# Applications of Machine Learning Technology in Agricultural Data Mining
## 2 Overview of Published Articles
The study included collected temperature and humidity data from 18 sensor points in a batch-type corn dryer by using three predictive models: Multiple Linear Regression (MLR), Extreme Learning Machine (ELM), and Long Short-Term Memory (LSTM). The authors showed that the UVE-enhanced ELM model outperformed the rest, achieving an R\({}^{2}\) of 0.946 and an RMSE of 0.581%, while the UVE-LSTM model also performed strongly, with an R\({}^{2}\) of 0.934. As concluded by the authors, this result provides a more scientific method for predicting corn's drying moisture content and lays the foundation for using the prediction model to guide actual production. Furthermore, in agriculture, precision irrigation technology has become the core of modern agriculture, especially IoT, Big Data and AI. Precise prediction of irrigation water uses in the maize industry through Ensemble Kalman Filter (EnKF) and fuzzy optimization methods combined with the Decision Support System for Agrotechnology Transfer (DSSAT) model, by using remote sensing data of land moisture and leaf area provided by Google Earth Engine (GEE), to make predictions through the EnKF-DSSAT and fuzzy optimization-DSSAT models, showed interesting results [21]. These models achieved high accuracy in short-term (98.11%) and long-term (97.78%) forecasts, being reported as significantly better than the traditional models. It was concluded that this new approach can shows significant advantages and effectively improve the accuracy of maize irrigation water demand prediction, especially in solving the problems of poor accuracy and low resource utilization of traditional irrigation systems. In the fruit industry, determining peach firmness is critical for optimizing harvesting time and fruit quality, by exploring the enhancement of Classification and Regression Tree (CART) models using metaheuristic algorithms to improve the prediction accuracy of peach firmness. The study used a variety of metaheuristic algorithms, including the Genetic Algorithm (GA), Bat Algorithm (BA), Differential Evolution (DE), Particle Swarm Optimization (PSO) to optimize the CART model parameters. The results showed that CART model optimized using GA gave the most accurate predictions on the new data. Thisempirical experiment shows that by using various metaheuristic optimization techniques implemented in metaheuristicOpt and GA R libraries it is possible to improve the accuracy of the default CART model. It is important to note that the experiment is limited by the used R libraries and the optimization approach previously described. Hence different results could be obtained if other software packages or optimization approaches are used, as the authors mentioned previously [22]. With agriculture becoming increasingly robotic and automated, human activity in the field needs to be better comprehended for safety, efficiency, and unobstructed human-robot collaboration. According to [PERSON] et al. [23] human-robotic synergetic systems are considered to be the most mature way to circumvent problems appearing due to the complex and unpredictable nature of the agricultural environment, which contrasts with the stable domain found in industrial settings. They have described the fundamentals of human-robot interaction (HRI) from an agriculture-oriented perspective, which tried to identify potential hazards that can put human safety at risk. The, inertial measurement units (IMUs) which are electronic devices that measure physical motion parameters such as rotational rate and acceleration by using accelerometers and gyroscopes, have been also discussed. It was concluded that a combined approach is foreseen that reprioritizes safety measures, redetermines practical limits, identifies the riskiest postures and increases the awareness of the risk factors.