This study presents a drone-based aerial imaging method for automated rice seedling detection and counting in paddy fields.Utilizing a drone equipped with a high-resolution camera,images are captured 14 days postsowin...This study presents a drone-based aerial imaging method for automated rice seedling detection and counting in paddy fields.Utilizing a drone equipped with a high-resolution camera,images are captured 14 days postsowing at a consistent altitude of six meters,employing autonomous flight for uniform data acquisition.The approach effectively addresses the distinct growth patterns of both single and clustered rice seedlings at this early stage.The methodology follows a two-step process:first,the GoogleNet deep learning network identifies the location and center points of rice plants.Then,the U-Net deep learning network performs classification and counting of individual plants and clusters.This combination of deep learning models achieved a 90%accuracy rate in classifying and counting both single and clustered seedlings.To validate the method’s effectiveness,results were compared against traditional manual counting conducted by agricultural experts.The comparison revealed minimal discrepancies,with a variance of only 2–4 clumps per square meter,confirming the reliability of the proposed method.This automated approach offers significant benefits by providing an efficient,accurate,and scalable solution for monitoring seedling growth.It enables farmers to optimize fertilizer and pesticide application,improve resource allocation,and enhance overall crop management,ultimately contributing to increased agricultural productivity.展开更多
Rice spike detection and counting play a crucial role in rice yield research.Automatic detection technology based on Unmanned Aerial Vehicle(UAV)imagery has the advantages of flexibility,efficiency,low cost,safety,and...Rice spike detection and counting play a crucial role in rice yield research.Automatic detection technology based on Unmanned Aerial Vehicle(UAV)imagery has the advantages of flexibility,efficiency,low cost,safety,and reliability.However,due to the complex field environment and the small target morphology of some rice spikes,the accuracy of detection and counting is relatively low,and the differences in phenotypic characteristics of rice spikes at different growth stages have a significant impact on detection results.To solve the above problems,this paper improves the You Only Look Once v8(YOLOv8)model,proposes a new method for detecting and counting rice spikes,and designs a comparison experiment using rice spike detection in different periods.Themethod improves the model’s ability to detect rice ears with special morphologies by introducing a Dynamic Snake Convolution(DSConv)module into the Bottleneck of the C2f structure of YOLOv8,which enhances themodule’s ability to extract elongated structural features;In addition,the Weighted Interpolation of Sequential Evidence for Intersection over Union(Wise-IoU)loss function is improved to reduce the harmful gradient of lowquality target frames and enhance themodel’s ability to locate small spikelet targets,thus improving the overall detection performance of the model.The experimental results show that the enhanced rice spike detection model has an average accuracy of 91.4%and a precision of 93.3%,respectively,which are 2.3 percentage points and 2.5 percentage points higher than those of the baseline model.Furthermore,it effectively reduces the occurrence of missed and false detections of rice spikes.In addition,six rice spike detection models were developed by training the proposed models with images of rice spikes at themilk and waxmaturity stages.The experimental findings demonstrated that the models trained on milk maturity data attained the highest detection accuracy for the same data,with an average accuracy of 96.2%,an R squared(R^(2))value of 0.71,and a Rootmean squared error(RMSE)of 20.980.This study provides technical support for early and non-destructive yield estimation in rice in the future.展开更多
Using machine vision to accurately identify apple number on the tree is becoming the key supporting technology for orchard precision production management.For adapting to the complexity of the field environment in var...Using machine vision to accurately identify apple number on the tree is becoming the key supporting technology for orchard precision production management.For adapting to the complexity of the field environment in various detection situations,such as illumination changes,color variation,fruit overlap,and branches and leaves shading,a robust algorithm for detecting and counting apples based on their color and shape modes was proposed.Firstly,BP(back propagation)neural network was used to train apple color identification model.Accordingly the irrelevant background was removed by using the trained neural network model and the image only containing the apple color pixels was acquired.Then apple edge detection was carried out after morphological operations on the obtained image.Finally,the image was processed by using circle Hough transform algorithm,and apples were located with the help of calculating the center coordinates of each apple edge circle.The validation experimental results showed that the correlation coefficient of R2 between the proposed approaches based counting and manually counting reached 0.985.It illustrated that the proposed algorithm could be used to detect and count apples from apple trees’images taken in field environment with a high precision and strong anti-jamming feature.展开更多
基金funded by the Ministry of Education and Training Project(code number:B2023-TCT-08).
