Deep Learning Approaches for Enhanced Precision in Crop Yield Prediction

Accurate crop yield prediction is a vital tool for farmers to make well-informed decisions in the field of agriculture, which Ais crucial for developing economies. The work explores the complex challenges that researchers encounter when predicting crop yields. These challenges are distinguished by dynamic, noisy, non-stationary, and non-linear characteristics found in varied data sets. An in-depth exploration is conducted to understand the various factors that impact crop yield. These factors include changes intemperature, the amount of rainfall, diseases of plants, pests, the use of fertilizer, and the quality of the soil. Furthermore, the study carefully looks at the features used and evaluates the various deep learning techniques and performance measures used in the context of crop yield prediction. The comprehensive investigation advances our understanding of the complexities involved and provides insightful information that will help develop reliable and efficient crop yield forecast models.