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Macronutrients, including nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S), are the most basic nutrient elements in the solution for the hydroponic system. However, the current management of hydroponic nutrient solutions usually depends on EC and pH sensors due to the lack of accurate specific macronutrient sensing equipment, which easily leads to nutritional imbalance for the cultivated plant. In this study, the UV-NIR absorption spectroscopy (200–1100 nm) was used to predict six macronutrients in hydroponic solutions; two kinds of single-task learning algorithms, including partial least squares (PLS) and least absolute shrinkage and selection operator (LASSO), and two kinds of multi-task learning algorithms, including dirty multi-task learning (DMTL) and robust multi-task learning (RMTL), were investigated to develop prediction models and assess capabilities of UV-NIR. The results showed that N and Ca could be quantitatively predicted by UV-NIR with the ratio of performance to deviation (RPD) more than 2, K could be qualitatively predicted (1.4 < RPD < 2), and P, Mg, and S could not be successfully predicted (RPD < 1.4); the RMTL algorithm outperformed others for predicting K and Ca benefit from the underlying task relationships with N; and predicting P, Mg, and S were identified as irrelevant (outlier) tasks. Our study provides a potential approach for predicting several macronutrients in hydroponic solutions with UV-NIR, especially using RMTL to improve model prediction ability.

【Objective】Cage biometrics and statistics are one of the key reference factors for marine pasture farming management. Aiming at the interference of reverberation noise and complex background, this paper constructs fish detection data sets under different lighting conditions, and uses forward-looking sonar imaging technology to propose a fish recognition lightweight model based on YOLOV5-MobilenetV3 and sonar images (LAPR-Net) to realize fish recognition in water cages in turbid or dark scenes.【Method】Taking tilapia as the research object, based on the frame structure of the YOLOV5 model, the backbone network module ado pts the lightweight Mob ileNetV3 bneck block, using the linear bottleneck inverse residual structure and depth separable convolution extract the features of fish in sonar images, applying the attention mechanism SE-Net to obtain multi-scale semantic features of sonar images and enhance the correlation between features; the neck network adopts the path aggregation network structure to perform multi-scale fusion of target features, to enhance the feature fusion ability; the prediction part adopts the maximum local search based on the non-maximum suppression method, removes the redundant detection frame, screens the detection frame with the highest confidence, and finally outputs and displays the detection result of the fish, including the position, category and detection probability of detecting an object.【Result】Four other mainstream detection models were selected for comparative experiments, including YOLOV3-ting (Darknet53), YOLOV5 (CSPdarknet53), YOLOV5 (Repvgg), and YOLOV5s (Transformer). It proposes the model parameter quantityof 3 545 453, FLOPs of 6.3 G, and the mAP of 0.957, and the average inference speed of each picture of the model is 0.08868 s. Compared with the YOLOV5 model, the mAP of the improved model has increased by 9.7%.【Conclusion】The proposed network improves the speed of training and recognition, reduces the requirements for hardware equipment, and provides a reference for the detection model of cage cultured fish in marine pastures.

【Objective】Light is the most important factor affecting plant growth, development, metabolism and external phenotype. In the process of plant growth, artificial light sources are often used instead of natural light to fill the seeds and seedlings to improve the quality, yield and efficiency of fruits and vegetables. Artificial light plant is one of the important ways for standardized and efficient seedling cultivation, but its high energy consumption, accounting for 60% to 80% of the total energy consumption of plant factories, seriously hinder the promotion and application of factory seedling technology. Therefore, reducing the energy consumption of light source and improving the utilization rate of light is one of the core goals for plant seedling.【Method】Aiming at the problems of fixed height, high energy consumption and difficult to match the light demand of seed and seedling growth, this study developed a precise light cultivation platform for seedlings based on Programmable Logic Controller (PLC) technology by combining theoretical analysis, model construction and experiment. Taking cucumber seedlings as the object, the performance of the light intensity control system of the precision light cultivation platform for seedlings was verified, and its effect on the growth of cucumber seedlings was explored.【Result】The experimental results showed that the light intensity and light distribution uniformity were improved after the optimization of the lamp bead structure. The developed platform can fill the light of cucumber seedlings by manual, automatic, Human Machine Interface (HMI) and host computer. Through the platform optical radiation energy consumption model and the layer level adjustment control strategy, the LED operating power is effectively reduced, saving about 40.65% of electric energy. The maximum and minimum relative error of the optical platform radiation model are 34.37% and 3.57%, respectively, and the average relative error is 11.45%. Cultivation experiments showed that under the same light intensity treatment, there was no significant difference in seedling phenotype parameters and strong seedling index by comparing the precision light cultivation platform with the traditional high floor fixed platform.【Conclusion】The precision light cultivation platform of seedlings based on PLC technology realizes automatic lifting and intelligent dimming, and saves up to 40.65% power without affecting the growth of seedlings.

Accurate litchi identification is of great significance for orchard yield estimations. Litchi in natural scenes have large differences in scale and are occluded by leaves, reducing the accuracy of litchi detection models. Adopting traditional horizontal bounding boxes will introduce a large amount of background and overlap with adjacent frames, resulting in a reduced litchi detection accuracy. Therefore, this study innovatively introduces the use of the rotation detection box model to explore its capabilities in scenarios with occlusion and small targets. First, a dataset on litchi rotation detection in natural scenes is constructed. Secondly, three improvement modules based on YOLOv8n are proposed: a transformer module is introduced after the C2f module of the eighth layer of the backbone network, an ECA attention module is added to the neck network to improve the feature extraction of the backbone network, and a 160 × 160 scale detection head is introduced to enhance small target detection. The test results show that, compared to the traditional YOLOv8n model, the proposed model improves the precision rate, the recall rate, and the mAP by 11.7%, 5.4%, and 7.3%, respectively. In addition, four state-of-the-art mainstream detection backbone networks, namely, MobileNetv3-small, MobileNetv3-large, ShuffleNetv2, and GhostNet, are studied for comparison with the performance of the proposed model. The model proposed in this article exhibits a better performance on the litchi dataset, with the precision, recall, and mAP reaching 84.6%, 68.6%, and 79.4%, respectively. This research can provide a reference for litchi yield estimations in complex orchard environments.