, the model can output stable forecasting intervals, effectively mitigating the instability associated with multi-step point forecasts. This research provides a scientific method for precise regulation and disaster early warning in facility cultivation environments.

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A multivariate soil temperature interval forecasting method for precision regulation of plant growth environment

Yin,

Wu,

Huang

et al. 2024

Front. Plant Sci.

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Key Technologies in Intelligent Seeding Machinery for Cereals: Recent Advances and Future Perspectives

Liu,

Zhou,

Zhang

et al. 2024

Agriculture

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The operational performance of cereal seeding machinery influences the yield and quality of cereals. In this article, we review the existing literature on intelligent technologies for cereal seeding machinery, encompassing active controllable seeding actuators, intelligent seeding rate control, and intelligent seed position control systems. In this manuscript, (1) the characteristics and innovative structures of existing motor-driven seed-metering devices and ground surface profiling mechanisms are expounded; (2) state-of-the-art detection principles and applications for soil property sensors are described based on different soil properties; (3) optimal seeding rate decision approaches based on soil properties are summarized; (4) the research state of seeding rate measuring and control technologies is expounded in detail; (5) trajectory control methods for seeding machinery and seeding depth control systems are described based on measurement and control principles; and (6) the present state, limitations, and future development directions of intelligent cereal seeding machinery are described. In the future, more advanced multi-algorithm and multi-sensor fusion technologies for soil property detection, optimal seeding rate decisions, seeding rates, and seed position control are likely to evolve. This review not only expounds the latest studies on intelligent actuating, sensing, and control technologies for intelligent cereal seeding machinery, but also discusses the shortcomings of existing intelligent seeding technologies and future developing trends in detail. This review, therefore, offers a reference for future research in the domain of intelligent seeding machinery for cereals.

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Development of an online prediction system for soil organic matter and soil moisture content based on multi-modal fusion

Cited by 2 publications

References 43 publications

A multivariate soil temperature interval forecasting method for precision regulation of plant growth environment

A multivariate soil temperature interval forecasting method for precision regulation of plant growth environment

Key Technologies in Intelligent Seeding Machinery for Cereals: Recent Advances and Future Perspectives

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