Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.

A new approach to learning and recognizing leaf diseases from individual lesions using convolutional neural networks

Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research area.Most studies have focused on recognizing diseases from images of whole leaves.This approach limits the resulting models’ability to estimate leaf disease severity or identify multiple anomalies occurring on the same leaf.Recent studies have demonstrated that classifying leaf diseases based on individual lesions greatly enhances disease recognition accuracy.In those studies,however,the lesions were laboriously cropped by hand.This study proposes a semi-automatic algorithm that facilitates the fast and efficient preparation of datasets of individual lesions and leaf image pixel maps to overcome this problem.These datasets were then used to train and test lesion classifier and semantic segmentation Convolutional Neural Network(CNN)models,respectively.We report that GoogLeNet’s disease recognition accuracy improved by more than 15%when diseases were recognized from lesion images compared to when disease recognition was done using images of whole leaves.A CNN model which performs semantic segmentation of both the leaf and lesions in one pass is also proposed in this paper.The proposed KijaniNet model achieved state-of-the-art segmentation performance in terms of mean Intersection over Union(mIoU)score of 0.8448 and 0.6257 for the leaf and lesion pixel classes,respectively.In terms of mean boundary F1 score,the KijaniNet model attained 0.8241 and 0.7855 for the two pixel classes,respectively.Lastly,a fully automatic algorithm for leaf disease recognition from individual lesions is proposed.The algorithm employs the semantic segmentation network cascaded to a GoogLeNet classifier for lesion-wise disease recognition.The proposed fully automatic algorithm outperforms competing methods in terms of its superior segmentation and classification performance despite being trained on a small dataset.

Image segmentation of overlapping leaves based on Chan–Vese model and Sobel operator

To improve the segmentation precision of overlapping crop leaves,this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator.The approach consists of three stages.First,a feature that identifies hues with relatively high levels of green is used to extract the region of leaves and remove the background.Second,the Chan–Vese model and improved Sobel operator are implemented to extract the leaf contours and detect the edges,respectively.Third,a target leaf with a complex background and overlapping is extracted by combining the results obtained by the Chan–Vese model and Sobel operator.To verify the effectiveness of the proposed algorithm,a segmentation experiment was performed on 30 images of cucumber leaf.The mean error rate of the proposed method is 0.0428,which is a decrease of 6.54%compared with the mean error rate of the level set method.Experimental results show that the proposed method can accurately extract the target leaf from cucumber leaf images with complex backgrounds and overlapping regions.

Plant trait estimation and classification studies in plant phenotyping using machine vision - A review

Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques.Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field.Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red,green and blue(RGB)imaging,thermal imaging,chlorophyll fluorescence imaging(CFIM),hyperspectral imaging,3-dimensional(3-D)imaging or high resolution volumetric imaging.This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping.This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification.In this paper,information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods.This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural(2-D and 3-D),physiological and temporal trait estimation,and classification studies in plants.