Yonglin Gao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yonglin Gao has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
Dongdong Liu
1 shared publication- Dongdong Liu
Xinjiang Shidaguoli Agricultural Science and Technology Co., Ltd., Alaer 843017, China
1 shared publication - Dongdong Liu1 shared publication
Jianting Wang
1 shared publicationTiebiao Zhao
1 shared publication- Tiebiao Zhao
Xinjiang Shidaguoli Agricultural Science and Technology Co., Ltd., Alaer 843017, China
1 shared publication
Latest Publications
- Open Access
- 3 June 2026
- Published by Elsevier in Agricultural Water Management
- Vol. 332
High-precision root zone soil moisture inversion for arid cotton fields using UAV-meteorological-physiological multi-source data fusion
- 3 June 2026
- Published by Elsevier in Agricultural Water Management
- Vol. 332
Open AccessHigh-precision root zone soil moisture inversion for arid cotton fields using UAV-meteorological-physiological multi-source data fusion
- Open Access
- 1 January 2025
- Published by Wiley in Complexity
- Vol. 2025 (1)
A Real‐Time Apple Maturity Detection Method Combining Lightweight Networks and Multiscale Attention Mechanisms
Apple maturity detection algorithms based on deep learning typically involve a large number of parameters, resulting in high computational costs, long processing times, and dependence on high computational power graphics processing units (GPUs). This paper proposes an improved YOLOv8 model to address the issues related to the maturity detection of Fuji apples grown in China using image‐based methods. The model was optimized in several ways according to the characteristics of apple targets and scenes. First, a lightweight MobileNetV3 is used as the backbone network, replacing the original CSPDarknet‐53 backbone network, which reduces the model parameters and computational complexity and increases the inference speed. Second, by introducing the efficient multiscale attention (EMA) module and using the bidirectional feature pyramid network (BiFPN) in the neck part, the model enhances the extraction capability of important features and suppresses redundant features, thus improving the model’s generalization ability. Experimental results show that the size of the model is 2.6 megabytes. On the apple dataset, its precision, recall, F1 score, and mean average precision reach 90.2%, 88.5%, 89.3%, and 91.3%, respectively, with improvements of 4.3%, 3.2%, 3.7%, and 2.6% compared to the original model. Based on this model, an Android application has been developed for real‐time apple maturity detection. The improved model proposed in this paper achieves real‐time apple target recognition and maturity detection, providing quick and accurate target recognition guidance for the mechanical automatic harvesting of apples.
- 1 January 2025
- Published by Wiley in Complexity
- Vol. 2025 (1)
Open AccessA Real‐Time Apple Maturity Detection Method Combining Lightweight Networks and Multiscale Attention Mechanisms
Apple maturity detection algorithms based on deep learning typically involve a large number of parameters, resulting in high computational costs, long processing times, and dependence on high computational power graphics processing units (GPUs). This paper proposes an improved YOLOv8 model to address the issues related to the maturity detection of Fuji apples grown in China using image‐based methods. The model was optimized in several ways according to the characteristics of apple targets and scenes. First, a lightweight MobileNetV3 is used as the backbone network, replacing the original CSPDarknet‐53 backbone network, which reduces the model parameters and computational complexity and increases the inference speed. Second, by introducing the efficient multiscale attention (EMA) module and using the bidirectional feature pyramid network (BiFPN) in the neck part, the model enhances the extraction capability of important features and suppresses redundant features, thus improving the model’s generalization ability. Experimental results show that the size of the model is 2.6 megabytes. On the apple dataset, its precision, recall, F1 score, and mean average precision reach 90.2%, 88.5%, 89.3%, and 91.3%, respectively, with improvements of 4.3%, 3.2%, 3.7%, and 2.6% compared to the original model. Based on this model, an Android application has been developed for real‐time apple maturity detection. The improved model proposed in this paper achieves real‐time apple target recognition and maturity detection, providing quick and accurate target recognition guidance for the mechanical automatic harvesting of apples.
- Open Access
A Cotton Leaf Water Potential Prediction Model Based on Particle Swarm Optimisation of the LS-SVM Model
Frequent monitoring of crop moisture levels can significantly improve crop production efficiency and optimise water resource utilisation. The aim of the present study was to generate moisture status maps using thermal infrared imagery, centring on the development of a predictive model for the cotton leaf water potential. The model was constructed using particle swarm optimisation (PSO) in conjunction with the least squares support vector machine (LS-SVM). Traditional SVM models suffer from high computational complexity, long training times, and inequality constraints in predicting leaf water potential. To address such issues, the PSO algorithm was introduced to improve the performance of the LS-SVM model. The PSO-optimised LS-SVM model exhibited notable improvements in performance when evaluated on two distinct test datasets (Alaer and Tumushuke). The research results indicate that the predictive accuracy of the PSO-LS-SVM model significantly improved, as evidenced by an increase of 0.05 and 0.04 in the values, both of which reached 0.95. This improvement is reflected in the corresponding RMSE values, which were reduced to 0.100 and 0.103. Furthermore, a model was established based on data from three cotton growth stages, achieving high predictive accuracy even with fewer training samples. By using the PSO-LS-SVM model to predict leaf water potential information, the predicted data were mapped onto drone images, enabling the transformation of the leaf water potential from a point to an area. The present findings contribute to a more comprehensive understanding of the cotton leaf water potential by visually representing the spatial distribution of crop water status on a large scale. The results hold substantial significance for the improvement of crop irrigation management.Open AccessA Cotton Leaf Water Potential Prediction Model Based on Particle Swarm Optimisation of the LS-SVM Model
Frequent monitoring of crop moisture levels can significantly improve crop production efficiency and optimise water resource utilisation. The aim of the present study was to generate moisture status maps using thermal infrared imagery, centring on the development of a predictive model for the cotton leaf water potential. The model was constructed using particle swarm optimisation (PSO) in conjunction with the least squares support vector machine (LS-SVM). Traditional SVM models suffer from high computational complexity, long training times, and inequality constraints in predicting leaf water potential. To address such issues, the PSO algorithm was introduced to improve the performance of the LS-SVM model. The PSO-optimised LS-SVM model exhibited notable improvements in performance when evaluated on two distinct test datasets (Alaer and Tumushuke). The research results indicate that the predictive accuracy of the PSO-LS-SVM model significantly improved, as evidenced by an increase of 0.05 and 0.04 in the values, both of which reached 0.95. This improvement is reflected in the corresponding RMSE values, which were reduced to 0.100 and 0.103. Furthermore, a model was established based on data from three cotton growth stages, achieving high predictive accuracy even with fewer training samples. By using the PSO-LS-SVM model to predict leaf water potential information, the predicted data were mapped onto drone images, enabling the transformation of the leaf water potential from a point to an area. The present findings contribute to a more comprehensive understanding of the cotton leaf water potential by visually representing the spatial distribution of crop water status on a large scale. The results hold substantial significance for the improvement of crop irrigation management.