Intelligent Crop Monitoring: Machine Learning-based Classification of Plant Growth Stages for Precision Agriculture

The study's overarching goal is to advance precision agriculture by exploring the potential of machine learning algorithms for smart crop monitoring and the categorization of plant growth stages. The research evaluates several categorization methods using a comprehensive dataset that include crop labels, nutrient levels (N, P, and K), environmental conditions (temperature, humidity, pH, and rainfall), and other details. Accuracy and AUC values beyond 0.99 were produced using ensemble techniques like Extra Trees Classifier and Random Forest, which led to outstanding results. Results like this show how effective these methods are at pinpointing which crop varieties thrive in different types of soil and climates. The most influential components, according to the feature significance analysis, were precipitation and relative humidity. Pre-processing verified both the data's excellent quality and the model's consistency. The study’s use of 10-fold cross-validation further proved the model’s constant effectiveness. This research proves that data-driven methods may enhance crop management, yield forecasting, and resource optimization. One of the next directions that will be explored to further generalize the models and improve decision-making is the integration of real-time sensor data with satellite imagery. To help agricultural specialists and farmers with their day-to-day tasks, the research emphasizes the significance of having interpretable and accessible AI technology.