Agricultural Yield Prediction Using ML Algorithms in the Industry 5.0

Integration of machine learning (ML) techniques into agricultural yield prediction is increasingly significant, particularly within the context of Industry 5.0, which emphasizes the incorporation of advanced technologies into traditional industries. This abstract delves into the methodologies, benefits. The hurdles and future opportunities of utilizing machine learning for predicting agricultural yields within the context of Industry 5.0. Supervised learning models like (SVM), random forests, and DL frameworks such as (CNNs) have demonstrated exceptional capabilities in analyzing complex agricultural datasets. These datasets include historical yield data, meteorological information, soil quality parameters, crop types, and agricultural practices. Predicting agricultural yields with machine learning involves several crucial steps: collecting data, preprocessing it, engineering features, selecting and training the model, and validating the results. Advanced ML techniques enable the deployment of predictive Models that can discern complex patterns and relationships in data, enabling precise yield predictions for different crops and regions. In the framework of Industry 5.0, the integration of ML-powered agricultural yield prediction systems with IoT devices, sensor networks, drones, and autonomous machinery facilitates real-time data acquisition and monitoring. This influx of real-time data allows for dynamic adjustments in farming practices, such as irrigation scheduling, pest management, fertilizer application, and harvesting strategies based on predictive insights. This article explores essential ML approaches, challenges encountered, and future pathways for enhancing agricultural sustainability and productivity through data analytics within smart farming systems. The use of ML techniques in smart agriculture plays a critical by analyzing historical and real-time data to forecast crop yields. Optimizing agricultural practices, and improving resource allocation while increasing productivity. This includes deploying various ML models such as regression, classification, and ensembles to analyze extensive datasets encompassing weather conditions, soil characteristics, crop details, and farming methodologies.