Synergizing Smart Agriculture with Hybrid Deep Learning: Predicting Crop Yields Using IoT

Faculty of CS & IT, Kalinga University, Naya Raipur, Chhattisgarh, India

^* Corresponding author: ku.abhijeetmadhukarhaval@kalingauniversity.ac.in

Abstract

Agriculture can be defined as the systematic and intentional practice of cultivating and managing plants and animals to produce food, fiber, and other agricultural products. Agricultural practices in India hold the second position globally and encompass approximately 61.1% of the total land area in the country. The Indian economy primarily relies on agriculture and agro-industrial products. Various factors, such as soil composition (including elements like Nitrogen, phosphorus, and Potassium), crop rotation practices, soil moisture content, ambient temperatures, precipitation patterns, and other relevant variables, can significantly influence crop productivity. Smart Agriculture (SA) implementation has recently yielded significant practical benefits, establishing it as a highly significant and valuable system. Using environmental information, including wind velocity, temperature, and moisture, in outdoor plantations facilitates farming operations’ strategic management and regulation, enhancing crop yield and quality. Accurately predicting crop yield trends poses a challenge due to the intricate nature of sensing data, characterized by complexity, nonlinearity, and multiple variables. This study proposes a Hybrid Deep Learning model for Predicting Crop Yields (HDL-PCY) using the Internet of Things (IoT). The HDL-PCY system utilizes the Empirical Mode Decomposition (EMD) technique to break down the crop yield information into distinct element groups with varying frequency attributes. Subsequently, a Long Short-Term Memory (LSTM) network is trained for each group to serve as a sub-predictor. Finally, the predictions generated by the LSTM networks are combined to produce the overall prediction result. The obtained results demonstrate that the proposed HDL-PCY can achieve higher levels of accuracy of 97.32%, 98.03%, 98.74%, and 95.92% for precipitation, temperature, pH, and moisture content, respectively, thereby catering to the requirements of SA.