The number of leaves in maize plant is one of the key traits describing its growth conditions. It is directly related to plant development and leaf counts also give insight into changing plant development stages. Compared with the traditional solutions which need excessive human interventions, the methods of computer vision and machine learning are more efficient. However, leaf counting with computer vision remains a challenging problem. More and more researchers are trying to improve accuracy. To this end, an automated, deep learning based approach for counting leaves in maize plants is developed in this paper. A Convolution Neural Network(CNN) is used to extract leaf features. The CNN model in this paper is inspired by Google Inception Net V3, which using multi-scale convolution kernels in one convolution layer. To compress feature maps generated from some middle layers in CNN, the Fisher Vector (FV) is used to reduce redundant information. Finally, these encoded feature maps are used to regress the leaf numbers by using Random Forests. To boost the related research, a relatively single maize image dataset (Different growth stage with 2845 samples, which 80% for train and 20% for test) is constructed by our team. The proposed algorithm in single maize data set achieves Mean Square Error (MSE) of 0.32.

The Cucumber Productivity Simulation Model (CPSM) was developed to precisely predict the dynamic process of cucumber productivity in a solar greenhouse. This research conducted a variety of sowing experiments and collected data on cucumber productivity and meteorological conditions from 2013 to 2015 and 2018 to 2020. Employing the principles of least squares, the relationship between cucumber productivity indicators and effective accumulated temperature (EAT) was fitted, determining key crop parameters and constructing the CPSM. Validation of the model was conducted using independent experimental data, evaluating its simulation accuracy. The results indicate that (1) CPSM can dynamically and meticulously simulate the formation process of different productivity indicators in cucumber. Normalized root mean square errors (NRMSE) ranged from 0.44% to 19.64%, and mean relative errors (MRE) ranged from 0.31% to 17.23% across different productivity indicator models. The models for organ water content, maximum root length, specific leaf area, and organ fresh weight distribution index demonstrated high simulation accuracy, while others showed relatively high accuracy. (2) Simulation accuracy varied with indicators and varieties. 19 indicators (34.55%) exhibited high simulation accuracy and 30 indicators (54.55%) showed relatively high accuracy. The JY35 variety (10.44 ± 8.49%) outperformed the JS206 variety (13.44 ± 8.50%) in terms of simulation accuracy. The JY35 variety had 39 superior productivity indicators (70.91%) while the JS206 variety had sixteen (29.09%). CPSM utilizes easily accessible temperature data as its input, allowing for precise and detailed simulation of productivity indicators for cucumber production in solar greenhouses. This research lays a theoretical foundation and provides technical support for guiding intelligent production management, efficient utilization of agricultural resources, and climate change productivity assessment in solar greenhouse cucumber production.

In order to investigate the stage plant architecture and productivity characteristics of different cucumber varieties and pruning methods and to construct a comprehensive productivity evaluation system based on plant architecture parameters, this study used JY35 and JS206 as experiment materials and conducted a dual factor control experiment with variety and pruning methods (single-stem pruning (SP) and natural growth (NG)) to systematically analyze the key phenotypic characteristics and productivity indicators of cucumbers at different developmental stages based on variance analysis and principal component evaluation. The results indicated the following: (1) Varieties and pruning methods have a significant impact on the plant architecture characteristics and productivity indicators. (2) The dominant plant architecture characteristics and productivity indicators of JY35 include dry and fresh weights of the tendril, main stem, total stem, leaves, petioles, flowers, overground parts, and overall plant, as well as dry and fresh weight distribution index of the tendril, total stem, leaves, petioles, flowers, overground parts, and overall plant, main stem fresh weight distribution index, water content of roots, tendrils, main stem, leaves, petioles, and flowers, volume of total stem, main stem, and petioles, plant height, total leaf area per plant, leaf area index, and specific leaf area. The remaining plant architecture characteristics and productivity indicators are dominated by the plant architecture of JS206. (3) The dominant plant architecture characteristics and productivity indicators of the SP method include dry and fresh weight distribution index of roots, fruit carpopodiums, main stems, and total stems, water content of petioles, stems, and leaves, and root-to-shoot ratio. The remaining plant architecture characteristics and productivity indicators are dominated by the NG method. This study quantified the dynamic correlation between cucumber plant architecture and productivity characteristics, and the research results can provide a morphological basis for facility cucumber variety breeding and theoretical support for optimizing pruning cultivation mode and achieving efficient utilization of light and heat resources.

The quantification of the relationship between morphological and color indicators in various organs of horticultural crops is of great significance for crop digital visualization research using computer vision technology. To study this relationship, observational data from a six-year experiment were collected, focusing on seven kinds of color component values of different organs including root, stem, and leaf. Using the collected color data as input, a simulation model was established based on the Elman neural network for six horticultural crops including zizania, cucumber, celery, spinach, parsley, and tea. Results indicated that the horticultural crop morphology model based on the Elman neural network exhibited high simulation accuracy with root mean square error (RMSE) ranging from 0.14 to 1.05 cm and normalized root mean square error (NRMSE) ranging from 2.02% to 11.34% for the maximum root length simulation model. The simulation model for stem length and diameter had an RMSE ranging from 1.42 to 4.96 cm and 0.25 to 1.17 mm, respectively, with NRMSE ranging from 18.19% to 25.65% and 15.13% to 27.25%, respectively. Similarly, chlorophyll content, leaf length, leaf width, and leaf area simulation models exhibited RMSE ranging from 2.80 to 8.22 SPAD, 0.44 to 18.04 cm, 0.22 to 3.49 cm, and 0.25 to 36.39 cm 2 , respectively, with NRMSE ranging from 8.63% to 21.04%, 15.00% to 22.87%, 15.12% to 33.58%, and 6.88% to 24.90%, respectively. These findings provide essential theoretical support for precision agriculture in areas of water and fertilizer management, plant growth diagnosis, and yield prediction.

Cucumber (Cucumis sativus L.) and celery (Apium graveolens L.) are among the most widely cultivated vegetable crops, belonging to the melon and leafy vegetable categories, respectively. This study aims to provide predictions for the quality formation of greenhouse cucumber and celery, as well as technical support for intelligent vegetable production management. Based on the light and temperature response characteristics of cucumber and celery growth and development and using the experimental varieties ‘Jinsheng 206’ for cucumber and ‘Juventus’ for celery, the observational data from a five-cropping season trial over 2 years were utilized. By analyzing the relationship between the growth and development of vegetables and key meteorological factors (radiation and temperature), simulation models for quality formation were constructed using the day–night temperature difference method (ATD) and the product of thermal effectiveness and the photosynthetically active method (TEP) as independent variables. The model parameters were determined and the model was validated using independent trial data. The results showed the following: (1) There exist quantifiable relationships between different quality indicators and ATD or TEP. These relationships are mainly presented through linear functions, exponential functions, logarithmic function, and logical functions. (2) The normalized root mean square error (NRMSE) of the cucumber quality model ranges from 1.13% to 29.53%, and the NRMSE of the celery quality model ranges from 1.63% to 31.47%. (3) Based on two kinds of normalization methods, the average NRMSE of the VQSM model is 13.72%, demonstrating a relatively high level of accuracy in simulation. These results demonstrate that the proposed model can dynamically and accurately simulate the quality formation process of vegetables, providing a theoretical basis and data support for the research on productivity and the economic benefits of greenhouse crops.