Monitoring and Discrimination of Plant Disease and Insect Pests based on agricultural IOT

Wang, Xin; Wang, Z.; Zhang, S.W.; Shi, Yun

doi:10.2991/icitmi-15.2015.21

“…The experts can collect diseases, pests, and insects information using data processing and mining, as well as sensor nodes. [22] Fuzzy Internet of Things (IoT) Some technologies have been applied for the detection system, such as web-based, mobile-based, and internet of things (IoT). Furthermore, the dominant approaches are expert system and deep learning.…”

Section: Image Processing Systemmentioning

confidence: 99%

“…Forward Chaining [1], Fuzzy system [2], K-Means Clustering [3] [4], Decision Tree [5] [6], Bayesian networks and incremental learning [7], Naïve Bayes and Certainty Factor [8], Naïve Bayes [9], Computer vision and artificial intelligence [10], Deep Convolutional Neural Network [11] [12], Fuzzy inference system [13], Convolutional Neural Network (CNN) [14], Fractal Dimension Values and Fuzzy C-Means [15], Deep learning [16] [17], Machine learning [18]. Furthermore, there are some applications for detection system of plant pests and diseases using technologies as follows: expert system [1] [19], mobile system [6] [12], computer vision and artificial intelligence [10] [20], image processing system [15] [16] [18] [20], and Internet of Things (IoT) [21] [22] [23]. However, research in this area is still needed, especially from the computer science perspective.…”

Section: Introductionmentioning

confidence: 99%

Technologies, methods, and approaches on detection system of plant pests and diseases

Anamisa¹,

Yusuf²,

Agustiono³

et al. 2019

EECSI

0

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This research aims to identify the technology, methods, approaches applied in developing plant pest and disease detection systems. For this purpose, it mainly reviews systematically related research on identification, monitoring, detection, and control techniques of plant pests and diseases using a computer or mobile technology. Evidence from the literature shows previous both academia and practitioners have used various technologies, methods and approaches for developing detection system of plant pests and diseases. Some technologies have been applied for the detection system, such as web-based, mobile-based, and internet of things (IoT). Furthermore, the dominant approaches are expert system and deep learning. While backward chaining, forward chaining, fuzzy model, genetic algorithm (GA), K-means clustering, Bayesian networks and incremental learning, Naïve Bayes and Certainty Factors, Convolutional Neural Network, and Decision Tree are the most frequently methods applied in the previous researches. The review also indicated that no single technology or technique is best for developing accurate pest/disease detection system. Instead, the combination of technologies, methods, and approaches resulted in different performance and accuracies. A possible explanation for this is because the systems are used for detecting, controlling and monitoring various plants, such as corn, onion, wheat, rice, mango, flower, and others that are different. This research contributes by providing a reference for technologies, methods, and approaches to the detection system for plant pests and diseases. Also, it adds a way of literature review. This research has implications for researchers as a reference for researching in the computer system, especially for the detection of plant pest and disease research. Hence, this research also extends the body of knowledge of the intelligence system, deep learning, and computer science. For practice, the method references can be used for developing technology for detecting plant pest and disease.

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“…It is utilized to detect disease and insect pest information using data processing and mining as well as sensor nodes. Wang et al [22] captured the implementation of association rule mining and fuzzy reasoning information fusion in the controlling plant disease and insect pests. The proposed model is obtaining environment information and the apriori algorithm to implement the real-time monitoring system based on environmental parameters and the presence of diseases and insect pests in the plant.…”

Section: Related Workmentioning

confidence: 99%

Technologies, methods, and approaches on detection system of plant pests and diseases

Anamisa¹,

Yusuf²,

Agustiono³

et al. 2019

EECSI

0

View full text Add to dashboard Cite

This research aims to identify the technology, methods, approaches applied in developing plant pest and disease detection systems. For this purpose, it mainly reviews systematically related research on identification, monitoring, detection, and control techniques of plant pests and diseases using a computer or mobile technology. Evidence from the literature shows previous both academia and practitioners have used various technologies, methods and approaches for developing detection system of plant pests and diseases. Some technologies have been applied for the detection system, such as web-based, mobile-based, and internet of things (IoT). Furthermore, the dominant approaches are expert system and deep learning. While backward chaining, forward chaining, fuzzy model, genetic algorithm (GA), K-means clustering, Bayesian networks and incremental learning, Naïve Bayes and Certainty Factors, Convolutional Neural Network, and Decision Tree are the most frequently methods applied in the previous researches. The review also indicated that no single technology or technique is best for developing accurate pest/disease detection system. Instead, the combination of technologies, methods, and approaches resulted in different performance and accuracies. A possible explanation for this is because the systems are used for detecting, controlling and monitoring various plants, such as corn, onion, wheat, rice, mango, flower, and others that are different. This research contributes by providing a reference for technologies, methods, and approaches to the detection system for plant pests and diseases. Also, it adds a way of literature review. This research has implications for researchers as a reference for researching in the computer system, especially for the detection of plant pest and disease research. Hence, this research also extends the body of knowledge of the intelligence system, deep learning, and computer science. For practice, the method references can be used for developing technology for detecting plant pest and disease.

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“…Showed 83% accuracy in the detection [8]. X. F. Wang1, Z. Wang et.al.proposed data mining and fusion based solution with environmental information and apriority algorithm [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Social Innovation through Precision Farming: Leaf Disease Detection System Using IoT

Chavan¹,

Kothavale²,

Patil³

2019

IJRAT

0

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Precision farming is a need of today's world. In last few decades image processing with IOT domain is rapidly increasing. In IOT domain we can easily use to multiple sensors for sensing unreadable format data. By using image processing domain we can easily work on complicated image to process efficiently. The plant disease detection is based on food security detection. The food security is based on food effect as well as environment. If food environment is not good then automatically food quality will loss. So we have to implement plant disease detection system using IOT and image processing. In this paper we have used four different sensors. 1. PH Sensor 2. Temperature sensor 3. Humidity sensor 4. Soil Moisture Sensor. We can collect data through Raspberry pies well as collect plant images. The main goal of the proposed work is to monitor the plant leaf, detect and classify them according to the diseases using the data mining and image processing techniques. Geological condition is extraordinary for farming in light of the fact that it gives numerous good conditions. By collecting, the information from various types of sensors predicts the diseases that can affect the leaf. We have implemented the classification and clustering algorithm to sort out good quality and bad quality plant detection. Our segmentation approach and utilization of support vector machine demonstrate disease classification over 300images with an accuracy of 90%.

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Monitoring and Discrimination of Plant Disease and Insect Pests based on agricultural IOT

Abstract: Plant disease and insect pests; Agriculture Internet of things (IOT); monitoring discriminant model; data mining and information fusion.

Cited by 15 publications

References 10 publications

Technologies, methods, and approaches on detection system of plant pests and diseases

Technologies, methods, and approaches on detection system of plant pests and diseases

Technologies, methods, and approaches on detection system of plant pests and diseases

Social Innovation through Precision Farming: Leaf Disease Detection System Using IoT

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