Weed Classification Using Particle Swarm Optimization and Deep Learning Models

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and quality.Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity.To address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image classification.Throughout this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed datasets.The Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed images.An effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification accuracy.The proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed classification.This work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

Weed Species Composition in Paddy Field of Usur Town,Bade Local Government,Yobe State,Nigeria

Farmers are eager to know the various types of weeds in paddy fields.This will help in choosing the best weed management practice for effective weed control as well as reducing rice yield losses.The objectives of the study are to identify the weeds species affecting the rice field,to assess the composition of weeds species,to classify the weed species into different families,genera,species,common names,Hausa names,lifecycles,life forms,native/exotic species,propagation and uses,and to determine the dominant weed species.Random vegetation surveys were conducted.Weeds observed were photographed,and prepared as herbarium specimens.Standard key manuals and checklists were utilized for weed identification and later organized using the Angiosperm Phylogeny Group(APG)classification system.A total number of 72 plants species distributed within 16 families and 50 genera were inventoried.The annuals(66.67%)were the dominant weed followed by perennials(33.33%)while biennials were the least.The broad leaves were the dominant weed(44.61%)identified followed by Poaceae(27.7%)and Sedges(11.11%).Results obtained from this study could be useful in choosing the best management practice and in making a decision on the choice of herbicides and directing research towards improved weed control measures.

Review of deep learning-based weed identification in crop fields

Automatic weed identification and detection are crucial for precision weeding operations.In recent years,deep learning(DL)has gained widespread attention for its potential in crop weed identification.This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL.Through an analysis of relevant literature from both within and outside of China,the author summarizes the development history,research progress,and identification and detection methods of DL-based weed identification technology.Emphasis is placed on data sources and DL models applied to different technical tasks.Additionally,the paper discusses the challenges of time-consuming and laborious dataset preparation,poor generality,unbalanced data categories,and low accuracy of field identification in DL for weed identification.Corresponding solutions are proposed to provide a reference for future research directions in weed identification.

A study on deep learning algorithm performance on weed and crop species identification under different image background

Weed identification is fundamental toward developing a deep learning-based weed control system.Deep learning algorithms assist to build a weed detection model by using weed and crop images.The dynamic environmental conditions such as ambient lighting,moving cameras,or varying image backgrounds could affect the performance of deep learning algorithms.There are limited studies on how the different image backgrounds would impact the deep learning algorithms for weed identification.The objective of this research was to test deep learning weed identification model performance in images with potting mix(non-uniform)and black pebbled(uniform)backgrounds interchangeably.The weed and crop images were acquired by four canon digital cameras in the greenhouse with both uniform and non-uniform background conditions.A Convolutional Neural Network(CNN),Visual Group Geometry(VGG16),and Residual Network(ResNet50)deep learning architectures were used to build weed classification models.The model built from uniform background images was tested on images with a non-uniform background,as well as model built from non-uniform background images was tested on images with uniform background.Results showed that the VGG16 and ResNet50 models built from non-uniform background images were evaluated on the uniform background,achieving models'performance with an average f1-score of 82.75%and 75%,respectively.Conversely,the VGG16 and ResNet50 models built from uniform background images were evaluated on the non-uniform background images,achieving models'performance with an average f1-score of 77.5%and 68.4%respectively.Both the VGG16 and ResNet50 models'performances were im-proved with average f1-score values between 92%and 99%when both uniform and non-uniform background images were used to build the model.It appears that the model performances are reduced when they are tested with images that have different object background than the ones used for building the model.