Automatic plant disease diagnosis using mobile capture  devices

Johannes, Alexander; Seitz, Maximiliam

doi:10.51202/9783181023006-39

Cited by 11 publications

(11 citation statements)

References 23 publications

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“…In addition, it is common in the literature works that developed their own dataset applied to a specific type of crop such as Fuentes et al (2017) that collected images of tomato leaves using conventional cameras. Johannes et al (2017) developed a system capable of identifying diseases in photos of wheat leaves obtained by smartphones. Liu et al (2017) created a dataset of apple leaves and proposed a new architecture based on AlexNet for the recognition of diseases and Ma et al (2018) developed a dataset with images of common disease symptoms affecting cucumber leaves.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for classification and severity estimation of coffee leaf biotic stress

Esgario

Krohling

Ventura

2020

Computers and Electronics in Agriculture

204

109

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Biotic stress consists of damage to plants through other living organisms. Efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increase on productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. The experimental results obtained for classification as well as for severity estimation indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic stresses in coffee plantations.

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Section: Introductionmentioning

confidence: 99%

Deep learning for classification and severity estimation of coffee leaf biotic stress

Esgario

Krohling

Ventura

2020

Computers and Electronics in Agriculture

204

109

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“…Johannes et al [46] used an algorithm based on heat map technology to extract the diseased objects. In addition, each heat map is described by two descriptors, one for evaluating the color information of the disease, and the other for identifying the texture of the heat map.…”

Section: Visualization Techniquementioning

confidence: 99%

Plant Disease Detection and Classification by Deep Learning—A Review

Zhang

2021

IEEE Access

417

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Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved. INDEX TERMSDeep learning, plant leaf disease detection, visualization, small sample, hyperspectral imaging.

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“…The authors presented many surprising results that say a high percentage of farmers nearly 84% used mobile applications for farm management and many other farmers used it for real-time monitoring, data collection and experimental work. Johannes et al [14] presented a novel image processing method for disease identification with the help of hot-spot detection and statistical inference methods using mobile devices. Toseef and Khan [15] developed a mobile application using a fuzzy inference system for crop diagnosis and it achieved an accuracy of 99 %.…”

Section: Literature Surveymentioning

confidence: 99%

Plant Disease Diagnosis and Solution System Based on Neural Networks

Reddy¹,

Reddy²,

Sushanth³

et al. 2021

INDJCSE

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Plant diseases are one of the major factors affecting crop yield. Early identification of these diseases can improve productivity and save money and time for the farmer. This paper presents a novel technique to diagnose plant diseases using a mobile application. A Convolutional Neural Network (CNN) model was built and trained using MobileNetV2 architecture with the help of image processing techniques and transfer learning. A dataset comprising 87,000 images that contain 38 classes of diseases belonging to 14 different crops was used to train the model. The model achieved an accuracy of 98.69% and a loss of 0.5373. A mobile application was built in Android Studio with the help of a trained model. The mobile application built works without a need for a remote server. The application can identify the disease, gives information regarding the identified disease and also suggests necessary remedies to tackle the disease.

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Automatic plant disease diagnosis using mobile capture devices

Cited by 11 publications

References 23 publications

Deep learning for classification and severity estimation of coffee leaf biotic stress

Deep learning for classification and severity estimation of coffee leaf biotic stress

Plant Disease Detection and Classification by Deep Learning—A Review

Plant Disease Diagnosis and Solution System Based on Neural Networks

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