Scopus Publications
- AI-powered UAV remote sensing for drought stress phenotyping: Automated chlorophyll estimation in individual plants using deep learning and instance segmentation
Zhuhao Shen, Huichun Zhang, Liming Bian, Lei Zhou, Qifei Tian, Yufeng Ge
Expert Systems with Applications, 2026 - Path Planning for a Cartesian Apple Harvesting Robot Using the Improved Grey Wolf Optimizer
Dachen Wang, Huiping Jin, Chun Lu, Xuanbo Wu, Qing Chen, Lei Zhou, Xuesong Jiang, Hongping Zhou
Agronomy, 2026
As a high-value fruit crop grown worldwide, apples require efficient harvesting solutions to maintain a stable supply. Intelligent harvesting robots represent a promising approach to address labour shortages. This study introduced a Cartesian robot integrated with a continuous-picking end-effector, providing a cost-effective and mechanically simpler alternative to complex articulated arms. The system employed a hand–eye calibration model to enhance positioning accuracy. To overcome the inefficiencies resulting from disordered harvesting sequences and excessive motion trajectories, the harvesting process was treated as a travelling salesman problem (TSP). The conventional fixed-plane return trajectory of Cartesian robots was enhanced using a three-dimensional continuous picking path strategy based on a fixed retraction distance (H). The value of H was determined through mechanical characterization of the apple stem’s brittle fracture, which eliminated redundant horizontal displacements and improved operational efficiency. Furthermore, an improved grey wolf optimizer (IGWO) was proposed for multi-fruit path planning. Simulations demonstrated that the IGWO achieved shorter path lengths compared to conventional algorithms. Laboratory experiments validated that the system successfully achieved vision-based localization and fruit harvesting through optimal path planning, with a fruit picking success rate of 89%. The proposed methodology provides a practical framework for automated continuous harvesting systems. - High-throughput Phenotyping Systems for Tracking Nutrient Stress Response of Pinus Ettiottii
Nongye Jixie Xuebao Transactions of the Chinese Society for Agricultural Machinery, 2026 - From pixels to points: An AI framework with weaker-and-fewer-labels for lightweight 3D phenotyping using 2D-3D coordinate mapping and VLMs
Lei Zhou, Yingjun Xu, Chu Zhang, Xiya Zhang, Qian Wu, Liming Bian, Osama Elsherbiny, Huichun Zhang
Artificial Intelligence in Agriculture, 2026
3D phenotyping of seedlings is crucial to tomato cultivation in greenhouse facilities. Current studies focus on high-quality point cloud reconstruction and artificial intelligence (AI) 3D segmentation to derive phenotypic traits like plant height and crown width, which heavily rely on manual annotation and possess high complexity in deployment. This study proposes a novel AI framework from pixels to points, for efficient 3D plant phenotyping of tomato seedlings. Through the integration of 2D-3D coordinate mapping and AI vision language models, the proposed method enables accurate reconstruction and analysis of 3D phenotypic traits from single-view data. Top-down RGB images and corresponding point clouds with spatial alignment are captured using a binocular camera. Vision language models are employed with the text prompt “plant” to automatically generate bounding boxes and masks, thereby minimizing manual annotation. These outputs are further transferred to a lightweight YOLO11-segment model. The core innovation is established in our 2D-3D mapping strategy, through which plant-specific 3D points are efficiently extracted using only 2D masks. Non-plant points within initial masks are repurposed to determine ground height for improved plant height estimation, while masks are refined using the Excess Green Index to enhance crown width measurement. An mAP₅₀ of 96.0% is achieved by the YOLO11-segment model. Concerning sparse canopy, highly accurate results are yielded by our phenotyping approach, with RMSE values of 1.7 cm for plant height and 1.0 cm for crown width, and R 2 values of 0.93 and 0.95 against