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.
arXiv:2608. 07801v1 Announce Type: cross Abstract: Precision nitrogen (N) management (PNM) for cotton requires in-season monitoring of crop growth parameters and N status indicators to decide fertilizer timing, placement, and application rates for optimal canopy development and yield.
The paper presents a crop‑yield prediction prototype for Punjab, Pakistan that combines several machine‑learning models (Random Forest, XGBoost, SVR, Ridge‑stacked ensemble) with a MobileNetV2 leaf‑health classifier, and deploys it as a web app with SHAP explanations. An initial 80/20 split on a small merged Kaggle table yields an R² of 0.991, but an audit reveals severe data duplication, reducing performance to –0.20 when whole years are held out. Further analysis on deduplicated data and a longer FAOSTAT table shows that a simple per‑crop linear trend outperforms the complex models, and that neither pesticide use nor national temperature changes explain the residuals; the leaf‑health component, though highly accurate on image data, is not linked to yield records and adds no predictive value. whyItMatters":"The study highlights how data quality and proper validation can dramatically alter the perceived effectiveness of advanced predictive models in agricultural forecasting."
The study evaluates machine learning models for nitrogen recommendations in winter wheat by directly scoring profit loss on 892 yield response curves, rather than relying on prediction accuracy. Results show that none of the models recover the best rate within farm tolerance, and at typical prices all models underperform standard UK advice. A simple post‑model correction step reduces profit losses by up to 43% without retraining, while a hybrid approach further mitigates bias and large losses.
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.
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.
arXiv:2606. 13959v1 Announce Type: new Abstract: Sierra Leone's agriculture operates with almost no data-driven decision support, and no published machine learning study has examined the country's crop yields.
arXiv:2607. 00834v1 Announce Type: new Abstract: Near-Infrared (NIR) spectroscopy has emerged as a promising alternative to traditional soil analysis methods, offering advantages such as speed, low cost, and non-destructive testing.
The study evaluates machine learning for nitrogen recommendations in winter wheat by directly scoring profit loss on 892 yield response curves, rather than relying on prediction accuracy. Results show that ML models alone fail to recover the best nitrogen rate within farm tolerance and underperform standard UK advice across price scenarios. However, a simple post‑model correction step significantly reduces profit losses, and a hybrid approach combining standard advice with a damped correction eliminates bias and large losses.
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 paper evaluates machine learning for nitrogen recommendations in winter wheat by directly scoring the profit lost on measured yield response curves, rather than relying on prediction accuracy. Using 892 yield curves from UK experiments, the authors find that machine learning models fail to recover the best nitrogen rate within farm tolerance and generally underperform standard UK advice in terms of profit. However, a simple post‑model correction step can reduce profit losses by up to 43% without retraining, suggesting that machine learning can enhance standard advice rather than replace it.
arXiv:2608.29008v1 Announce Type: new Abstract: Accurate forecasting of grape berry temperature (Tb) is essential for enabling timely heat stress management in vineyards. In this study, a feed-forwar...
arXiv:2606. 05731v1 Announce Type: new Abstract: In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops.