Plant Disease Detection using Internet of Thing (IoT)

Nawaz, Muhammad Amir; Khan, Tehmina; Mudassar, Rana; Kausar, M Shafi; Usman, Amir; Fatima, Tanvir; Ahmad, Rizwan; Ahmad, Jaleel

doi:10.14569/ijacsa.2020.0110162

Cited by 27 publications

(9 citation statements)

References 10 publications

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“…Relying on the existing deep learning methods can not effectively identify the fine-grained characteristics of diseases and pests that exist naturally in the application of the above actual agricultural scenarios, resulting in technical difficulties such as low identification accuracy and generalization robustness, which has long restricted the performance improvement of decision-making management of diseases and pests by the Intelligent Agricultural Internet of Things [ 104 ]. The existing research is only suitable for fine-grained identification of fewer class of diseases and pests, can not solve the problem of large-scale, large-category, accurate and efficient identification of diseases and pests, and is difficult to deploy directly to the mobile terminals of smart agriculture.…”

Section: Challengesmentioning

confidence: 99%

Plant diseases and pests detection based on deep learning: a review

2021

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Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

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Section: Challengesmentioning

confidence: 99%

Plant diseases and pests detection based on deep learning: a review

2021

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“…Nawaz et. al [18] proposed a framework for identifying the type of leaf is constructed. The suggested approach uses sensor devices to determine the leaves' temperature, stickiness, and shade.…”

Section: Iot and Deep Learningmentioning

confidence: 99%

Intelligent Plant Disease Diagnosis Using Machine Learning Techniques- A Review

Kem,

Chauhan,

Agnihotri

et al. 2024

IJIRCST

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No matter how we look at it, there are no chances to live without the flora surrounding us. One can be troubled by a broad range of diseases that attack the integrity of his or her health. Basically, all the plant parts are fruits, stems, roots, leaves, and so on. The time and money in terms of having successfully figured out the disease of a plant are much less than that if a diagnosis error has been made. Sustained economic losses caused by plant disease are due to the facilitation of rot production, which involves the reduction of agricultural product yields and quantities. Creating measures that would halt the destruction of crops due to plant diseases is essential since the contributing factor of 70% of agricultural produce to GDP is high. This group of illnesses must be watched closely since the diseases start as soon as the plants have begun their growing process. The conventional approach to surveillance at this point specifically is to carry out an examination, which is quite costly in terms of money. Automated for faster and more effective processing of this operation. Many researchers, by using various methods, have created networks that are mostly exemplified in diverse forms. It is also worthwhile to note that in the field of agriculture, it is very important that the plants are sorted by type. Diagnosis on pathology datasets with the aid of image feature extraction and transformation methods that are appropriate to the illness.

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“…An IoT-based farming system that connects the agriculture farms with endusers where internet connectivity is provided to sensors and controllers in the farm so that the end-user will be allowed to monitor and control the connected farm through a smartphone application [52]. [53] proposed a framework that identifies the disease in the plant while utilizing the sensor's information of the plant leaves such as temperature, moistness, and shading. The technology of wireless sensor network plays an important role in agriculture.…”

Section: Existing Framework In Precision Agriculturementioning

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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Plant Disease Detection using Internet of Thing (IoT)

Cited by 27 publications

References 10 publications

Plant diseases and pests detection based on deep learning: a review

Plant diseases and pests detection based on deep learning: a review

Intelligent Plant Disease Diagnosis Using Machine Learning Techniques- A Review

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

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