The replacement of personal computer terminal with edge device is recognized as a portable and cost-effective potential solution in solving equipment miniaturization and achieving high flexibility of robotic fruit har...The replacement of personal computer terminal with edge device is recognized as a portable and cost-effective potential solution in solving equipment miniaturization and achieving high flexibility of robotic fruit harvesting at in-field scale.This study proposes a lightweight improved You Only Look Once version 8n(YOLOv8n)model for detecting Orah fruits and deploying this model on an edge device.First of all,the model size was reduced while maintaining detection accuracy via the introduction of the ADown modules.Subsequently,a Concentrated-Comprehensive Dual Convolution(C3_DualConv)module combining dual convolutional bottlenecks was proposed to enhance the model capability to capture features of Orah fruits obscured by branches and leaves;this practice further reduced the model size.Additionally,a Bidirectional Feature Pyramid Network(BiFPN)that includes a pyramid level 2 high-resolution layer was employed for more efficient multi-scale feature fusion.Besides,three Coordinate Attention(CA)mechanism modules were also added to improve the recognition and capture capability for Orah fruit features.Finally,a more focused minimum points distance intersection over union loss was adopted to boost the detection efficiency of densely occluded Orah fruits.Experimentally demonstrating that the improved YOLOv8n model accurately detected Orah fruits in complex orchard environments,achieving a 97.7%of precision,an Average Precision at IoU threshold 0.5(AP@0.5)of 98.8%,and a 96.69%of F1 score,while maintaining a compact model size of 4.1 MB,under a Windows-based system terminal.This proposed model was optimally deployed on an Nvidia Jetson Orin Nano using TensorRT Python Application Programming Interface(API),the average interface speed exceeds 30 fps,indicating a real-time detection ability.This study can provide technical support for Orah fruit robotic harvesting on the basis of edge device.展开更多
Accurate detection of citrus can be easily affected by adjacent branches and overlapped fruits in natural orchard conditions,where some specific information of citrus might be lost due to the resultant complex occlusi...Accurate detection of citrus can be easily affected by adjacent branches and overlapped fruits in natural orchard conditions,where some specific information of citrus might be lost due to the resultant complex occlusion.Traditional deep learning models might result in lower detection accuracy and detection speed when facing occluded targets.To solve this problem,an improved deep learning algorithm based on YOLOv5,named IYOLOv5,was proposed for accurate detection of citrus fruits.An innovative Res-CSPDarknet network was firstly employed to both enhance feature extraction performance and minimize feature loss within the backbone network,which aims to reduce the miss detection rate.Subsequently,the BiFPN module was adopted as the new neck net to enhance the function for extracting deep semantic features.A coordinate attention mechanism module was then introduced into the network’s detection layer.The performance of the proposed model was evaluated on a home-made citrus dataset containing 2000 optical images.The results show that the proposed IYOLOv5 achieved the highest mean average precision(93.5%)and F1-score(95.6%),compared to the traditional deep learning models including Faster R-CNN,CenterNet,YOLOv3,YOLOv5,and YOLOv7.In particular,the proposed IYOLOv5 obtained a decrease of missed detection rate(at least 13.1%)on the specific task of detecting heavily occluded citrus,compared to other models.Therefore,the proposed method could be potentially used as part of the vision system of a picking robot to identify the citrus fruits accurately.展开更多
Based on the chemical properties of dithiocarbamate pesticides,a device for rapid detection was developed in the paper,and the experimental conditions were optimized. Dithiocarbamate residues in fruits were successful...Based on the chemical properties of dithiocarbamate pesticides,a device for rapid detection was developed in the paper,and the experimental conditions were optimized. Dithiocarbamate residues in fruits were successfully detected using molecular absorption spectro-photometry,and the recovery rate was over 80%.The rapid detection method was simple to operate with low cost,and was conducive to application in basic level and enterprise laboratories.展开更多
The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that...The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.展开更多
