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 and then predicts the remaining time to farrowing using symbolic regression, achieving a mean absolute error of 9.28 hours. Compared with closed‑box models such as support vector regression and neural networks, symbolic regression outperformed them in both accuracy and interpretability.
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: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
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
arXiv:2510. 14538v3 Announce Type: replace Abstract: Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.
By Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Paolo Morettin, Elena Umili, Antonio Vergari, Efthymia Tsamoura, Andrea Passerini, Stefano Teso
The paper presents a hybrid model that combines a four‑parameter water‑balance core with a Random Forest residual correction and conformal prediction to generate 90‑percent confidence intervals for soil moisture forecasts. The risk‑aware rule uses the lower bound of these intervals to trigger irrigation decisions, improving early detection of management‑threshold crossings while maintaining precision. Evaluation on three years of hourly data from a Mediterranean cropland station shows the hybrid outperforms nine baselines, achieving up to +27.4 % skill at three hours and demonstrating that uncertainty quantification dictates the reliable lead time for irrigation advice.
By Andrea Scariolo
arXiv:2609.08078v1 Announce Type: new
Abstract: Food waste in the restaurant sector poses a substantial challenge to environmental sustainability and economic efficiency. This paper presents an explo...
By Md Mehedi Hasan Naeem, Md Ashraful Islam, Moumita Barua, Ishtiyak Ahmmad Araf, Md. Arefin Haque Mahir
arXiv:2608. 06001v1 Announce Type: new Abstract: Commercial grazing systems yield irregular livestock observations, which challenge cattle growth forecasting.
By Muhammad Riaz Hasib Hossain, Rafiqul Islam, Shawn R. McGrath, Md Zahidul Islam, David W. Lamb
SymboLLM-FE combines symbolic regression and large language models to automate feature engineering for tabular data. It first extracts mathematically expressive formulas that correlate strongly with the target, then refines them with LLMs to improve interpretability. Experiments on six real‑world datasets and four Kaggle competitions show that SymboLLM‑FE outperforms existing AutoFE methods while reducing the number of costly LLM calls.
By Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou, Yu-Feng Li, Lan-Zhe Guo
arXiv:2605. 27618v2 Announce Type: replace Abstract: Despite the wide use of explainability techniques to attempt to understand the behavior of Artificial Intelligence (AI), the generated explanations may not always be reliable.
By Tom\'as Pereira, Jo\~ao Vitorino, Eva Maia, Isabel Pra\c{c}a