Yield Prediction Using Spatial and Temporal Deep Learning Algorithms and Data Fusion

The world’s population is expected to grow to 9.6 billion by 2050. This exponential growth imposes a significant challenge on food security making the development of efficient crop production a growing concern. The traditional methods of analyzing soil and crop yield rely on manual field surveys and the use of expensive instruments. This process is not only time-consuming but also requires a team of specialists making this method of prediction expensive. Prediction of yield is an integral part of smart farming as it enables farmers to make timely informed decisions and maximize productivity while minimizing waste. Traditional statistical approaches fall short in optimizing yield prediction due to the multitude of diverse variables that influence crop production. Additionally, the interactions between these variables are non-linear which these methods fail to capture. Recent approaches in machine learning and data-driven models are better suited for handling the complexity and variability of crop yield prediction. Maize, also known as corn, is a staple crop in many countries and is used in a variety of food products, including bread, cereal, and animal feed. In 2021-2022, the total production of corn was around 1.2 billion tonnes superseding that of wheat or rice, making it an essential element of food production. With the advent of remote sensing, Unmanned aerial vehicles or UAVs are widely used to capture high-quality field images making it possible to capture minute details for better analysis of the crops. By combining spatial features, such as topography and soil type, with crop growth information, it is possible to develop a robust and accurate system for predicting crop yield. Convolutional Neural Networks (CNNs) are a type of deep neural network that has shown remarkable success in computer vision tasks, achieving state-of-the-art performance. Their ability to automatically extract features and patterns from data sets makes them highly effective in analyzing complex and high-dimensional datasets, such as drone imagery. In this research, we aim to build an effective crop yield predictor using data fusion and deep learning. We propose several Deep CNN architectures that can accurately predict corn yield before the end of the harvesting season which can aid farmers by providing them with valuable information about potential harvest outcomes, enabling them to make informed decisions regarding resource allocation. UAVs equipped with RGB (Red Green Blue) and multi-spectral cameras were scheduled to capture high-resolution images for the entire growth period of 2021 of 3 fields located in Ottawa, Ontario, where primarily corn was grown. Whereas, the ground yield data was acquired at the time of harvesting using a yield monitoring device mounted on the harvester. Several data processing techniques were employed to remove erroneous measurements and the processed data was fed to different CNN architectures, and several analyses were done on the models to highlight the best techniques/methods that lead to the most optimal performance. The final best-performing model was a 3-dimensional CNN model that can predict yield utilizing the images from the Early(June) and Mid(July) growing stages with a Mean Absolute Percentage error of 15.18% and a Root Mean Squared Error of 17.63 (Bushels Per Acre). The model trained on data from Field 1 demonstrated an average Correlation Coefficient of 0.57 between the True and Predicted yield values from Field 2 and Field 3. This research provides a direction for developing an end-to-end yield prediction model. Additionally, by leveraging the results from the experiments presented in this research, image acquisition, and computation costs can be brought down.

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