The study explores explainable AI for precision livestock farming by using acceleration sensor data to predict farrowing time in sows. It first detects nest‑building behavior in 166 of 180 sows, then applies symbolic regression to forecast the remaining time to farrowing, achieving a mean absolute error of 9.28 hours. Symbolic regression outperformed traditional closed‑box models such as support vector regression and neural networks in both accuracy and interpretability.
By Elisabeth Mayrhuber, Maciej Oczak, Michael Iwersen, David Brunner, Viktoria Dorfer, Stephan Winkler
arXiv:2607. 09751v1 Announce Type: new Abstract: Machine Learning (ML) algorithms have been widely used to estimate agricultural variables across diverse contexts.
By Hamid Ebrahimy, Moritz Lucas, Martin Atzmueller
arXiv:2506. 04571v3 Announce Type: replace Abstract: Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies.
By Srikanth Thudumu, Jason Fisher
The study compares five regression models—multiple linear regression, random‑forest regression, decision‑tree regression, ridge regression, and lasso regression—for predicting crop yield using a dataset of 1,000,000 observations and ten variables. After preprocessing, the models were evaluated with 80:20 and 70:30 training‑testing splits, using R‑squared, MSE, and RMSE as metrics. Lasso regression and multiple linear regression achieved the highest test R‑squared (~0.913) and lowest RMSE (~0.501), with linear models providing competitive baselines and random forest showing a large training‑testing gap.
By Saptak Das, Arijit Sarkar, Soumik Neogi, Suborno Naskar
arXiv:2512. 21066v4 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) reveals how explanatory variables relate to a response variable, yet communicating XAI outputs to laypersons remains difficult, limiting trust in AI-based predictions.
By Tomoaki Yamaguchi, Yutong Zhou, Masahiro Ryo, Keisuke Katsura
arXiv:2512. 03578v3 Announce Type: replace-cross Abstract: Time series extrinsic regression (TSER) refers to the task of predicting a continuous target variable from an input time series.
By Florent Forest, Amaury Wei, Olga Fink