manual measurements. For dense canopy, the usage of a reference chessboard improves the performance (RMSE was reduced from 9.57 cm to 2.07 cm). Annotation dependency is significantly reduced, computational complexity is decreased, edge deployment is supported, and efficient technology transfer is enabled by the presented method. Considerable potential is offered for high-throughput screening of elite tomato varieties with desirable agronomic traits. • Real-time low-cost 3D phenotyping of tomato plants is proposed. • Weak labels simplify the 3D plant segmentation. • Segment the 3D point cloud using 2D pixel-masks with spatial alignment. • Vision language models and knowledge transfer further simplify the AI application. - Design of an intelligent Camellia oleifera fruit picking robot system with a dual-track transport
Jipeng Chen, Kai Wang, Lei Zhou, Hongping Zhou
Smart Agricultural Technology, 2025
Camellia oleifera is an economically important tree species with high utilization value. At present, Camellia oleifera fruit is faced with problems such as high labor intensity and shortage of human resources through manual picking. How to realize intelligent and mechanized picking of Camellia oleifera fruit is attracting more and more attention. In this paper, a dual-track intelligent picking robot system for Camellia oleifera fruit is proposed. The system is composed of walking device, multi-axis motion platform, cooperative robot, and vision units. Firstly, the overall structure and working principle of the picking system are introduced, and the structure and design methods of the main parts such as walking device, cooperative robot, end-effector, multi-axis motion platform and dual-track are described. Secondly, a positioning method of walking device based on improved YOLOv8 is employed for the trunk of Camellia oleifera tree. A fruit picking method based on GroundingDINO visual large model is proposed for Camellia oleifera fruit recognition. Thirdly, the control unit is designed, and the control flow of the dual-track intelligent picking robot system is analyzed. Finally, the prototype of the picking robot system is completed and the prototype operation test is carried out. The results show that the designed dual-track picking system can carry a robot to complete the picking of Camellia oleifera fruit in outdoor environment, which provides a new idea for intelligent and mechanized picking of Camellia oleifera fruit. - Classification of rice varieties using hyperspectral imaging with multi-dimensional fusion convolutional neural networks
Chen Jin, Lei Zhou, Yiying Zhao, Hengnian Qi, Xiaoping Wu, Chu Zhang
Journal of Food Composition and Analysis, 2025 - Walnut tree 3D posture determination and positioning for vibration harvesting using AI-binocular vision
Minghong Shi, Hongping Zhou, Lei Zhou, Shouxiang Jin, Feng Tan, Jingwei Jiang, Daojiong Wang, Linyun Xu
Smart Agricultural Technology, 2025
As a globally important crop, walnut has multiple values in terms of food security, ecology and economy. Its efficient harvesting technology is crucial to increase yield and reduce labor costs, but the existing vibratory harvesting equipment relies too much on manual adjustment of operating parameters, resulting in low efficiency, poor adaptability, and difficulty in adapting to the characteristics of different tree trunks. This study innovatively proposes a three-dimensional trunk localization and attitude estimation method based on two-dimensional deep learning image processing and binocular vision to help upgrade the intelligent walnut vibration harvesting technology. In this study, a trunk bounding box-based SAM model is used to generate weakly supervised labels, train a lightweight YOLOv11 segmentation model, and extract the target trunk region from a 3D point cloud via a 2D mask, which is combined with skeleton analysis to realize trunk localization and pose estimation. The experimental results show that after the introduction of 5-meter depth threshold filtering, the 2D segmentation AP50 reaches 99.5% (the model is only 6MB), the average absolute error of 