相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Food Science & Technology
Mohammad Nadimi et al.
Summary: This study proposes the use of radiographic imaging and deep learning tools to intelligently characterize the mechanical damage in flaxseeds. By extracting features and utilizing various classification algorithms, damaged seeds can be accurately classified.
FOOD AND BIOPROCESS TECHNOLOGY
(2023)
Article
Agriculture, Multidisciplinary
Luis-Enrique Montoya-Cavero et al.
Summary: In recent years, artificial intelligence and technological advances have improved the detection and localization performance of harvesting robots for agricultural produce. However, this improvement also brings challenges such as manual labeling of large datasets, lengthy training periods, and high costs of hardware. This study provides up-to-date information on the vision subsystems of harvesting robots, analyzes the challenges of introducing new technology, and discusses future trends.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Engineering, Multidisciplinary
Chiagoziem C. C. Ukwuoma et al.
Summary: Recent advances in computer vision have led to extensive applications in various fields, including agriculture. This article focuses on the use of deep learning models for fruit detection and classification, as conventional machine learning methods have limitations in handling the complexities of fruit characteristics. The article discusses datasets, descriptors, model implementations, challenges, and the results of deep learning methods for fruit identification and classification. In addition, a deep learning model using the Fruit 360 dataset is implemented to assist beginner researchers in understanding the role of deep learning in agriculture.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2022)
Article
Agriculture, Multidisciplinary
Piyanun Ruangurai et al.
Summary: With the development of precision agriculture technology, machine vision is being used to obtain accurate spatial information for control systems in a cost-effective manner. In the research reported here, a machine vision-based guidance system for a seeding tractor was developed and tested for rice planting in wet and puddled paddy fields. The system utilized the furrow/rut pattern created by the tractor's wheels as input for steering and velocity control. The results showed that using PCA as an initial estimate followed by iterative optimization of a new likelihood function achieved the best navigation accuracy.
PRECISION AGRICULTURE
(2022)
Article
Engineering, Multidisciplinary
Rabi Kabir Ahmad et al.
Summary: This paper focuses on the characteristics of coconut shell biomass, including chemical structure, energy potential, and morphological analysis. The study finds that coconut shells have important qualities for charcoal production, such as high density, high calorific value, low moisture content, and ash. Additionally, coconut shells contain trace amounts of complex heavy metals and sulfur/nitrogen, making them suitable for thermochemical conversion without pre-treatment. With its carbon-rich and environmentally friendly properties, coconut shell biomass can be used as an alternative energy source.
AIN SHAMS ENGINEERING JOURNAL
(2022)
Article
Chemistry, Applied
Xiang Li et al.
Summary: This study investigates the use of blended barley and green lentil flours in the production of puffed snacks, aiming to optimize their physical and microstructural properties. The findings suggest that higher extrusion temperature and specific blending ratios contribute to better expansion, texture, and overall quality of the snacks.
Article
Horticulture
S. Sabzi et al.
Summary: This study aimed to estimate the physicochemical properties of apples using video processing and artificial intelligence, achieving a high level of accuracy in determining the fruits' ripeness levels. The results demonstrated the effectiveness of machine vision systems in non-destructive evaluation of fruits.
INTERNATIONAL JOURNAL OF FRUIT SCIENCE
(2022)
Article
L.G. Divyanth et al.
Journal of Biosystems Engineering
(2022)
Article
Engineering, Chemical
Chitra Sivakumar et al.
Summary: This study compared the microstructure of four different pulse flours obtained from two milling methods. The distribution and content of protein, as well as the presence of damaged starch and fiber particles, were examined. The results showed that the microstructure and protein content of the pulse flours were influenced by both pulse type and milling method. These differences in microstructure and protein content can affect the functionality and quality of food products.
Article
Chemistry, Analytical
Kamran Kheiralipour et al.
Summary: The feasibility of estimating the ripeness levels of wild pistachio fruit using image processing and artificial intelligence techniques was evaluated in this study. A machine vision system achieved a high correct classification rate of up to 100% for different ripeness levels of the wild pistachios.
Article
Chemistry, Analytical
L. G. Divyanth et al.
Summary: This study utilized deep learning algorithms to evaluate the resistance of pea roots to Aphanomyces root rot (ARR). By implementing three effective data-balancing techniques, the classification accuracy of the model was improved, showing the potential for application in other image-based phenotyping datasets.
