Maximizing Crop Yield: Crop Yield Prediction using Advanced ML Algorithms

For food security and economic stability, effective crop damage management is vital, as agriculture is vital to human survival. This research endeavor presents a data-centric methodology for forecasting crop damage and yield by employing machine learning methods on an extensive collection of agricultural parameters, including historical damage records, DESIS Hyperspectral images, crop attributes, pesticide usage, and environmental factors. Robust feature engineering and exploratory data analysis (EDA) are implemented to identify intricate relationships within the dataset. This research paper integrates conventional algorithmic approaches such as Random Forest, K-Nearest Neighbors, Gaussian Naïve Bayes, and Support Vector Classifier (SVC) with a Long Short-Term Memory (LSTM) neural network model of advanced complexity. The fundamental objective is to develop precise and resilient models that can accurately categorize levels of crop injury. This research paper employs LSTM models to enhance predictive capabilities and offer significant insights into the intricate correlations that exist between agricultural parameters and crop damage. In addition to advancing precision agriculture, the findings of this study equip producers with useful instruments for risk management and proactive decision-making.