Disease and pest infection detection in coconut tree through deep learning techniques

Singh, Piyush; Verma, Abhishek; Alex, John Sahaya Rani

doi:10.1016/j.compag.2021.105986

Cited by 74 publications

(38 citation statements)

References 17 publications

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“…Singh et al proposed a segmentation approach by employing K-means clustering, watershed segmentation, and threshold-based exemption on coconut leaf image datasets to detect leaf blight disease and attained a 96.94% detection accuracy by employing CNN [44]. Tassis et al proposed an approach by employing mask R-CNN, for instance, segmentation and removal of the background using semantic segmentation by employing UNet and attained a 94.27% detection accuracy on coffee plant disease detection [45].…”

Section: Literature Surveymentioning

confidence: 99%

Cardamom Plant Disease Detection Approach Using EfficientNetV2

2022

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Cardamom is a queen of spices. It is indigenously grown in the evergreen forests of Karnataka, Kerala, Tamilnadu, and the northeastern states of India. India is the third largest producer of cardamom. Plant diseases cause a catastrophic influence on food production safety; they reduce the eminence and quantum of agricultural products. Plant diseases may cause significantly high loss or no harvest in dreadful cases. Various diseases and pests affect the growth of cardamom plants at different stages and crop yields. This study concentrated on two diseases of cardamom plants, Colletotrichum Blight and Phyllosticta Leaf Spot of cardamom and three diseases of grape, Black Rot, ESCA, and Isariopsis Leaf Spot. Various methods have been proposed for plant disease detection, and deep learning has become the preferred method because of its spectacular accomplishment. In this study, U 2 -Net was used to remove the unwanted background of an input image by selecting multiscale features. This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model. A comprehensive set of experiments was carried out to ascertain the performance of the proposed approach and compare it with other models such as EfficientNet and Convolutional Neural Network (CNN). The experimental results showed that the proposed approach achieved a detection accuracy of 98.26%.

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Section: Literature Surveymentioning

confidence: 99%

Cardamom Plant Disease Detection Approach Using EfficientNetV2

2022

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“…Singh et al [ 30 ] developed a deep learning model for disease and pest infection detection in a coconut tree. The images are first segmented using the k -means algorithm.…”

Section: Related Work: a Literature Reviewmentioning

confidence: 99%

“…The next experiments seek to validate the applicability of the proposed H2ID framework for identifying disease infectious. To reach this conclusion, several comparisons have been made with the state-of-the-art algorithms (InceptionResNet [ 30 ], and DenseNet [ 28 ]). We first measure the runtime performance and then determine the quality performance.…”

Section: Performance Evaluationmentioning

confidence: 99%

An edge-driven multi-agent optimization model for infectious disease detection

et al. 2022

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This research work introduces a new intelligent framework for infectious disease detection by exploring various emerging and intelligent paradigms. We propose new deep learning architectures such as entity embedding networks, long-short term memory, and convolution neural networks, for accurately learning heterogeneous medical data in identifying disease infection. The multi-agent system is also consolidated for increasing the autonomy behaviours of the proposed framework, where each agent can easily share the derived learning outputs with the other agents in the system. Furthermore, evolutionary computation algorithms, such as memetic algorithms, and bee swarm optimization controlled the exploration of the hyper-optimization parameter space of the proposed framework. Intensive experimentation has been established on medical data. Strong results obtained confirm the superiority of our framework against the solutions that are state of the art, in both detection rate, and runtime performance, where the detection rate reaches 98% for handling real use cases.

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“…Field surveys attempt to detect the reality of the ground using farmers' reports and objective surveys. Due to sampling mistakes and non-sampling, these studies suffer from decrease in replies, resource constraints and dependability [16]. Process-oriented crop models simultaneously increase crops and develop crop by inputs depending on crop characteristics and environmental circumstances.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Satellite data readings only offer indirect measures of the agricultural yield, specifically measured irradiance, so as to translate satellite data into yield predictions on physicochemical or analytical frameworks. Statistics models employ weather indicators and predictors for the results of the three preceding techniques [16,17]. These models assess the yield rate trend for the development and management of genetics and fit linear models between predictors and residues.…”

Section: Literature Reviewmentioning

confidence: 99%

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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Disease and pest infection detection in coconut tree through deep learning techniques

Cited by 74 publications

References 17 publications

Cardamom Plant Disease Detection Approach Using EfficientNetV2

Cardamom Plant Disease Detection Approach Using EfficientNetV2

An edge-driven multi-agent optimization model for infectious disease detection

Pest Detection in Plants Using Convolutional Neural Network

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