Article
Green & Sustainable Science & Technology
Mohammad Nadimi et al.
Summary: Recent research has shown that laser biostimulation can reverse the adverse effects of sub-optimal storage on crop germination. Dual-wavelength laser treatment was found to be the most effective in improving germination percentage, time, and speed in sub-optimally stored seeds. It also enhanced root length and seed weight in healthy seeds. However, the effect of red laser treatment was not significant.
Article
Computer Science, Artificial Intelligence
L. G. Divyanth et al.
Summary: This study aims to reduce the effort needed to prepare large image datasets for crop/weed identification by creating artificial images using generative adversarial networks (GANs). The fidelity of these synthetic images was tested and the results showed that image augmentation using GANs can improve the performance of crop/weed classification models. This method has the added advantage of reducing time and manpower in the process.
Article
Horticulture
Adel Hosainpour et al.
Summary: This study developed a machine vision system that combines image processing and artificial intelligence to evaluate the quality of white mulberry fruit. By extracting color and texture features and using artificial neural networks and support vector machine classifiers, the samples were successfully classified into high and low quality grades. The system achieved a 100% correct classification rate under both classifiers. The results confirm the suitability of machine vision as a reliable, low-cost, rapid, and intelligent tool for quality monitoring.
Article
Agricultural Engineering
Mohammad Nadimi et al.
Summary: Laser biostimulation is a safe and sustainable method for enhancing plant germination and growth. This study evaluated the effect of two low power portable lasers on the germination of Canada Western Red Winter wheat seeds. It was found that treatment with a dual-wavelength laser for 10 minutes significantly improved germination speed and growth.
APPLIED ENGINEERING IN AGRICULTURE
(2022)
Article
Agronomy
Chaiyan Sirikun et al.
Summary: Rice grain yield was estimated using a locally made combine harvester equipped with a specially developed sensing system, which collected data on grain mass flow rate, moisture content, and field information. The system, consisting of a yield meter, GNSS receiver, and customized software, showed potential for improving agricultural mechanization in Thailand. System performance was evaluated in three neighboring fields, with different cutter bar heights impacting yield estimation accuracy.
Article
Agricultural Engineering
Dandan Wang et al.
Summary: This study developed an accurate method for detecting apple fruitlets based on a channel pruned YOLO V5s deep learning algorithm, with a small model size. Experimental results showed that the method effectively detected apple fruitlets under different conditions, outperforming seven other methods.
BIOSYSTEMS ENGINEERING
(2021)
Article
Agriculture, Multidisciplinary
Xue Xia et al.
Summary: The number of flower buds on the apple tree is crucial for determining fruit load, making bud removal essential in apple tree pruning. Recent studies have shown the effectiveness of computer vision and deep learning in image recognition, leading to the development of a DCNN-based ADEN model for fine-grained classification of apple buds.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Agriculture, Multidisciplinary
Jia Kang et al.
Summary: The growth and distribution of root system in soil plays a crucial role in plant growth and crop production. This study focused on the segmentation of cotton mature root system using a semantic segmentation model with attention mechanism, which showed higher accuracy and efficiency compared to other models. The proposed model accurately distinguishes cotton roots from complex soil background, providing important theoretical value and practical application reference for deep learning in plant root segmentation.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Agriculture, Multidisciplinary
Wenxia Bao et al.
Summary: Manual diagnosis of crop diseases is costly and inefficient, prompting the development of a lightweight CNN model called SimpleNet for automatic identification of wheat ear diseases. SimpleNet incorporates features like Convolutional Block Attention Module and feature fusion module, achieving high identification accuracy of 94.1% on test data set, surpassing classic and lightweight CNN models.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Agriculture, Multidisciplinary
Xu Li et al.
Summary: This paper introduces a deep learning target detection algorithm based on Yolov4_tiny for green pepper detection, which uses an adaptive spatial feature pyramid method that combines an attention mechanism and multi-scale prediction to improve the recognition effect of occluded and small-target green peppers. The method achieves a high AP value of 95.11% in detecting green peppers, making it comparable to current state-of-the-art technology in green pepper detection models, and is suitable for real-time detection and embedded development needs of agricultural robots.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Agriculture, Multidisciplinary
Shifeng Dong et al.
Summary: This study introduces a new method for agricultural pest recognition, CRA-Net, which enhances the accuracy and localization for small pests by incorporating CRFPN and AA modules. Experimental results demonstrate that the proposed method achieves a high average precision.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Agriculture, Multidisciplinary
Ronghua Gao et al.
