Malaysia has adopted various strategies in developing its space sector. Indigenous space technologies would enable a sustainable growth of the space field and at the same time develop the strategic space technologies. Therefore, issues related to the current space research level are fundamentally crucial to be highlighted. Subsequently, the space focus areas can be derived in order to meet the expectations of the national and international space technology growth requirements, which are moving on to a stronger posture in R&D. In the absence of a strong R&D national space industry leadership, the Malaysian space sector remains in a traditional downstream mode of the world space technology supply chain ever since. The space technology supply chain can be divided into the ground segment and the space segment. This paper examines the current space research activities in Malaysia within the framework of the space technology supply chain. As a result, a preliminary gap in the overview of space research in Malaysia is established.
Accurate, reliable and transparent crop yield prediction is crucial for informed decision-making by governments, farmers, and businesses regarding food security as well as agricultural business and management. Deep learning (DL) methods, particularly Long Short-Term Memory networks, have emerged as one of the most widely used architectures in yield prediction studies, providing promising results. Although other sequential DL methods like 1D Convolutional Neural Networks (1D-CNN) and Bidirectional long short-term memory (Bi-LSTM) have shown high accuracy for various tasks, including crop yield prediction, their application in regional scale crop yield prediction remains largely unexplored. Interpretability is another pressing and challenging issue in DL-based crop yield prediction, a factor that ensures the reliability of the model. Thus, this study aims to develop and implement an explainable DL model capable of accurately predicting crop yield and providing explanations for the predictions. To achieve this, we developed three state-of-the-art sequential DL models: LSTM, 1D CNN, and Bi-LSTM. We then employed three popular interpretability techniques: Local interpretable model-agnostic explanations (LIME), Integrated Gradient (IG) and Shapley Additive Explanation (SHAP) to understand the decision-making process of the models. The Bi-LSTM model outperformed other models in terms of predictive performance (R2 up to 0.88) and generalizability across locations and ranges of yield data. Explainability analysis reveals that enhanced vegetation index (EVI), temperature and precipitation at later stages of crop growth are most important in determining Winter wheat yield. Further, we demonstrated that XAI methods can also be used to understand the decision-making process of the models, to understand instances such as high- and low-yield samples, to find possible explanations for erroneous predictions, and to identify regions impacted by particular stress. By employing advanced DL techniques along with an innovative approach to explainability, this study achieves highly accurate yield prediction while providing intuitive insights into the model's decision-making process.