3D localization is <4cm, and the attitude estimation error is 1.2°, which verifies the high accuracy and embedded deployment feasibility. The weakly supervised strategy of integrating SAM and YOLOv11 effectively solves the problems of high annotation cost and model lightweighting, while the integration of binocular vision and 2D segmentation circumvents the limitations of traditional 2D attitude estimation without complex 3D point cloud processing. The method can support the intelligent clamping decision of walnut vibration harvesting equipment. - Deep learning-based regression of food quality attributes using near-infrared spectroscopy and hyperspectral imaging: A review
Yuxin Xiao, Lei Zhou, Yiying Zhao, Hengnian Qi, Yuanyuan Pu, Chu Zhang
Food Chemistry, 2025 - Applications of deep learning in tea quality monitoring: a review
Tao Wu, Lei Zhou, Yiying Zhao, Hengnian Qi, Yuanyuan Pu, Chu Zhang, Yufei Liu
Artificial Intelligence Review, 2025
Tea is a popular beverage which can offer numerous benefits to human health and support the local economy. There is an increasing demand for accurate and rapid tea quality evaluation methods to ensure that the quality and safety of tea products meet the customers’ expectations. Advanced sensing technologies in combination with deep learning (DL) offer significant opportunities to enhance the efficiency and accuracy for tea quality evaluation. This review aims to summarize the application of DL technologies for tea quality assessment in three stages: cultivation, tea processing, and product evaluation. Various state-of-the-art sensing technologies (e.g., computer vision, spectroscopy, electronic nose and tongue) have been used to collect key data (images, spectral signals, aroma profiles) from tea samples. By utilizing DL models, researchers are able to analyze a wide range of tea quality attributes, including tea variety, geographical origin, quality grade, fermentation stage, adulteration level, and chemical composition. The findings from this review indicate that DL, with its end-to-end analytical capability and strong generalization performance, can serve as a powerful tool to support various sensing technologies for accurate tea quality detection. However, several challenges remain, such as limited sample availability for data training, difficulties for fusing data from multiple sources, and lack of interpretability of DL models. To this end, this review proposes potential solutions and future studies to address these issues, providing practical considerations for tea industry to effectively uptake new technologies and to support the development of the tea industry. - Precision farming: Using an IoT multimodal data-driven deep network to optimize irrigation in wheat crops
Osama Elsherbiny, Lei Zhou, Yong He, Zhengjun Qiu
Expert Systems with Applications, 2025 - Automatic segmentation and hyperspectral information extraction of Camellia oleifera fruits based on CA-TransUNet++ under complex natural environment
Weidong Yuan, Hongping Zhou, Cong Zhang, Lei Zhou, Xuesong Jiang, Hongzhe Jiang
Computers and Electronics in Agriculture, 2025 - Field-based phenotyping for poplar seedlings biomass evaluation based on zero-shot segmentation with multimodal UAV images
Qifei Tian, Huichun Zhang, Liming Bian, Lei Zhou, Zhuhao Shen, Yufeng Ge
Computers and Electronics in Agriculture, 2025 - CO-YOLO: A lightweight and efficient model for Camellia oleifera fruit object detection and posture determination
Shouxiang Jin, Lei Zhou, Hongping Zhou
Computers and Electronics in Agriculture, 2025 - Integrating sensor fusion with machine learning for comprehensive assessment of phenotypic traits and drought response in poplar species
Ziyang Zhou, Huichun Zhang, Liming Bian, Lei Zhou, Yufeng Ge
Plant Biotechnology Journal, 2025 - Detection of Soluble Solid Content in Citrus Fruits Using Hyperspectral Imaging with Machine and Deep Learning: A Comparative Study of Two Citrus Cultivars
Yuxin Xiao, Yuanning Zhai, Lei Zhou, Yiming Yin, Hengnian Qi, Chu Zhang
Foods, 2025 - Application of deep learning for high-throughput phenotyping of seed: a review
