Comprehensive Approaches to Agricultural Intelligence: U-Net, YOLO, and Random Forest in Practice

This paper seeks to implement and test advanced precision agricultural monitoring and detection systems derived from machine learning, remote sensing, and sensor technologies to improve pest detection, crop yields assessment, and animal husbandry management practices. The approach involves an automated and hierarchical framework integrating cutting edge machine learning technologies with fine spatial resolution remote sensing images and sensors. For instance, the authors apply CNN (convolutional neural networks) for pest identification; the deep learning models trained on Sentinel-2 images serve as yield predictors; UWB location coordinates and accelerometric measures combined form livestock behavior monitoring. This methodology encompasses the stages of implementation, testing and finally verification of the model. A variety of machine learning systems such as Faster-PestNet or deep semantic segmentation algorithms process data while remote sensing and the sensors’ data were used in predictions and classifications. This allows using different performance measures such as accuracy, precision recall rates and RMSE (root mean square error) to test the systems.