An effective hybrid attention model for crop yield prediction using IoT-based three-phase prediction with an improved sailfish optimizer

Internet of Things (IoT)-based smart agriculture approaches have been adopted to achieve promising results in terms of food productivity. Moreover, high cost and technological complexity are major problems in existing agricultural management systems. Thus, a new IoT-based smart agriculture farming model is implemented in three phases namely crop detection, crop disease detection, and crop yield prediction. In the first phase, the plant leaf images are gathered from the Kaggle source PlantifyDr. The collected images are passed to the deep feature retrieval phase, where the Visual Geometry Group (VGG16) is utilized to calculate feature (f1). Then, a Hybrid Attention-based Crop Type Detection Network (HA-CTDecNet) is used for detecting the crop types, in which the parameters are optimized by Attack Power Improved Sailfish Optimizer (API-SFO). In the second step of crop disease detection phase, the input images are fed to Otsu Thresholding for Abnormality segmentation. The deep attributes from the segmented images are haul out using VGG16 and attain the feature (f2). The extracted features are forwarded to the Hybrid Attention-based Crop Disease Detection Network (HA-CDDecNet), where the constraints are optimized using API-SFO. In the third crop yield prediction phase, the data related to crop location are collected from the crop yield forecast dataset. Then, the collected data and the extracted features (f1) and (f2) are concatenated and fed to the Principle Component Analysis (PCA). Then, these features are given to the Hybrid Attention-based Crop Yield Prediction Network (HA-CYPreNet) with the parameter optimization of API-SFO. The investigation is accepted to verify the effectiveness of the suggested IoT-based smart agriculture model with the traditional approaches by considering distinct measures. The Mean Absolute Error (MAE) of the crop yield prediction model is improved with 35% than CNN, 27.5% than LSTM, 17.5% than GRU, and 7.5% than LSTM_GRU while considering the learning percentage value as 55.