Artificial intelligence-based automatic systems can reduce time,human error and post-harvest operations.By using such systems,food items can be successfully classified and graded based on defects.For this context,a ma...Artificial intelligence-based automatic systems can reduce time,human error and post-harvest operations.By using such systems,food items can be successfully classified and graded based on defects.For this context,a machine vision system was developed for fruit grading based on defects.The prototype consisted of defective fruit detection and mechanical sorting systems.Image processing algorithms and deep learning frameworks were used for detection of defective fruit.Different image processing algorithms including preprocessing,thresholding,morphological and bitwise operations combined with a deep leaning algorithm,i.e.,convolutional neural network(CNN),were applied to fruit images for the detection of defective fruit.The data set used for training CNN model consisted of fruit images collected from a publiclyavailable data set and captured fruit images:1799 and 1017 for mangoes and tomatoes,respectively.Subsequent to defective fruit detection,the information obtained was communicated to microcontroller that further actuated the mechanical sorting system accordingly.In addition,the system was evaluated experimentally in terms of detection accuracy,sorting accuracy and computational time.For the image processing algorithms scheme,the detection accuracy for mango and tomato was 89% and 92%,respectively,and for CNN architecture used,the validation accuracy for mangoes and tomatoes was 95% and 94%,respectively.展开更多
Although the development of the robot picking vision system is widely applied,it is very challenging for fruit detection in orchards with complex light and environment,especially for fruit colors similar to the backgr...Although the development of the robot picking vision system is widely applied,it is very challenging for fruit detection in orchards with complex light and environment,especially for fruit colors similar to the background.In recent,there are few studies on pecan fruit detection and location based on machine vision.In this study,an accurate and efficient pecan fruit detection method was proposed based on machine vision under natural pecan orchards.In order to solve the illumination problem,a light compensation algorithm was first utilized to process the collected samples,and then an improved Faster Region Convolutional Neural Network(Faster RCNN)with the Feature Pyramid Networks(FPN)was established to train the samples.Finally,the pecan number counting method was introduced to count the number of pecan.A total of 241 pecan images were tested,and comparison experiments were carried out.The mean average precision(mAP)of the proposed detection method was 95.932%,compared with the result without uneven illumination correction(UIC),which was increased by 0.849%,while the mAP of the Single Shot Detector(SSD)+FPN was 92.991%.In addition,the number of clusters was counted using the proposed method with an accuracy rate of 93.539%compared with the actual clusters.The results demonstrate that the proposed network has good robustness for pecan fruit detection in different illumination and various unstructured environments,and the experimental achievement has great potential for robot-picking visual systems.展开更多
Human labor efficiency has become unable to keep the pace with gradually annual citrus increasing production.Highly efficient and intelligent citrus picking and accurate yield estimation is the key to solve the proble...Human labor efficiency has become unable to keep the pace with gradually annual citrus increasing production.Highly efficient and intelligent citrus picking and accurate yield estimation is the key to solve the problem.Success heavily depends on detection accuracy,prediction speed,and easy model deployment.Traditional target detection methods often fail to achieve balanced results in all those aspects.An improved YOLOv8 network model with four significant features is proposed.First,a lightweight FasterNet network structure was introduced to the backbone network,which reduced the number of parameters and computations while maintaining high-precision detection.Second,a progressive feature pyramid network AFPN structure was added to the neck network.Third,a parallel multi-branch attention mechanism PMBA was added before the detection head to improve the sensing ability after the feature fusion network.Fourth,a Wise-IoU was introduced to replace the original CIoU loss function to make the whole training process converge faster.Based on this,this study proposes an improved version of the YOLOv8 model:the FAP-YOLOv8.This improved model achieved an average accuracy(mAP@0.5)of 97.2%on the citrus datasets,with an accuracy that was 4.7%higher than the original YOLOv8,which was 19.2%,7.4%,5.1%,4.9%,and 5.2%higher than the other models:Faster R-CNN,CenterNet,YOLOv5s,YOLOx-s,and YOLOv7,respectively.The number of parameters was reduced by 55.45%,the computation was reduced by 20%compared to the YOLOv8 benchmark,and the frame rate reached 46.51 fps to meet the detection requirements of lightweight networks.The experiments showed that the FAP-YOLOv8 models all outperformed the comparison models.Consequently,the proposed FAPYOLOv8 model can help solve the citrus detection problem in orchards,which can be better applied to edge devices and provides strong support for intelligent orchard management.展开更多
