Convolutional Neural Network for Automatic Identification of Plant Diseases with Limited Data

Afifi, Ahmed; Alhumam, Abdulaziz; Abdelwahab, Amira

doi:10.3390/plants10010028

Cited by 66 publications

(32 citation statements)

References 36 publications

(55 reference statements)

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“…Using an artificial neural network and a support vector machine, the pattern is identified for each species of plant. SVM has a 96.67 percent accuracy rating, whereas ANN has a 92 percent accuracy rating [8]. Ray et al ( 2017) devised a method for detecting fungal illness at an early stage, and correctly diagnosing the disease aids in its prevention.…”

Section: Literature Reviewmentioning

confidence: 99%

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Pest Detection in Plants Using Convolutional Neural Network

Sharma¹

2021

IJRASET

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Agriculture or farming is an imperative occupation since the historical backdrop of humanity is kept up. Artificial Intelligence is leading to a revolution in the agricultural practices. This revolution has safeguarded the crops from being affected by distinct factors like climate changes, porosity of the soil, availability of water, etc. The other factors that affect agriculture includes the increase in population, changes in the economy, issues related to food security, etc. Artificial Intelligence finds a lot of applications in the agricultural sector also which includes crop monitoring, soil management, pest detection, weed management and a lot more. Significant problems for sustainable farming include detection of illness and healthy monitoring of plants. Therefore, plant disease must automatically be detected with higher precision by means of image processing technology at an early stage. It consists of image capturing, preprocessing images, image segmentation, extraction of features and disease classification. The digital image processing method is one of those strong techniques used far earlier than human eyes could see to identify the tough symptoms. Considering different climatic situations in various regions of the world that impact local weather conditions. These climate changes affect crop yield directly. There is a great demand for such a platform in the world of today which would enable the farmer market his farm products. We have proposed in this study a system where farmers can sell their products directly to customers without the intervention of distributors and traders. The predictive analytics system is necessary for the farmer to get the maximum yield which benefit the farmer. This may be done if the environment, market conditions and knowledge of the timely planning of farms are known properly. Keywords: Pest Detection, Artificial Intelligence, Agriculture, Image processing, Convolutional Neural Networks

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Section: Literature Reviewmentioning

confidence: 99%

“…Image-In the convolution neural network method, net classification is highly effective. For domain-specific training, transfer learning is also employed [8,9]. When taught and evaluated for a variety of species, the results suggest that it is more accurate.…”

Section: Literature Reviewmentioning

confidence: 99%

Pest Detection in Plants Using Convolutional Neural Network

Sharma¹

2021

IJRASET

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“…The restrictions of the traditional approach have motivated researchers to develop technological proposals for the early identification of crop diseases in an accurate, fast, and reliable manner, and in order to meet the increasing demands of consumers and alleviate the environmental impact of chemical inputs on the environment and health. In this regard, several methods [6][7][8][9] have been proposed to automate the process of disease detection. These methods for automatic recognition of crop diseases are divided into two groups, direct and indirect methods [10].…”

Section: Introductionmentioning

confidence: 99%

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

2021

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The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection.

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“…It was demonstrated that deep learning approaches outperform machine learning algorithms, such as support vector machine, random forest, stochastic gradient descent [ 38 ]. The development of the deep learning methods towards better disease recognition in plants included transfer learning approaches [ 39 ], implementing networks of various architectures [ 37 , 40 , 41 , 42 , 43 , 44 ], working with the limited amount of data [ 45 ], and Bayesian deep learning [ 46 ].…”

Section: Introductionmentioning

confidence: 99%

Image-Based Wheat Fungi Diseases Identification by Deep Learning

Genaev

Сколотнева

Gultyaeva

et al. 2021

Plants

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Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many cultures are exposed to them. The disease is difficult to control on a large scale; thus, one of the relevant approaches is the crop field monitoring, which helps to identify the disease at an early stage and take measures to prevent its spread. One of the effective control methods is disease identification based on the analysis of digital images, with the possibility of obtaining them in field conditions, using mobile devices. In this work, we propose a method for the recognition of five fungal diseases of wheat shoots (leaf rust, stem rust, yellow rust, powdery mildew, and septoria), both separately and in case of multiple diseases, with the possibility of identifying the stage of plant development. A set of 2414 images of wheat fungi diseases (WFD2020) was generated, for which expert labeling was performed by the type of disease. More than 80% of the images in the dataset correspond to single disease labels (including seedlings), more than 12% are represented by healthy plants, and 6% of the images labeled are represented by multiple diseases. In the process of creating this set, a method was applied to reduce the degeneracy of the training data based on the image hashing algorithm. The disease-recognition algorithm is based on the convolutional neural network with the EfficientNet architecture. The best accuracy (0.942) was shown by a network with a training strategy based on augmentation and transfer of image styles. The recognition method was implemented as a bot on the Telegram platform, which allows users to assess plants by lesions in the field conditions.

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Convolutional Neural Network for Automatic Identification of Plant Diseases with Limited Data

Cited by 66 publications

References 36 publications

Pest Detection in Plants Using Convolutional Neural Network

Pest Detection in Plants Using Convolutional Neural Network

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

Image-Based Wheat Fungi Diseases Identification by Deep Learning

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