Chen Jin, Lei Zhou, Yuanyuan Pu, Chu Zhang, Hengnian Qi, Yiying Zhao
Artificial Intelligence Review, 2025 - Simultaneously predicting SPAD and water content in rice leaves using hyperspectral imaging with deep multi-task regression and transfer component analysis
Yuanning Zhai, Jun Wang, Lei Zhou, Xincheng Zhang, Yun Ren, Hengnian Qi, Chu Zhang
Journal of the Science of Food and Agriculture, 2025 - Development and experiment of screw end effector for picking camellia fruit
Journal of Forestry Engineering, 2025 - Accurate plant 3D reconstruction and phenotypic traits extraction via stereo imaging and multi-view point cloud alignment
Zhencan Wang, Huichun Zhang, Liming Bian, Lei Zhou, Yufeng Ge
Frontiers in Plant Science, 2025 - Deep Learning-Based Monitoring of Nutrient Content in Pear Trees
Guang Pu Xue Yu Guang Pu Fen Xi Spectroscopy and Spectral Analysis, 2024 - Poplar seedling varieties and drought stress classification based on multi-source, time-series data and deep learning
Lu Wang, Huichun Zhang, Liming Bian, Lei Zhou, Shengyi Wang, Yufeng Ge
Industrial Crops and Products, 2024 - Plant phenotyping for drought-stressed poplar saplings using few-shot learning and skeleton extraction algorithm
Nongye Gongcheng Xuebao Transactions of the Chinese Society of Agricultural Engineering, 2024 - 3D positioning of Camellia oleifera fruit-grabbing points for robotic harvesting
Lei Zhou, Shouxiang Jin, Jinpeng Wang, Huichun Zhang, Minghong Shi, HongPing Zhou
Biosystems Engineering, 2024 - Evaluating drought stress response of poplar seedlings using a proximal sensing platform via multi-parameter phenotyping and two-stage machine learning
Xuexing Fan, Huichun Zhang, Lei Zhou, Liming Bian, Xiuliang Jin, Luozhong Tang, Yufeng Ge
Computers and Electronics in Agriculture, 2024 - A novel method combining deep learning with the Kennard–Stone algorithm for training dataset selection for image-based rice seed variety identification
Chen Jin, Xinyue Zhou, Mengyu He, Cheng Li, Zeyi Cai, Lei Zhou, Hengnian Qi, Chu Zhang
Journal of the Science of Food and Agriculture, 2024 - Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants
Mohamed Farag Taha, Hanping Mao, Samar Mousa, Lei Zhou, Yafei Wang, Gamal Elmasry, Salim Al-Rejaie, Abdallah Elshawadfy Elwakeel, Yazhou Wei, Zhengjun Qiu
Agronomy, 2024 - Visible/near-infrared Spectroscopy and Hyperspectral Imaging Facilitate the Rapid Determination of Soluble Solids Content in Fruits
Yiying Zhao, Lei Zhou, Wei Wang, Xiaobin Zhang, Qing Gu, Yihang Zhu, Rongqin Chen, Chu Zhang
Food Engineering Reviews, 2024 - Detection and Instance Segmentation of Grape Clusters in Orchard Environments Using an Improved Mask R-CNN Model
Xiang Huang, Dongdong Peng, Hengnian Qi, Lei Zhou, Chu Zhang
Agriculture Switzerland, 2024 - Defects recognition of pine nuts using hyperspectral imaging and deep learning approaches
Dongdong Peng, Chen Jin, Jun Wang, Yuanning Zhai, Hengnian Qi, Lei Zhou, Jiyu Peng, Chu Zhang
Microchemical Journal, 2024 - Leafy vegetable freshness identification using hyperspectral imaging with deep learning approaches
Mengyu He, Cheng Li, Zeyi Cai, Hengnian Qi, Lei Zhou, et al.
Infrared Physics and Technology, 2024 - Poplar leaf phenotyping methods using zero-shot deep learning
Journal of Forestry Engineering, 2024 - A Zero-Shot Deep Learning-Supported Sensing System for Crop Seeds and Berries Phenotyping
Lei Zhou, Huichun Zhang, Liming Bian, Yiying Zhao, Qinlin Xiao
IEEE Sensors Journal, 2024 - Three-Dimensional Quantification and Visualization of Leaf Chlorophyll Content in Poplar Saplings under Drought Using SFM-MVS
Qifei Tian, Huichun Zhang, Liming Bian, Lei Zhou, Yufeng Ge
Forests, 2024 - Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis
Lei Zhou, Huichun Zhang, Liming Bian, Ye Tian, Haopeng Zhou
Plant Phenomics, 2024 - Assessment of Poplar Drought Stress Level Based on 1DCNN Fusion of Multi-source Phenotypic Data
Nongye Jixie Xuebao Transactions of the Chinese Society for Agricultural Machinery, 2024 - Application of deep learning in laser-induced breakdown spectroscopy: a review
Chu Zhang, Lei Zhou, Fei Liu, Jing Huang, Jiyu Peng
