Abstract

Crop failure due to drought periods may affect crop yield due to climatic variation, and natural disasters may affect soil health in the agricultural environment. Factors that impact soil health, such as filtering the natural water quality level. Forecasting tools are used to identify future trends in the soil robotic sensor to improve the accuracy of crop yield. In this paper, we study the real-time data monitoring for accurate prediction to help traders to better decisions. The best-fit Proximal Policy Optimization (PPO)model is chosen by finding a specific location to crop the yield in the environment. The output of such an interpretable model could improve valuable forecasting for plant growth. This approach can achieve 96% accuracy in solving a complex problem. This computational result is cost-effective and intelligent farming for predicting yield and resource allocation in farming. Precision agriculture for early detection, for planning a model for long-term food security, and for real-time agricultural decision making. To forecast crop yield monitoring of health care parameters for environmental factors, Remote sensing, and robotic soil sensors for real-time data. Crop specification for land location to reduce the impact of soil climate change variability. Agriculture is a critical sector for rising food demand and soil health parameters such as pH, moisture content, and nutrient concentration. The study of PPO stands for Proximal Policy Optimization, a powerful tool for smart agricultural farming.