A methods guideline for deep learning for tabular data in agriculture with a case study to forecast cereal yield

TL;DR: This research integrates blockchain and deep learning to create an intelligent greenhouse control system, enhancing crop yields, efficiency, and security while ensuring transparency and accountability through IoT and blockchain-based traceability and data management.

TL;DR: This study compares the accuracy, complexity, and data efficiency of 10 statistical and deep learning models for reference evapotranspiration (ETo) time series forecasting, finding that N-BEATS and Holt-Winters methods outperform SARIMA in accuracy and efficiency.

TL;DR: In this article , the authors proposed the RAID (robust and interpretable DPLF) model, which enhances the accuracy of peak load forecasting by recognizing daily peak load patterns and building separate DNN models for each day of the week.

TL;DR: This study evaluates the effects of cross-validation strategies and machine learning optimization on soybean yield prediction models using UAV-based remote sensing, finding that spatially-aware cross-validation and simple models improve model transferability and extrapolation accuracy.

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.

TL;DR: This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.

TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.

TL;DR: This article will be concerned primarily with the second and third questions, which are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory.