Heterogeneous Internet of things organization Predictive Analysis Platform for Apple Leaf Diseases Recognition

Pandiyan, Sanjeevi; Ashwin, M.; Manikandan, R.; Raghunath, K. M. Karthick; Raman, G. Venkat

doi:10.1016/j.comcom.2020.02.054

Cited by 24 publications

(10 citation statements)

References 9 publications

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“…The Heterogeneous IoT method for the detection of leaf disease is based on the identification of gestures of the leaf image and to find out the leaf and fruit that are suffering most from the disease and pointed out the disease in the leaf [23]. Detecting the stem diseases of jute plants using an Android application, by taking the pictures of stems of a jute plant affected by the disease and sending them to the cloud server for analysis through machine learning technique [24].…”

Section: Crop Disease Monitoringmentioning

confidence: 99%

IoT-Smart Agriculture: Comparative Study on Farming Applications and Disease Prediction of Apple Crop using Machine Learning

Islam

Jamwal²,

Mir³

et al. 2022

Iraqi Journal of Science

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Recently, the Internet of Things has emerged as an encouraging technology that is scaling up new heights towards the modernization of real word physical objects into smarter devices in several domains. Internet of Things (IoT) based solutions in agriculture drives farming into a smart way through the proliferation of smart devices to enhanced production with minimal human involvement. This paper presents a comprehensive study of the role of IoT in prominent applications of farming, wireless communication protocols, and the role of sensors in precision farming. In this research article, the existing frameworks in IoT-based agriculture systems with relevant technologies are presented. Furthermore, the comparative analysis of the apple disease prediction system concerning different types of disease found in the apple crop are discussed. In addition, this paper presents the contributions made by numerous researchers over the past few years in the apple disease prediction system. The aim of this research is to support the development of smart agriculture applications that would be helpful in precision farming for the optimization of resources with the help of IoT in agriculture and early disease prediction in apple crops.

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Section: Crop Disease Monitoringmentioning

confidence: 99%

IoT-Smart Agriculture: Comparative Study on Farming Applications and Disease Prediction of Apple Crop using Machine Learning

Islam

Jamwal²,

Mir³

et al. 2022

Iraqi Journal of Science

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show abstract

“…The proposed model was compared with existing models for accuracy and efficiency. Sanjeevi Pandiyan et al [147], applied the concepts of image segmentation and IoT, to develop a system/platform that can detect the diseases in plants. Authors proposed a novel platform having an Advanced Segmented Dimension Extraction (ASDE) with Heterogeneous Internet of Things procedural (HIoT) aspects, to detect the apple leaf diseases.…”

Section: Iot In Disease Detectionmentioning

confidence: 99%

Recent Developments of the Internet of Things in Agriculture: A Survey

2020

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A rise in the population has immensely increased the pressure on the agriculture sector. With the advent of technology, this decade is witnessing a shift from conventional approaches to the most advanced ones. The Internet of Things (IoT) has transformed both the quality and quantity of the agriculture sector. Hybridization of species along with the real-time monitoring of the farms paved a way for resource optimization. Scientists, research institutions, academicians, and most nations across the globe are moving towards the practice and execution of collaborative projects to explore the horizon of this field for serving mankind. The tech industry is racing to provide more optimal solutions. Inclusion of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide sufficient scope to predict, process, and analyze the situations and improve the activities in the real-time scenario. The concept of heterogeneity and interoperability of the devices by providing flexible, scalable, and durable methods, models are also opening new domains in this field. Therefore, this paper contributes towards the recent IoT technologies in the agriculture sector, along with the development of hardware and software systems. The public and private sector projects and startup's started all over the globe to provide smart and sustainable solutions in precision agriculture are also discussed. The current scenario, applications, research potential, limitations, and future aspects are briefly discussed. Based on the concepts of IoT a precision farming framework is also proposed in this article.

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“…In literature [25], the ResNet model is combined with a faster R-CNN detector, and many comparative tests illustrate that the modified network has excellent performance in disease classification and identification of greenhouse crop leaves. Literature [26] used the ResNet50 model to identify grape yellow disease and compared other six CNN models (e.g., VGGNet and ResNet18). After the performance comparison test, the accuracy of classification rate of the ResNet50 reached 99.18%.…”

Section: Introductionmentioning

confidence: 99%

Research on Strawberry Disease Diagnosis Based on Improved Residual Network Recognition Model

Yan

2022

Mathematical Problems in Engineering

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Considering the problems of high cost, inefficiency, and time consumption of manual diagnosis of strawberry diseases, G-ResNet50 is proposed based on transfer learning and deep residual network for strawberry disease identification and classification. The G-ResNet50 is based on the ResNet50, and the focal loss function is introduced in G-ResNet50 to make the model devote itself to disease images that are difficult to classify. During the training process of the G-ResNet50 model, its convolutional layer and pooling layer inherit the pre-trained weight parameters from the ResNet50 model on the PlantVillage dataset, while adding dropout regularization and batch regularization methods to optimize the network model. The strawberry disease dataset includes four sample images of healthy plants, powdery mildew, strawberry anthracnose, and leaf spot disease. The dataset is enhanced and expanded by operations including angle rotation, adjusting contrast and brightness, and adding Gaussian noise. Compared with existing models such as VGG16, ResNet50, InceptionV3, and MobileNetV2, the results of model training and testing on 7,525 four-category leaf datasets show that the G-ResNet50 model has faster convergence speed and better classification effect, and its average recognition accuracy rate reached 98.67%, which is significantly higher than other models. Through the three evaluation indicators of precision rate, recall rate, and confusion matrix, it is concluded that the G-ResNet50 has good robustness, high stability, and high recognition accuracy and can provide a feasible solution for strawberry disease detection in practical applications.

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Heterogeneous Internet of things organization Predictive Analysis Platform for Apple Leaf Diseases Recognition

Cited by 24 publications

References 9 publications

IoT-Smart Agriculture: Comparative Study on Farming Applications and Disease Prediction of Apple Crop using Machine Learning

IoT-Smart Agriculture: Comparative Study on Farming Applications and Disease Prediction of Apple Crop using Machine Learning

Recent Developments of the Internet of Things in Agriculture: A Survey

Research on Strawberry Disease Diagnosis Based on Improved Residual Network Recognition Model

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