Controlled Environmental Agriculture (CEA) has gained a lot of attention in recent years, mainly because of its ability to overcome extreme weather problems and ensure food safety. CEA can meet the full growth state monitoring of the crop period; however, the optimization of the growing environment is still limited by the algorithm defects. In this article, we present an optimization method of growing environment based on reinforcement learning, Q-learning and the time-series prediction model Informer. This approach is demonstrated for the first time as far as we know. By employing Informer, we predicted the growth of strawberries based on current environmental variables and plant status. The prediction results served as a reward to motivate Q-learning, guiding it to make optimal modifications to the environment in real-time. This approach aimed to achieve the optimal cultivation environment continuously. Two groups of validation experiments were conducted based on different cultivation objectives: “obtaining the most stolons” and “obtaining the highest fruit count”. Compared to the empirically planted groups, the experimental groups using the RL-Informer model achieved yield increases of 17.81% and 20.78%, respectively. These experiments highlight the outstanding performance of the proposed RL-Informer model in real-time prediction and modification of environmental variables.

Feature detection of strawberry multi-type defects and the ripeness stage faces huge challenges because of color diversity and visual similarity. Images from hyperspectral near-infrared (NIR) information sources are also limited by their low spatial resolution. In this study, an accurate RGB image (with a spatial resolution of $2048\times 1536$ pixels) and NIR image (ranging from 700–1100 nm in wavelength, covering 146 bands, and with a spatial resolution of $696\times 700$ pixels) fusion method was proposed to improve the detection of defects and features in strawberries. This fusion method was based on a pretrained VGG-19 model. The high-frequency parts of original RGB and NIR image pairs were filtered and fed into the pretrained VGG-19 simultaneously. The high-frequency features were extracted and output into ReLU layers; the $l_1$-norm was used to fuse multiple feature maps into one feature map, and area pixel averaging was introduced to avoid the effect of extreme pixels. The high- and low-frequency parts of RGB and NIR were summed into one image according to the information weights at the end. In the validation section, the detection dataset included expanded 4000 RGB images and 4000 NIR images (training and testing set ratio was 4:1) from 240 strawberry samples labeled as mud contaminated, bruised, both defects, defect-free, ripe, half-ripe, and unripe. The detection neural network YOLOv3-tiny operated on RGB-only, NIR-only, and fused image input modes, achieving the highest mean average precision of 87.18% for the proposed method. Finally, the effects of different RGB and NIR weights on the detection results were also studied. This research demonstrated that the proposed fusion method can greatly improve the defect and feature detection of strawberry samples.

In crop cultivation, particularly in controlled environmental agriculture, light quality is one of the most critical factors affecting crop growth and harvest. Many scholars have studied the effects of light quality on strawberry traits, but they have used relatively simple light components and considered only a small number of light qualities and traits in each experiment, and the results were not complete or objective. In order to comprehensively investigate the effects of different light qualities from 350 nm to 1000 nm on strawberry traits to better predict the future growth trend of strawberries under different light qualities, we proposed a new approach. We introduced Spearman’s rank correlation coefficient to handle complex light quality variations and multiple traits, preprocessed the cultivation data through the CEEDMAN method, and predicted them using the Informer network. We took 500 strawberry plants as samples and cultivated them in 72 groups of dynamically changing light qualities. Then, we recorded the growth changes and formed training and testing sets. Finally, we discussed the correlation between light quality and plant trait changes in consistency with current studies, and the proposed prediction model achieved the best performance in the prediction task of nine plant traits compared with the comparison models. Thus, the validity of the proposed method and model was demonstrated.