Edible mushrooms are rich in nutrients; however, harvesting mainly relies on manual labor. Coarse localization of each mushroom is necessary to enable a robotic arm to accurately pick edible mushrooms. Previous studies used detection algorithms that did not consider mushroom pixel-level information. When these algorithms are combined with a depth map, the information is lost. Moreover, in instance segmentation algorithms, convolutional neural network (CNN)-based methods are lightweight, and the extracted features are not correlated. To guarantee real-time location detection and improve the accuracy of mushroom segmentation, this study proposed a new spatial-channel transformer network model based on Mask-CNN (SCT-Mask-RCNN). The fusion of Mask-RCNN with the self-attention mechanism extracts the global correlation outcomes of image features from the channel and spatial dimensions. Subsequently, Mask-RCNN was used to maintain a lightweight structure and extract local features using a spatial pooling pyramidal structure to achieve multiscale local feature fusion and improve detection accuracy. The results showed that the SCT-Mask-RCNN method achieved a segmentation accuracy of 0.750 on segm_Precision_mAP and detection accuracy of 0.638 on Bbox_Precision_mAP. Compared to existing methods, the proposed method improved the accuracy of the evaluation metrics Bbox_Precision_mAP and segm_Precision_mAP by over 2% and 5%, respectively.

The data-driven model predictive control (MPC) has been widely applied in the field of energy-saving control. However, the low interpretability and speed of optimization often occur on the pure MPC of temperature in the energy-saving control system. In this study, a data-physics mixed model was proposed for the air conditioning energy-saving control system in mushroom houses. Firstly, a basic prediction model was established to combine the gated recurrent unit (GRU) neural network with the attention mechanism. Some influencing factors were considered, such as heat transfer from fresh air, heat dissipation from mushroom respiration, radiant heat from envelope structures, and air conditioning cooling. The internal thermal balance equation of the mushroom house was incorporated into the physical loss term, along with the data loss term between the predicted and actual temperatures. The loss function was then formed. The prediction model and the loss function together formed a special gray-box prediction model. Temperature prediction was implemented on the mushroom house using a data-physical hybrid model. Then, a target function was established to consider the deviation among the predicted temperature. The reference trajectory was taken as the prediction accuracy term, while the air conditioning operation time was taken as the energy consumption term. Finally, an improved Adam algorithm was developed to integrate the "belief" and the "look-ahead" mechanisms, according to the basic Adam algorithm. The target function was rapidly obtained in the optimal control sequence for the air conditioner over the control time domain. The first control value in the sequence was applied to the air conditioning system. Thus the optimal control was achieved to simultaneously consider both accuracy and energy consumption for the air conditioning system in the mushroom house. Experimental results showed that the temperature prediction model with the data-physical hybrid outperformed the various previous models. The accuracy of prediction was improved by 18%, compared with the pure data-driven GRU model, while the root mean square error (RMSE) was controlled within 0.10 °C, and the model training time was only 16 s. The fitness value of the improved Adam algorithm was 0.751, with a computation time of 47 s. A comparative analysis with various optimization algorithms showed that the fitness value decreased by 6%, compared with the Adam optimization algorithm, while the computation time was reduced by 81%, compared with the elitist fast non-dominated sorting genetic algorithm (NSGA-II). Compared with the traditional threshold control, the air conditioning model adapted the best to the flexible changes in the reference trajectory. The control input was adjusted in advance using a unique prediction mechanism, indicating the better tracking performance of the reference trajectory. The tracking accuracy was improved by 63% during a 5-day simulation experiment. The control accuracy RMSE was reduced by an average of 73% and the energy consumption of air conditioning was reduced by an average of 12%. The energy-saving control model enhanced the interpretability of the prediction. The computation speed was also improved with the high solution accuracy of the rolling optimization. The high robustness was used to change the control parameters. As such, precise temperature and energy-saving control were achieved in the mushroom house. The finding can also provide a strong reference for the energy-saving control system in industrialized mushroom cultivation.