Additive manufacturing (AM), also known as three-dimensional (3D) printing, is a manufacturing technology that constructs objects by sequentially adding material layer by layer. AM encompasses a range of different techniques capable of working with very different materials from metals and alloys to polymers and composites. As an advanced fabrication technology, AM is characterized by strong design flexibility, the ability to create intricate structures, and cost-effectiveness when compared to conventional fabrication methods. AM technology is widely employed in various sectors such as aerospace, healthcare, and industrial manufacturing, and its application is increasingly expanding into agricultural manufacturing. This study provides a comprehensive review and analysis of the current status of AM technology applied in the five main agricultural manufacturing aspects such as the application of AM technology in the manufacturing of agricultural equipment parts, its use in agricultural sensors, its role in the utilization of agricultural waste, its application in the field of plant growth mechanisms and in phytoremediation tissues. The current existing problems of AM technology and future development trends are also included to provide the implications for researchers. The adoption of AM technology in agriculture offers significant advantages, including enhanced production efficiency, cost reduction, innovation facilitation, and environmental protection. From initial prototyping to custom production today, AM technology provides more flexible, efficient and sustainable solutions for agricultural manufacturing. Especially in the fields of agricultural machinery, planting facilities and agricultural biomaterials, the application of AM technology has shown great potential and advantages. With the continuous advancement in technology and the reduction of costs, AM technology will become more popular and play a more vital role in agricultural manufacturing. In the future, we can foresee that AM will realize the manufacturing of agricultural products with higher precision, a more complex structure and more functions, providing more intelligent and personalized solutions for agricultural production. As such, it is emerging as a critical driving force in the advancement of precision agriculture.

Accurate and rapid detection of tea shoots within the tea canopy is essential for achieving the automatic picking of famous tea. The current detection models suffer from two main issues: low inference speed and difficulty in deployment on movable platforms, which constrain the development of intelligent tea picking equipment. Furthermore, the detection of tea canopy shoots is currently limited to natural daylight conditions, with no reported studies on detecting tea shoots under artificial light during the nighttime. Developing an all-day tea picking platform would significantly improve the efficiency of tea picking. In view of these problems, the research objective was to propose an all-day lightweight detection model for tea canopy shoots (TS-YOLO) based on YOLOv4. Firstly, image datasets of tea canopy shoots sample were collected under low light (6:30–7:30 and 18:30–19:30), medium light (8:00–9:00 and 17:00–18:00), high light (11:00–15:00), and artificial light at night. Then, the feature extraction network of YOLOv4 and the standard convolution of the entire network were replaced with the lightweight neural network MobilenetV3 and the depth-wise separable convolution. Finally, to compensate for the lack of feature extraction ability in the lightweight neural network, a deformable convolutional layer and coordinate attention modules were added to the network. The results showed that the improved model size was 11.78 M, 18.30% of that of YOLOv4, and the detection speed was improved by 11.68 FPS. The detection accuracy, recall, and AP of tea canopy shoots under different light conditions were 85.35%, 78.42%, and 82.12%, respectively, which were 1.08%, 12.52%, and 8.20% higher than MobileNetV3-YOLOv4, respectively. The developed lightweight model could effectively and rapidly detect tea canopy shoots under all-day light conditions, which provides the potential to develop an all-day intelligent tea picking platform.

Review of air disturbance technology for plant frost protection

Cold injury (CI) causes irreversible damage to tea plants, which results in decline in the quality of famous teas and huge economic loss. A new, quick, non-destructive method is provided to assess the CI of tea leaf based on terahertz (THz) spectroscopy. Absorbance of the samples was measured with THz spectroscopy in frequency bands from 0.1 to 1.6 THz under low temperature treatments of 4.0, 0, −2.5, −5.0, −7.5, and −10.0 °C. Fast Fourier transformation was explored to decompose the endpoint signal to improve the ratio of signal to air and convert the time-domain spectra to the corresponding frequency-domain spectra. To improve the separation of overlap signals caused by substantial scattering of injured cells in the leaf, two-dimensional correlation spectroscopy (2DCOS) and average intensity (AI) were introduced into the partial least squares regression (PLSR) to build 2DCOS–PLSR and AI–PLSR models. Quantitative assessments of the 2DCOS–PLSR and AI–PLSR models were conducted to evaluate the three models. The assessment results showed that the correlation coefficients of the 2DCOS–PLSR model (R_2D) were 0.7873, 0.8305, and 0.9103, respectively. The root mean square errors of the 2DCOS–PLSR model (RMSE_2D) were 0.6032, 0.5763, and 0.5221, respectively. For the AI–PLSR model, R_AI values were 0.7477, 0.7691, and 0.8974, respectively. RMSE_AI values were 0.6038, 0.5962, and 0.5797. The combination of THz spectroscopy with the 2DCOS–PLSR model provided a better benchmark for the input interval selection and improved the accuracy of cold-injury detection results.