Summary: The study introduces a crop disease recognition model based on the DECA module, which achieves high accuracy by enhancing the attention mechanism and ResNet, demonstrating higher recognition accuracy on three datasets compared to other models.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Geography, Physical
Xueping Ni et al.
Summary: This study developed a complete framework for 3D segmentation of individual blueberries in clusters and successfully extracted blueberry cluster traits using photogrammetry and instance segmentation techniques. The accuracy of determining blueberry cluster traits was high, which could be used for fruit development monitoring, yield estimation, and harvest time prediction with great potential.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2021)
Article
Agricultural Engineering
Subramanian Parvathi et al.
Summary: An improved Faster R-CNN model was proposed for detecting maturity stages of coconuts, achieving better detection performance compared to other object detectors through training with collected and augmented coconut images.
BIOSYSTEMS ENGINEERING
(2021)
Review
Agriculture, Multidisciplinary
A. S. M. Mahmudul Hasan et al.
Summary: The rapid development of deep learning techniques has enabled efficient detection and classification of objects from images or videos, with applications in agriculture especially for weed management. Automated weed detection plays a key role in improving crop yields and fine-tuning pre-trained models on plant datasets has proven to achieve high accuracy.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Review
Optics
Mohammad Nadimi et al.
Summary: With the increasing world population, demands for food safety and security are expected to rise. In addition to traditional agricultural technologies, laser-based techniques are seen as a new method to enhance crop production and quality.
Article
Plant Sciences
Islas-Flores Ignacio et al.
Summary: Coconuts, cultivated in tropical regions worldwide, are highly valued for their edible components and natural fiber. The global market for coconut products is expanding, and the application of new technologies and research strategies can enhance the management and marketing of coconut crops. This review highlights the importance of research involving technologies and approaches to improve coconut fruit production and meet market demands.
SOUTH AFRICAN JOURNAL OF BOTANY
(2021)
Review
Environmental Sciences
Maryam Ouhami et al.
Summary: Crop diseases pose a serious issue in agriculture, impacting production quality and quantity. Technological advancements have shown great potential in disease control, with data fusion techniques from heterogeneous sources playing a key role in improving plant health prediction.
Article
Computer Science, Hardware & Architecture
Shaohua Wan et al.
Article
Agriculture, Multidisciplinary
Weihui Zeng et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2020)
Article
Chemistry, Analytical
Yue Mu et al.
Article
Agricultural Engineering
Longsheng Fu et al.
BIOSYSTEMS ENGINEERING
(2020)
Article
Agriculture, Multidisciplinary
Fangfang Gao et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2020)
Review
Agricultural Engineering
Juan P. Vasconez et al.
BIOSYSTEMS ENGINEERING
(2019)
Article
Agriculture, Multidisciplinary
Jordi Gene-Mola et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2019)
Review
Agriculture, Multidisciplinary
Anand Koirala et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2019)
Article
Computer Science, Information Systems
Xiaoyang Liu et al.
Proceedings Paper
Automation & Control Systems
Syed I. Moazzam et al.
2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION IN INDUSTRY (ICRAI)
(2019)
Proceedings Paper
Automation & Control Systems
Longsheng Fu et al.
Article
Computer Science, Information Systems
Li Zhang et al.
Article
Agricultural Engineering
Tien Thanh Nguyen et al.
BIOSYSTEMS ENGINEERING
(2016)
Review
Agriculture, Multidisciplinary
Yuanshen Zhao et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2016)
Article
Agriculture, Multidisciplinary
Chuanyuan Zhao et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2016)
Article
Chemistry, Analytical
Inkyu Sa et al.
Proceedings Paper
Computer Science, Theory & Methods
Akshay Prasad Dubey et al.
INTERNATIONAL CONFERENCE ON COMPUTATIONAL MODELLING AND SECURITY (CMS 2016)
(2016)
Article
Agriculture, Multidisciplinary
Jun Lu et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2015)
Review
Agriculture, Multidisciplinary
A. Gongal et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2015)
Article
Agricultural Engineering
H. K. Mebatsion et al.
BIOSYSTEMS ENGINEERING
(2011)
Article
Robotics
Tin Lun Lam et al.
IEEE TRANSACTIONS ON ROBOTICS
(2011)