Artificial Intelligence Review, 2023 - Leaf water content determination of oilseed rape using near-infrared hyperspectral imaging with deep learning regression methods
Chu Zhang, Cheng Li, Mengyu He, Zeyi Cai, Zhongping Feng, Hengnian Qi, Lei Zhou
Infrared Physics and Technology, 2023 - Classification and recognition of camellia oleifera fruit in the field based on transfer learning and YOLOv8n
Nongye Gongcheng Xuebao Transactions of the Chinese Society of Agricultural Engineering, 2023 - Recognition of camellia oleifera fruits in natural environment using multi-modal images
Nongye Gongcheng Xuebao Transactions of the Chinese Society of Agricultural Engineering, 2023 - High-throughput instance segmentation and shape restoration of overlapping vegetable seeds based on sim2real method
Ning Liang, Sashuang Sun, Lei Zhou, Nan Zhao, Mohamed Farag Taha, Yong He, Zhengjun Qiu
Measurement Journal of the International Measurement Confederation, 2023 - Deep learning-based ranging error mitigation method for UWB localization system in greenhouse
Ziang Niu, Huizhen Yang, Lei Zhou, Mohamed Farag Taha, Yong He, Zhengjun Qiu
Computers and Electronics in Agriculture, 2023 - Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review
Yuanning Zhai, Lei Zhou, Hengnian Qi, Pan Gao, Chu Zhang
Plant Phenomics, 2023 - Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning
Lei Zhou, Qinlin Xiao, Mohanmed Farag Taha, Chengjia Xu, Chu Zhang
Plant Phenomics, 2023 - Erratum: Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning (Plant Phenomics (2023) 5 (0022) DOI: 10.34133/plantphenomics.0022)
Lei Zhou, Qinlin Xiao, Mohamed Farag Taha, Chengjia Xu, Chu Zhang
Plant Phenomics, 2023 - Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves
Qinlin Xiao, Na Wu, Wentan Tang, Chu Zhang, Lei Feng, Lei Zhou, Jianxun Shen, Ze Zhang, Pan Gao, Yong He
Frontiers in Plant Science, 2022 - A novel hybrid deep network for diagnosing water status in wheat crop using IoT-based multimodal data
Osama Elsherbiny, Lei Zhou, Yong He, Zhengjun Qiu
Computers and Electronics in Agriculture, 2022 - A Method to Study the Influence of the Pesticide Load on the Detailed Distribution Law of Downwash for Multi-Rotor UAV
Fengbo Yang, Hongping Zhou, Yu Ru, Qing Chen, Lei Zhou
Agriculture Switzerland, 2022 - Development of an automatic pest monitoring system using a deep learning model of DPeNet
Nan Zhao, Lei Zhou, Ting Huang, Mohamed Farag Taha, Yong He, Zhengjun Qiu
Measurement Journal of the International Measurement Confederation, 2022 - Powdery Food Identification Using NIR Spectroscopy and Extensible Deep Learning Model
Lei Zhou, Xuefei Wang, Chu Zhang, Nan Zhao, Mohamed Farag Taha, Yong He, Zhengjun Qiu
Food and Bioprocess Technology, 2022 - Using Machine Learning for Nutrient Content Detection of Aquaponics-Grown Plants Based on Spectral Data
Mohamed Farag Taha, Ahmed Islam ElManawy, Khalid S. Alshallash, Gamal ElMasry, Khadiga Alharbi, Lei Zhou, Ning Liang, Zhengjun Qiu
Sustainability Switzerland, 2022 - Hyperspectral imaging coupled with CNN: A powerful approach for quantitative identification of feather meal and fish by-product meal adulterated in marine fishmeal
Dandan Kong, Yongqiang Shi, Dawei Sun, Lei Zhou, Wenkai Zhang, Ruicheng Qiu, Yong He
Microchemical Journal, 2022 - Recent Advances of Smart Systems and Internet of Things (IoT) for Aquaponics Automation: A Comprehensive Overview
Mohamed Farag Taha, Gamal ElMasry, Mostafa Gouda, Lei Zhou, Ning Liang, Alwaseela Abdalla, David Rousseau, Zhengjun Qiu
Chemosensors, 2022 - Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics
Mohamed Farag Taha, Alwaseela Abdalla, Gamal ElMasry, Mostafa Gouda, Lei Zhou, Nan Zhao, Ning Liang, Ziang Niu, Amro Hassanein, Salim Al-Rejaie, Yong He, Zhengjun Qiu
Chemosensors, 2022 - A portable NIR-system for mixture powdery food analysis using deep learning
Lei Zhou, Lehao Tan, Chu Zhang, Nan Zhao, Yong He, Zhengjun Qiu
Lwt, 2022 - Recent progress of nondestructive techniques for fruits damage inspection: a review
Yong He, Qinlin Xiao, Xiulin Bai, Lei Zhou, Fei Liu, Chu Zhang
