Deep Learning-Driven Smart Farming: A Comprehensive Review of Integrated Frameworks for Soil Moisture Prediction and Crop Yield Forecasting

Abstract

Smart Farming is a revolutionary concept that has gained prominence in response to global food security issues due to climate change, water scarcity, and population increase. Precise prediction of yield on the basis of the moisture content of soil is key in making optimal use of water and ensuring minimal wastage along with maximizing the output. This review paper attempts to conduct an exhaustive study of existing techniques for soil moisture and yield predictions and propose an innovative, deep learning-based smart farming paradigm for high accuracy yield prediction using soil moisture. In particular, the paper discusses current trends in traditional machine learning algorithms, latest developments in deep learning models, IoT-based smart farming systems, as well as their combinations. Various problems such as the availability of data, noise in sensors, spatiotemporal dynamics, and interpretability are explored with regard to each technique and discussed at length in the context of the proposed deep learning-based approach. Specifically, the proposed framework involves an end-to-end solution based on IoT sensor networks, multimodal data fusion, CNN, LSTM, and attention modules. Possible benefits include a 25–30% accuracy gain in predictions, up to 20–40% decrease in water utilization.