Detection of gray mold disease and its severity on strawberry using deep learning networks

Bhujel, Anil; Khan, Fawad; Basak, Jayanta Kumar; Jaihuni, Mustafa; Sihalath, Thavisack; Moon, Byeong-Eun; Park, Jaesung; Kim, Hyeontae

doi:10.1007/s41348-022-00578-8

Cited by 30 publications

(14 citation statements)

References 34 publications

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“…Pixel-level segmentation quantifies plant disease severity, which helps calculate pesticide dosage. In [25], the authors have presented deep learning to detect and measure grey mould illness in strawberry plants.…”

Section: Literature Surveymentioning

confidence: 99%

CRUN-Based Leaf Disease Segmentation and Morphological-Based Stage Identification

Sujithra

Ukrit

2022

Mathematical Problems in Engineering

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Natural growth is eliminated by the process of globalization in today’s globe owing to the development of technology and landscapes. The majority of today’s youngsters, as well as our seniors, lack an appropriate understanding of natural species such as plant names, tree names, and medicinal plants. This is attributed to technological advancements and a decline in gardening interest. To close this gap, horticulture may employ technology that aids in the improvement of plant understanding and growth. This method is implemented in the existing system for diagnosing leaf diseases using image processing and machine learning techniques. In the existing process, the classification of leaf disease is performed using image processing steps, such as preprocessing, segmentation, feature extraction, feature reduction, and classification. Even though it utilizes multiple processing steps and region-based classification, it identifies only the type of disease. In this paper, a combined approach of regional-based convolutional neural networks and U-Net (CRUN) is proposed for segmenting the leaf diseases from the augmented leaf dataset. Then, the segmented images are subjected to a morphological process to identify the level of disease in the leaf. This identification helps to identify the leaf’s nature and suggests a process to reduce the disease’s spread to other leaves through the proper use of fertilizers. The proposed method is applied to real-time images of sugarcane leaf diseases, such as bacterial blight and red rot, and banana leaf diseases, such as yellow and black sigatoka. This method is also applied to public sugarcane and banana leaf datasets from the Kaggle website. The proposed CRUN algorithm effectively segments the disease region. The morphological process helps to identify the disease level and protect the plant from further spread of disease. As a result, the proposed CRUN and morphological tests are most effective for automating leaf disease detection and prevention.

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Section: Literature Surveymentioning

confidence: 99%

CRUN-Based Leaf Disease Segmentation and Morphological-Based Stage Identification

Sujithra

Ukrit

2022

Mathematical Problems in Engineering

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“…Researchers used typical network architectures of semantic segmentation to segment disease parts of plants leaves, e.g., Lin et al used UNet to segment cucumber powdery mildew, reaching the segmentation accuracy of 72% [15]. Bhujel et al also used UNet to segment strawberry gray mold zones and the performance of the disease segmentation was better than that of the XGboot and k-means segmentation methods [16]. Loyani et al segmented the disease zones of tomato images using the UNet semantic segmentation network and the Mask-RCNN instance segmentation model [17].…”

Section: Introductionmentioning

confidence: 99%

Weakly supervised segmentation for plant disease using class activation maps and weak annotation labels

Chen

Zhang

et al. 2023

International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022)

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The segmentation of crop disease is an important task for plant image processing. Fully supervised segmentation methods are commonly used to segment disease on plant leaves, and a large amount of manually annotated information is needed. In contrast, weakly supervised segmentation methods have the advantages of reducing the workload of manual annotation. In this study, a weakly supervised segmentation method for plant disease was proposed for the segmentation of rust in apple rust images using the procedure below. A classification model was trained using images of both diseased and healthy leaves, and class activation maps (CAMs) of disease were obtained as pseudo labels for a further segmentation of the disease. The segmentation method using CAMs only achieved an Intersection over Union (IoU) score of 0.45. Then pointauxiliary labels and line-auxiliary labels of disease were also constructed to improve the accuracy of the disease segmentation. The IoU values resulting from the two schemes -the point-auxiliary labels + CAMs and line-auxiliary labels + CAMs methods reached 0.64 and 0.74, respectively. Moreover, the proposed method was also applied to the segmentation of disease on whole leaves. Furthermore, the proportion of disease in whole leaves can be also obtained, and the coefficient of the correlation between the obtained disease proportion and true disease proportion was 0.89. The results suggest the feasibility of the proposed method to segment plant disease and evaluate the severity degree of the plant disease.

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“…Vallabhajosyula et al (2022) used transfer learning to identify leaf diseases. Bhujel et al (2022) used deep learning networks to detect Botrytis on strawberry and its severity. Thangaraj et al (2021) proposed a deep convolutional neural network model based on transfer learning to identify tomato leaf diseases.…”

Section: Introductionmentioning

confidence: 99%

Tomato leaf disease recognition based on improved convolutional neural network with attention mechanism

Zhou

Zhao

et al. 2023

Plant Pathology

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Plant diseases are common factors restricting crop yield and quality. For individuals without training, it is still difficult to identify various diseases accurately because many leaf diseases are similar in colour, shape and other aspects. Requiring an expert diagnosis can cause delays and lead to increasing costs. Therefore, this paper proposes an improved convolutional neural network for recognition of tomato leaf diseases. The experimental dataset was from the publicly available dataset PlantVillage. Based on the original ResNet18 model, a new convolutional neural network, TomatoNet, was constructed by adding a squeeze‐and‐excitation module and modifying the classifier structure. The results show that the average recognition accuracy of the TomatoNet network is 99.63%, which is 0.53% higher than the ResNet18 network. In addition, the recognition accuracy improved from 88.97% to 98.35% after the improvement of the AlexNet network. Finally, the superiority of TomatoNet was verified by comparison with other advanced models. This experiment verifies the feasibility of a deep learning algorithm for plant leaf disease recognition, which can provide a more efficient and convenient solution for detecting plant leaf disease.

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Detection of gray mold disease and its severity on strawberry using deep learning networks

Cited by 30 publications

References 34 publications

CRUN-Based Leaf Disease Segmentation and Morphological-Based Stage Identification

CRUN-Based Leaf Disease Segmentation and Morphological-Based Stage Identification

Weakly supervised segmentation for plant disease using class activation maps and weak annotation labels

Tomato leaf disease recognition based on improved convolutional neural network with attention mechanism

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