Critical Reviews in Food Science and Nutrition, 2022 - End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses
Chu Zhang, Lei Zhou, Qinlin Xiao, Xiulin Bai, Baohua Wu, Na Wu, Yiying Zhao, Junmin Wang, Lei Feng
Plant Phenomics, 2022 - Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves
Qinlin Xiao, Wentan Tang, Chu Zhang, Lei Zhou, Lei Feng, Jianxun Shen, Tianying Yan, Pan Gao, Yong He, Na Wu
Plant Phenomics, 2022 - Development of a low-cost portable device for pixel-wise leaf SPAD estimation and blade-level SPAD distribution visualization using color sensing
Lehao Tan, Lei Zhou, Nan Zhao, Yong He, Zhengjun Qiu
Computers and Electronics in Agriculture, 2021 - Simultaneous determination of five micro-components in Chrysanthemum morifolium (Hangbaiju) using near-infrared hyperspectral imaging coupled with deep learning with wavelength selection
Juan He, Chu Zhang, Lei Zhou, Yong He
Infrared Physics and Technology, 2021 - Integration of visible and thermal imagery with an artificial neural network approach for robust forecasting of canopy water content in rice
Osama Elsherbiny, Lei Zhou, Lei Feng, Zhengjun Qiu
Remote Sensing, 2021 - Fusion of feature selection methods and regression algorithms for predicting the canopy water content of rice based on hyperspectral data
Osama Elsherbiny, Yangyang Fan, Lei Zhou, Zhengjun Qiu
Agriculture Switzerland, 2021 - Determination of leaf water content with a portable nirs system based on deep learning and information fusion analysis
Lei Zhou, Chu Zhang, Mohamed Farag Taha, Zhengjun Qiu, Yong He
Transactions of the Asabe, 2021 - Detection of adulteration in food based on nondestructive analysis techniques: a review
Yong He, Xiulin Bai, Qinlin Xiao, Fei Liu, Lei Zhou, Chu Zhang
Critical Reviews in Food Science and Nutrition, 2021 - Wheat Kernel Variety Identification Based on a Large Near-Infrared Spectral Dataset and a Novel Deep Learning-Based Feature Selection Method
Lei Zhou, Chu Zhang, Mohamed Farag Taha, Xinhua Wei, Yong He, Zhengjun Qiu, Yufei Liu
Frontiers in Plant Science, 2020 - Noise reduction in the spectral domain of hyperspectral images using denoising autoencoder methods
Chu Zhang, Lei Zhou, Yiying Zhao, Susu Zhu, Fei Liu, Yong He
Chemometrics and Intelligent Laboratory Systems, 2020 - Vision-based moving obstacle detection and tracking in paddy field using improved yolov3 and deep sort
Zhengjun Qiu, Nan Zhao, Lei Zhou, Mengcen Wang, Liangliang Yang, Hui Fang, Yong He, Yufei Liu
Sensors Switzerland, 2020 - Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral imaging
Chu Zhang, Wenyan Wu, Lei Zhou, Huan Cheng, Xingqian Ye, Yong He
Food Chemistry, 2020 - Information fusion of emerging non-destructive analytical techniques for food quality authentication: A survey
Lei Zhou, Chu Zhang, Zhengjun Qiu, Yong He
Trac Trends in Analytical Chemistry, 2020 - Application of wechat mini-program and Wi-Fi SoC in agricultural IoT: A low-cost greenhouse monitoring system
Lei Zhou, Zhengjun Qiu, Yong He
Transactions of the Asabe, 2020 - Detection of sulfite dioxide residue on the surface of fresh-cut potato slices using near-infrared hyperspectral imaging system and portable near-infrared spectrometer
Xiulin Bai, Qinlin Xiao, Lei Zhou, Yu Tang, Yong He
Molecules, 2020 - Application of Deep Learning in Food: A Review
Lei Zhou, Chu Zhang, Fei Liu, Zhengjun Qiu, Yong He
Comprehensive Reviews in Food Science and Food Safety, 2019 - Identification of soybean varieties using hyperspectral imaging coupled with convolutional neural network
Zhu, Zhou, Zhang, Bao, Wu, Chu, Yu, He, Feng
Sensors Switzerland, 2019 - Near-infrared hyperspectral imaging combined with deep learning to identify cotton seed varieties
Susu Zhu, Lei Zhou, Pan Gao, Yidan Bao, Yong He, Lei Feng
Molecules, 2019 - Detection of subtle bruises on winter jujube using hyperspectral imaging with pixel-wise deep learning method
Lei Feng, Susu Zhu, Lei Zhou, Yiying Zhao, Yidan Bao, Chu Zhang, Yong He
IEEE Access, 2019 - GaintKW: A measurement system of thousand kernel weight based on the Android platform
Wenhua Wu, Lei Zhou, Jian Chen, Zhengjun Qiu, Yong He
Agronomy, 2018