In recent years,worldwide research on fruit and vegetable quality detection technology includes machine vision,spectroscopy,acoustic vibration,tactile sensors,etc.These technologies have also been gradually applied to...In recent years,worldwide research on fruit and vegetable quality detection technology includes machine vision,spectroscopy,acoustic vibration,tactile sensors,etc.These technologies have also been gradually applied to fruit and vegetable grading and sorting lines in recent years,greatly improving the income of farmers.There have been numerous reviews of these techniques.Most of the published research on fruit and vegetable quality detection technology is still carried out in the laboratory.The emphases have been on quality feature extraction,model establishment and experimental verification.The successful application in the fruit and vegetable sorting production line proves that these studies have high application potential and value,and we look forward to the performance of these sensing technologies in the fruit and vegetable picking field.Therefore,in this paper,based on the future highly automated fruit and vegetable picking mode,we will focus on three kinds of fruit and vegetable quality detection technologies including machine vision,tactile sensor and spectroscopy,to provide some reference for future research.Since there are currently limited cases of detecting quality during the fruit and vegetable picking,experiments performed on prototypes of manipulator,or devices such as Nanocilia sensors,portable spectrometers,etc.,which are compact and convenient to mount on manipulator will be reviewed.Several tables and mosaics showing the performance of the three technologies in the detection of fruit and vegetable quality over the past five years have been listed.The performance of each sensing technology was relatively satisfactory in the laboratory in general.However,in the picking scenario,there are still many challenges to be solved.Different from industrial environments,agricultural scenarios are complex and changeable.Fragile and vulnerable agricultural products pose another challenge.The development of portable devices and nanomaterials have become important breakthroughs.Optical and tactile detection methods,as well as the integration of different quality detection methods,are expected to be the trends of research and development.展开更多
基金supported by the National Natural Science Foundation of China[grant No.62163005,32401709]the Youth Science Foundation of Guangxi Province[grant No.2025GXNSFBA069270]+1 种基金the Natural Science Foundation of Guangxi Province[grant No.2022GXNSFAA035633]the Open Fund of the National Key Laboratory of Agricultural Equipment Technology(South China Agricultural University)[grant No.SKLAET-202407].
文摘The replacement of personal computer terminal with edge device is recognized as a portable and cost-effective potential solution in solving equipment miniaturization and achieving high flexibility of robotic fruit harvesting at in-field scale.This study proposes a lightweight improved You Only Look Once version 8n(YOLOv8n)model for detecting Orah fruits and deploying this model on an edge device.First of all,the model size was reduced while maintaining detection accuracy via the introduction of the ADown modules.Subsequently,a Concentrated-Comprehensive Dual Convolution(C3_DualConv)module combining dual convolutional bottlenecks was proposed to enhance the model capability to capture features of Orah fruits obscured by branches and leaves;this practice further reduced the model size.Additionally,a Bidirectional Feature Pyramid Network(BiFPN)that includes a pyramid level 2 high-resolution layer was employed for more efficient multi-scale feature fusion.Besides,three Coordinate Attention(CA)mechanism modules were also added to improve the recognition and capture capability for Orah fruit features.Finally,a more focused minimum points distance intersection over union loss was adopted to boost the detection efficiency of densely occluded Orah fruits.Experimentally demonstrating that the improved YOLOv8n model accurately detected Orah fruits in complex orchard environments,achieving a 97.7%of precision,an Average Precision at IoU threshold 0.5(AP@0.5)of 98.8%,and a 96.69%of F1 score,while maintaining a compact model size of 4.1 MB,under a Windows-based system terminal.This proposed model was optimally deployed on an Nvidia Jetson Orin Nano using TensorRT Python Application Programming Interface(API),the average interface speed exceeds 30 fps,indicating a real-time detection ability.This study can provide technical support for Orah fruit robotic harvesting on the basis of edge device.
