Hybrid deep WaveNet-LSTM architecture for crop yield prediction

Navigating the complex landscape of 21st-century agriculture involves overcoming numerous obstacles, such as changing dietary trends, food safety issues, and health concerns due to soil inconsistencies, climatic fluctuations, and varied agricultural practices. The global population surge, climate change, and resource depletion compound these issues. For various stakeholders, including farmers and policymakers, precise crop yield predictions at diverse spatial levels can be immensely advantageous. The value of these forecasts heightens when they are available at multiple spatial resolutions. However, accurately predicting the complex interplay between many data sources and regional yields presents a significant challenge. Conventional methods often fall short, delivering inconsistent results that are difficult to generalize due to their limited ability to incorporate spatial and temporal features, insufficient understanding of market trends, and challenges with scalability and nonlinearity. This paper proposes an innovative Deep Learning approach that adeptly captures and integrates spatial and temporal features, marking a substantial enhancement over traditional methodologies that often grapple with these aspects. This approach forecasts crop yields with a minimal error rate, leveraging the robustness of a unique WaveNet and LSTM hybrid architecture, introducing a fresh perspective to agricultural yield predictions. The novelty of our methodology lies in its two-tier design: the preliminary phase involves pre-processing similar to existing models, and the second phase harnesses the combined power of WaveNet and LSTM for Feature Extraction and regression, enabling precise predictions. The model has been tested on four crops in the Netherlands using varied data splitting criteria, demonstrating stellar performance by offering predictions with minimal error rates. When compared against prevailing machine learning methodologies using an identical Netherlands dataset, our approach outperforms them, highlighting its efficacy and potential practical application in real-world agricultural settings.

Automated summary

Navigating the complexities of contemporary agriculture involves overcoming various obstacles such as dietary changes, food safety concerns, and health issues related to soil inconsistencies, climate fluctuations, and diverse agricultural practices. This paper proposes a novel Deep Learning approach that effectively captures and integrates spatial and temporal features to forecast crop yields with minimal error rates, outperforming prevailing machine learning methodologies.