Temperature Variation Modelling in Mushroom Growing Hall with IAN-Bidirectional GRU Model

Modern developments in computing and electronics have made intelligent systems suitable for use in the mechanization of agricultural processes. This proposed used multilayered perceptron and radial basis function networks to forecast the temperature shifts in the mushroom cultivation chamber based on independent variables including water temperature, ambient temperature, water tap, fresh air and circulation air dampers, and water tap. Preprocessing, feature extraction, and training the model are the three phases that comprise this proposed method. Reflectance calibration, normalization, and spectral smoothing are the steps involved in data preprocessing. Feature extraction made quick work of extracting information from mushroom photos by employing the SIFT algorithm. The proposed model was trained using an IAN-Bidirectional GRU. When compared to more conventional approaches, the proposed methodology is superior. The method's application resulted in a 93.75% improvement in accuracy.