基金supported in part by the Natural Science Foundation of Guangdong Province,China(Grant No.2020B1515120070,Grant No.2022A1515010885)the Innovation Team Project of Universities in Guangdong Province,China(Grant No.2021KCXTD010)+2 种基金the Key Construction Discipline Research Capacity Enhancement Project of Guangdong Province,China(Grant No.2022ZDJS014)the Key Construction Discipline Research Capacity Enhancement Project of GPNU,China(Grant No.22GPNUZDJS11)the Characteristic Innovation Project of Universities in Guangdong Province,China(Grant No.2023KTSCX066).
文摘Accurate detection of citrus can be easily affected by adjacent branches and overlapped fruits in natural orchard conditions,where some specific information of citrus might be lost due to the resultant complex occlusion.Traditional deep learning models might result in lower detection accuracy and detection speed when facing occluded targets.To solve this problem,an improved deep learning algorithm based on YOLOv5,named IYOLOv5,was proposed for accurate detection of citrus fruits.An innovative Res-CSPDarknet network was firstly employed to both enhance feature extraction performance and minimize feature loss within the backbone network,which aims to reduce the miss detection rate.Subsequently,the BiFPN module was adopted as the new neck net to enhance the function for extracting deep semantic features.A coordinate attention mechanism module was then introduced into the network’s detection layer.The performance of the proposed model was evaluated on a home-made citrus dataset containing 2000 optical images.The results show that the proposed IYOLOv5 achieved the highest mean average precision(93.5%)and F1-score(95.6%),compared to the traditional deep learning models including Faster R-CNN,CenterNet,YOLOv3,YOLOv5,and YOLOv7.In particular,the proposed IYOLOv5 obtained a decrease of missed detection rate(at least 13.1%)on the specific task of detecting heavily occluded citrus,compared to other models.Therefore,the proposed method could be potentially used as part of the vision system of a picking robot to identify the citrus fruits accurately.
基金Supported by Class-A Projects of Fujian Department of Education(JA12465)Science and Technology Program of Xiamen City(3502Z20123046)
文摘Based on the chemical properties of dithiocarbamate pesticides,a device for rapid detection was developed in the paper,and the experimental conditions were optimized. Dithiocarbamate residues in fruits were successfully detected using molecular absorption spectro-photometry,and the recovery rate was over 80%.The rapid detection method was simple to operate with low cost,and was conducive to application in basic level and enterprise laboratories.
文摘The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.
文摘Artificial intelligence-based automatic systems can reduce time,human error and post-harvest operations.By using such systems,food items can be successfully classified and graded based on defects.For this context,a machine vision system was developed for fruit grading based on defects.The prototype consisted of defective fruit detection and mechanical sorting systems.Image processing algorithms and deep learning frameworks were used for detection of defective fruit.Different image processing algorithms including preprocessing,thresholding,morphological and bitwise operations combined with a deep leaning algorithm,i.e.,convolutional neural network(CNN),were applied to fruit images for the detection of defective fruit.The data set used for training CNN model consisted of fruit images collected from a publiclyavailable data set and captured fruit images:1799 and 1017 for mangoes and tomatoes,respectively.Subsequent to defective fruit detection,the information obtained was communicated to microcontroller that further actuated the mechanical sorting system accordingly.In addition,the system was evaluated experimentally in terms of detection accuracy,sorting accuracy and computational time.For the image processing algorithms scheme,the detection accuracy for mango and tomato was 89% and 92%,respectively,and for CNN architecture used,the validation accuracy for mangoes and tomatoes was 95% and 94%,respectively.
基金funded by the Forestry Science and Technology Innovation Fund Project of Hunan Province(Grant No.XLK202108-4)and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Although the development of the robot picking vision system is widely applied,it is very challenging for fruit detection in orchards with complex light and environment,especially for fruit colors similar to the background.In recent,there are few studies on pecan fruit detection and location based on machine vision.In this study,an accurate and efficient pecan fruit detection method was proposed based on machine vision under natural pecan orchards.In order to solve the illumination problem,a light compensation algorithm was first utilized to process the collected samples,and then an improved Faster Region Convolutional Neural Network(Faster RCNN)with the Feature Pyramid Networks(FPN)was established to train the samples.Finally,the pecan number counting method was introduced to count the number of pecan.A total of 241 pecan images were tested,and comparison experiments were carried out.The mean average precision(mAP)of the proposed detection method was 95.932%,compared with the result without uneven illumination correction(UIC),which was increased by 0.849%,while the mAP of the Single Shot Detector(SSD)+FPN was 92.991%.In addition,the number of clusters was counted using the proposed method with an accuracy rate of 93.539%compared with the actual clusters.The results demonstrate that the proposed network has good robustness for pecan fruit detection in different illumination and various unstructured environments,and the experimental achievement has great potential for robot-picking visual systems.
