Leaf Segmentation and Classification with a Complicated Background Using Deep Learning

Yang, Kunlong; Zhong, Wenbin; Li, Feng-guo

doi:10.3390/agronomy10111721

Cited by 75 publications

(46 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, Ganesh et al [50] and Jia et al [51] developed harvesting detectors based on the Mask-RCNN for robotic detections of apple and orange in orchards with precision of 0.895 to 0.975. Yang et al [52] revealed the potential of Mask-RCNN for the identification of leaves in plant images for rapid phenotype analysis, yielding the average accuracy value up to 91.5%. Tian et al [53] illustrated that Mask-RCNN performed best compared to other models including CNN and SVM in automatic segmentation of apple flowers of different growth stages.…”

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

Zhang

Yang

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

Zhang

Yang

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Deep convolutional neural networks were developed to identify plant diseases and to predict the macronutrient deficiencies during the flowering and fruit development stage [102]. The visual geometry group (VGG) CNN architecture identified plant diseases with the leaf images of the plants and communicated the results to farmers through smart phones [103,104]. The endemic fungal infection diagnosis in the winter wheat [89] was validated and trained with Imagenet datasets and implemented with an adaptive deep CNN.…”

Section: Deep Architectures In Smart Farmingmentioning

confidence: 99%

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

et al. 2021

View full text Add to dashboard Cite

Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

show abstract

“…Automated ROI extraction is normally based on image segmentation, but no single technique exists for ideal segmentation. [27] While on the other hand, manual and semi-automated techniques are based on expert opinion. It shows that human-based extraction has its limitations.…”

Section: Range Oriented Pixel-based Segmentation (Rops) Algorithmmentioning

confidence: 99%

Classification of canola seed varieties based on multi-feature analysis using computer vision approach

Qadri

Razzaq

et al. 2021

International Journal of Food Properties

View full text Add to dashboard Cite

This study aims to analyze the potential of the computer vision (CV) approach to classify eight canola varieties. The input images of eight canola varieties were CON-I, CON-II, CON-III, Pakola, Canola Raya, Rainbow, PARC Canola Hybrid, and Tarnab-III. A digital camera acquired these images on an open sunny day without any complex laboratory setup. First-order histogram features, second-order statistical texture features, binary features, spectral features of three bands were, blue (B), green (G), and red (R), were employed in the artificial neural network (ANN). A 10-fold stratified cross-validation method was used for classification. The best results with accuracy ranging from 95% to 98% observed when the data of regions of interest (512 × 512) deployed to the classifier.

show abstract

Leaf Segmentation and Classification with a Complicated Background Using Deep Learning

Cited by 75 publications

References 30 publications

Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Classification of canola seed varieties based on multi-feature analysis using computer vision approach

Contact Info

Product

Resources

About