文摘This study presents a drone-based aerial imaging method for automated rice seedling detection and counting in paddy fields.Utilizing a drone equipped with a high-resolution camera,images are captured 14 days postsowing at a consistent altitude of six meters,employing autonomous flight for uniform data acquisition.The approach effectively addresses the distinct growth patterns of both single and clustered rice seedlings at this early stage.The methodology follows a two-step process:first,the GoogleNet deep learning network identifies the location and center points of rice plants.Then,the U-Net deep learning network performs classification and counting of individual plants and clusters.This combination of deep learning models achieved a 90%accuracy rate in classifying and counting both single and clustered seedlings.To validate the method’s effectiveness,results were compared against traditional manual counting conducted by agricultural experts.The comparison revealed minimal discrepancies,with a variance of only 2–4 clumps per square meter,confirming the reliability of the proposed method.This automated approach offers significant benefits by providing an efficient,accurate,and scalable solution for monitoring seedling growth.It enables farmers to optimize fertilizer and pesticide application,improve resource allocation,and enhance overall crop management,ultimately contributing to increased agricultural productivity.
基金funded by Jilin Province Innovation and Entrepreneurship Talent Project,grant number 2023QN15funded by Science and Technology Development Plan Project of Jilin Province,grant number 20220202035NC.
文摘Rice spike detection and counting play a crucial role in rice yield research.Automatic detection technology based on Unmanned Aerial Vehicle(UAV)imagery has the advantages of flexibility,efficiency,low cost,safety,and reliability.However,due to the complex field environment and the small target morphology of some rice spikes,the accuracy of detection and counting is relatively low,and the differences in phenotypic characteristics of rice spikes at different growth stages have a significant impact on detection results.To solve the above problems,this paper improves the You Only Look Once v8(YOLOv8)model,proposes a new method for detecting and counting rice spikes,and designs a comparison experiment using rice spike detection in different periods.Themethod improves the model’s ability to detect rice ears with special morphologies by introducing a Dynamic Snake Convolution(DSConv)module into the Bottleneck of the C2f structure of YOLOv8,which enhances themodule’s ability to extract elongated structural features;In addition,the Weighted Interpolation of Sequential Evidence for Intersection over Union(Wise-IoU)loss function is improved to reduce the harmful gradient of lowquality target frames and enhance themodel’s ability to locate small spikelet targets,thus improving the overall detection performance of the model.The experimental results show that the enhanced rice spike detection model has an average accuracy of 91.4%and a precision of 93.3%,respectively,which are 2.3 percentage points and 2.5 percentage points higher than those of the baseline model.Furthermore,it effectively reduces the occurrence of missed and false detections of rice spikes.In addition,six rice spike detection models were developed by training the proposed models with images of rice spikes at themilk and waxmaturity stages.The experimental findings demonstrated that the models trained on milk maturity data attained the highest detection accuracy for the same data,with an average accuracy of 96.2%,an R squared(R^(2))value of 0.71,and a Rootmean squared error(RMSE)of 20.980.This study provides technical support for early and non-destructive yield estimation in rice in the future.
基金The authors acknowledge that this research was supported by Chinese National Science and Technology Support Program(2012BAH29B04)863 Project(2012AA101900).
文摘Using machine vision to accurately identify apple number on the tree is becoming the key supporting technology for orchard precision production management.For adapting to the complexity of the field environment in various detection situations,such as illumination changes,color variation,fruit overlap,and branches and leaves shading,a robust algorithm for detecting and counting apples based on their color and shape modes was proposed.Firstly,BP(back propagation)neural network was used to train apple color identification model.Accordingly the irrelevant background was removed by using the trained neural network model and the image only containing the apple color pixels was acquired.Then apple edge detection was carried out after morphological operations on the obtained image.Finally,the image was processed by using circle Hough transform algorithm,and apples were located with the help of calculating the center coordinates of each apple edge circle.The validation experimental results showed that the correlation coefficient of R2 between the proposed approaches based counting and manually counting reached 0.985.It illustrated that the proposed algorithm could be used to detect and count apples from apple trees’images taken in field environment with a high precision and strong anti-jamming feature.
