Potential of plant identification apps in urban forestry studies in China:comparison of recognition accuracy and user experience of five apps

Information on species composition of an urban forest is essential for its management.However,to obtain this information becomes increasingly difficult due to limited taxonomic expertise.In this study,we tested the possibility of using plant identification applications running on mobile platforms to fill this vacuum.Five plant identification apps were compared for their potential in identifying urban tree species in China.An online survey was conducted to determine the features of apps that contributed to users’satisfaction.The results show that identification accuracy varied significantly among the apps.The best performer achieved an accuracy of 74.6%at the species level,which is comparable to the accuracy by professionals in field surveys.Among the features of apps,accuracy of identification was the most important factor that contributed to users’satisfaction.However,plant identification apps did not perform well when used on rare species or outside of the regions where they have been developed.Results indicate that plant identification apps have great potential in urban forest studies and management,but users need to be cautious when deciding which one to use.

Research on Plant Species Identification Based on Improved Convolutional Neural Network

Plant species recognition is an important research area in image recognition in recent years.However,the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy.Therefore,ShuffleNetV2 was improved by combining the current hot concern mechanism,convolution kernel size adjustment,convolution tailoring,and CSP technology to improve the accuracy and reduce the amount of computation in this study.Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning.The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum.In this paper,a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed,containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University.Finally,the improved model is compared with the baseline version of the model,which achieves better results in terms of improving accuracy and reducing the computational effort.The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%,and the recognition precision reaches up to 93.6%,which is 5.1%better than the original model and reduces the computational effort by about 31%compared with the original model.In addition,the experimental results were evaluated using metrics such as the confusion matrix,which can meet the requirements of professionals for the accurate identification of plant species.

Identification of Damage on Different Plants Caused by Botrytis cinerea and its Extracellular Macromolecular Toxin

With Bowytis cinerea and its extracellular macromolecular toxins as the test materials, 30 speiees of plants belonging to 29 genera and 21 families were selected as the test plants to observe the infectivity of B. dnerea and damage status of macromolecular toxins secreted by B. cinerea on plants. The resulsts showed that 17 species of plants were beth infected by B. cinerea and damaged by toxins, accounting for 56.7% of the total plants. Two species of plants could be neither infected by B. cinerea nor damaged by toxins. The study provided the reference for further understanding of pathogenic mechanism of plant pathogenic fungi toxins.

Lightweight Method for Plant Disease Identification Using Deep Learning

In the deep learning approach for identifying plant diseases,the high complexity of the network model,the large number of parameters,and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources.In this study,a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed.In the proposed model,the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions;the efficient channel attention module is added into the ShuffleNetV2 model network structure to enhance the channel features;and the ReLU activation function is replaced with the ReLU6 activation function to prevent the gen-eration of large gradients.Experiments are conducted on the public dataset PlantVillage.The results show that the proposed model achieves an accuracy of 99.43%,which is an improvement of 0.6 percentage points compared to the ShuffleNetV2 model.Compared to lightweight network models,such as MobileNetV2,MobileNetV3,EfficientNet,and EfficientNetV2,and classical convolutional neural network models,such as ResNet34,ResNet50,and ResNet101,the proposed model has fewer parameters and higher recognition accuracy,which provides guidance for deploying crop pest identification methods on resource-constrained devices,including mobile terminals.

Molecular identification of plants regenerated from somatic hybridization between rice and apomictic Panicum

We attempted to introduce apomictic gene(s)into rice via somatic hybridization by usingapomictic Panicum maximum Jacq.as thedonor of apomictic gene(s).Protoplasts of rice derived from suspen-sion cells were inactivated with indoacetamide(IOA)and protoplasts of Panicum maximum

Electrochemical Identification of Yulania spp. by Fingerprinting of Leaves Using Glassy Carbon Electrode

In this communication,we used electrochemical sensor for recording the electrochemical profiles of eleven species of Yulania spp.from leaf extract.Two solvents and two buffer conditions were used for electrochemical fingerprints collection.Their electrochemical fingerprints can be converted to different patterns and consequently for species recognition.The results indicate the pattern recognition is much convenient than that of the recognition of species directly using voltammetric signal.The current information in electrochemical fingerprinting represents the type and amount of electrochemically active molecules,which linked to the genetic differences among the plants.Therefore,the electrochemical fingerprints were applied for further phylogenetic study.The phylogenetic tree deduced from voltametric curves is divided into three main groups.The first clade contains Y.denudate,Liriodendron chinense,Y.cylindrica,Y.biondii,Y.sprengeri.The second clade contains Y.zenii,Y.liliiflora,Y.kobus,and Y.amoena.The third clade contains Y.×soulangeana,Manglietia fordiana and Y.sinostellata.In addition,Y.salicifolia is not in these main clades.The results demonstrate that electrochemical fingerprinting can be used as a com-plementary tool in the study of phylogenetics.

AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

Estimation of flea beetle damage in the field using a multistage deep learning-based solution

Estimation of damage in plants is a key issue for crop protection.Currently,experts in the field manually assess the plots.This is a time-consuming task that can be automated thanks to the latest technology in computer vision(CV).The use of image-based systems and recently deep learning-based systems have provided good results in several agricultural applications.These image-based applications outperform expert evaluation in controlled environments,and now they are being progressively included in non-controlled field applications.A novel solution based on deep learning techniques in combination with image processingmethods is proposed to tackle the estimate of plant damage in the field.The proposed solution is a two-stage algorithm.In a first stage,the single plants in the plots are detected by an object detection YOLO based model.Then a regression model is applied to estimate the damage of each individual plant.The solution has been developed and validated in oilseed rape plants to estimate the damage caused by flea beetle.The crop detection model achieves a mean precision average of 91%with a mAP@0.50 of 0.99 and a mAP@0.95 of 0.91 for oilseed rape specifically.The regression model to estimate up to 60%of damage degree in single plants achieves a MAE of 7.11,and R2 of 0.46 in comparison with manual evaluations done plant by plant by experts.Models are deployed in a docker,and with a REST API communication protocol they can be inferred directly for images acquired in the field from a mobile device.