基金financially supported by the Yunnan Provincial Major Science and Technology Special Project:Research and Development and Application Demonstration of Key Technology for Digitization of Cloud Fruit(Grant No.202002AE09001002).
文摘Human labor efficiency has become unable to keep the pace with gradually annual citrus increasing production.Highly efficient and intelligent citrus picking and accurate yield estimation is the key to solve the problem.Success heavily depends on detection accuracy,prediction speed,and easy model deployment.Traditional target detection methods often fail to achieve balanced results in all those aspects.An improved YOLOv8 network model with four significant features is proposed.First,a lightweight FasterNet network structure was introduced to the backbone network,which reduced the number of parameters and computations while maintaining high-precision detection.Second,a progressive feature pyramid network AFPN structure was added to the neck network.Third,a parallel multi-branch attention mechanism PMBA was added before the detection head to improve the sensing ability after the feature fusion network.Fourth,a Wise-IoU was introduced to replace the original CIoU loss function to make the whole training process converge faster.Based on this,this study proposes an improved version of the YOLOv8 model:the FAP-YOLOv8.This improved model achieved an average accuracy(mAP@0.5)of 97.2%on the citrus datasets,with an accuracy that was 4.7%higher than the original YOLOv8,which was 19.2%,7.4%,5.1%,4.9%,and 5.2%higher than the other models:Faster R-CNN,CenterNet,YOLOv5s,YOLOx-s,and YOLOv7,respectively.The number of parameters was reduced by 55.45%,the computation was reduced by 20%compared to the YOLOv8 benchmark,and the frame rate reached 46.51 fps to meet the detection requirements of lightweight networks.The experiments showed that the FAP-YOLOv8 models all outperformed the comparison models.Consequently,the proposed FAPYOLOv8 model can help solve the citrus detection problem in orchards,which can be better applied to edge devices and provides strong support for intelligent orchard management.
基金financially supported by the Key Research and Development Projects of Zhejiang Province(Grant No.2022C02021).
文摘In recent years,worldwide research on fruit and vegetable quality detection technology includes machine vision,spectroscopy,acoustic vibration,tactile sensors,etc.These technologies have also been gradually applied to fruit and vegetable grading and sorting lines in recent years,greatly improving the income of farmers.There have been numerous reviews of these techniques.Most of the published research on fruit and vegetable quality detection technology is still carried out in the laboratory.The emphases have been on quality feature extraction,model establishment and experimental verification.The successful application in the fruit and vegetable sorting production line proves that these studies have high application potential and value,and we look forward to the performance of these sensing technologies in the fruit and vegetable picking field.Therefore,in this paper,based on the future highly automated fruit and vegetable picking mode,we will focus on three kinds of fruit and vegetable quality detection technologies including machine vision,tactile sensor and spectroscopy,to provide some reference for future research.Since there are currently limited cases of detecting quality during the fruit and vegetable picking,experiments performed on prototypes of manipulator,or devices such as Nanocilia sensors,portable spectrometers,etc.,which are compact and convenient to mount on manipulator will be reviewed.Several tables and mosaics showing the performance of the three technologies in the detection of fruit and vegetable quality over the past five years have been listed.The performance of each sensing technology was relatively satisfactory in the laboratory in general.However,in the picking scenario,there are still many challenges to be solved.Different from industrial environments,agricultural scenarios are complex and changeable.Fragile and vulnerable agricultural products pose another challenge.The development of portable devices and nanomaterials have become important breakthroughs.Optical and tactile detection methods,as well as the integration of different quality detection methods,are expected to be the trends of research and development.
