In order to realize the real-time and accurate detection of potted flowers on benches, in this paper we propose a method based on the ZED 2 stereo camera and the YOLO V4-Tiny deep learning algorithm for potted flower detection and location. First, an automatic detection model of flowers was established based on the YOLO V4-Tiny convolutional neural network (CNN) model, and the center points on the pixel plane of the flowers were obtained according to the prediction box. Then, the real-time 3D point cloud information obtained by the ZED 2 camera was used to calculate the actual position of the flowers. The test results showed that the mean average precision (MAP) and recall rate of the training model was 89.72% and 80%, respectively, and the real-time average detection frame rate of the model deployed under Jetson TX2 was 16 FPS. The results of the occlusion experiment showed that when the canopy overlap ratio between the two flowers is more than 10%, the recognition accuracy will be affected. The mean absolute error of the flower center location based on 3D point cloud information of the ZED 2 camera was 18.1 mm, and the maximum locating error of the flower center was 25.8 mm under different light radiation conditions. The method in this paper establishes the relationship between the detection target of flowers and the actual spatial location, which has reference significance for the machinery and automatic management of potted flowers on benches.

Lettuce is an important vegetable in horticulture, but information about the interactive effects of light and temperature on its photosynthetic characteristics was inadequate. This work investigated the effects of three temperatures (15/10 (T15), 23/18 (T23), and 30/23 °C (T30)) and five light treatments (100 (P100), 200 (P200), 350 (P350), 500 (P500), and 600 μmol·m⁻²·s⁻¹ (P600)) on the light–response curves, chlorophyll content, and yield of lettuce. The results showed that the maximum photosynthetic rate, light saturation point, chlorophyll content, and yield of lettuce were all the highest at T23 compared with T15 and T30 under different light intensities. Under the same temperature conditions, the photosynthesis capacity and yield of lettuce in the P350 and P500 treatments at T15, P350, P500, and P600 treatments at T23 and P500, and P600 treatments at T30 were larger than other light treatments. The results suggested that temperature play had a more pronounced influence on photosynthesis and yield in lettuce, but the appropriate levels of light intensity improved its potential photosynthetic capacity and yield under different temperature conditions.

Three-dimensional (3D) plant canopy structure analysis is an important part of plant phenotype studies. To promote the development of plant canopy structure measurement based on 3D reconstruction, we reviewed the latest research progress achieved using visual sensors to measure the 3D plant canopy structure from four aspects, including the principles of 3D plant measurement technologies, the corresponding instruments and specifications of different visual sensors, the methods of plant canopy structure extraction based on 3D reconstruction, and the conclusion and promise of plant canopy measurement technology. In the current research phase on 3D structural plant canopy measurement techniques, the leading algorithms of every step for plant canopy structure measurement based on 3D reconstruction are introduced. Finally, future prospects for a standard phenotypical analytical method, rapid reconstruction, and precision optimization are described.

Fruit biomechanics is needed for quality determination, multiscale modelling and engineering design of fruit processes and equipments. However, these determined fruit biomechanics data often have obvious differences for the same fruit or tissue. In order to investigate it, the fruit biomechanics based on anatomy was reviewed in this paper. First, the anatomical characteristics of fruit biomaterials were described at the macroscopic ‘tissue’ level and microscopic ‘cellular’ level. Subsequently, the factors affecting fruit biomechanics based on anatomy and the relationships between fruit biomechanics, texture and mechanical damage were summarised according to the published literature. Fruit biomechanics is mainly affected by size, number and arrangement of cells, quantity and volume of intracellular spaces, structure, thickness, chemical composition and permeability of cell walls, and pectin degradation level and turgor pressure within cells based on microanatomy. Four test methods and partial determined results of fruit biomechanics were listed and reviewed. The determined mechanical properties data of fruit are only approximate values by using the existing four test methods, owing to the fruit biomaterials being non-homogeneous and living. Lastly, further aspects for research on fruit biomechanics were proposed for the future.

Accurate estimation of reference evapotranspiration (ET_o) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a standard to calculate ET_o. However, FAO PM56 cannot be used with limited meteorological variables, so it is compulsory to choose an alternative model for ET_o estimation, which requires fewer variables. This study built ten machine learning (ML) models based on multi-function, neural network, and tree-based structure against the FAO PM56 method. For this purpose, time series temperature data on a monthly scale are only used to train ML models. The developed ML models were applied to estimate ET_o at different test stations and the obtained results were compared with the FAO PM56 method to verify and validate their performance in ET_o estimation for the selected stations. In addition, multiple statistical indicators, including root-mean-square error (RMSE), coefficient of determination (R²), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (r) were calculated to compare the performance of each ML model on ET_o estimation. Among the applied ML models, the ET_o tree boost (TB) ML model outperformed the other ML models in estimating ET_o in diverse climatic conditions based on statistical indicators (R², NSE, r, RMSE, and MAE). Moreover, the observed R², NSE, and r were the highest for the TB ML model, while RMSE and MAE were found to be the lowest at the study sites compared to other applied ML models. Lastly, ET_o point data yielded from the TB ML model was used in an interpolation process to create monthly and annual ET_o maps. Based on the ET_o maps, this study suggests mainly a focus on areas with high ET_o values and proper irrigation scheduling of crops to ensure water sustainability.