RiceTalk: Rice Blast Detection Using Internet of Things and Artificial Intelligence Technologies

Chen, Wenliang; Lin, Yi‐Bing; Ng, Fung-Ling; Liu, Chun-You

doi:10.1109/jiot.2019.2947624

Cited by 157 publications

(62 citation statements)

References 13 publications

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“…The model was tested on detection of the diseases like Late Blight, Early Blight, and Powdery Mildew in tomato and potato crops. Wen-Liang Chen et al [151], based on AI technologies and IoT, developed a system named "RiceTalk" for the detection of Blight diseases in the rice plant. AgriTalk model was used as a base model in this work to develop this system.…”

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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Section: Iot In Disease Detectionmentioning

confidence: 99%

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

2020

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“…All those schemes and processing aspects are not ensuring the spotting of affected and unaffected area fruits. Hence proposed processes are described as the separation of affected and unaffected sekai-ichi apple fruit by using ontology-based Recognition and Isolation Guarantee detection factors [24,33,42]. At the same time, the spotting of affected sekai-ichi apple fruit location is somewhat Opposition preserving incompatible factors amongst segmentation and dimension extraction the spotting of unaffected sekai-ichi apple fruits.…”

Section: Boosted Continuous Non-spatial Whole Attribute Extraction (Bmentioning

confidence: 99%

“…To make optimal at level 1, the level 2 optimist and take an isolated view of the image comprises, From the above consideration, introduces a level 2 optimist clause two procedures namely of recognition and isolation spot are in equally shares the detection process [24,58]. Hence, the complete constituting the full quantity of triangle (1)…”

Section: "Division C" With Iot Framework For Console Assembly Withmentioning

confidence: 99%

“…Middle level establishing generalizable (reduced to a time-variant of detection process) by concentrating the time-varying features in the occurrence of a change for the worse identification [24]. After a specified (that would take by the middle level) period or an especially fewer delay, the upper level is express for features refining with the help of the function of sekai-ichi apple image segmentation [25,26].…”

Section: Introductionmentioning

confidence: 99%

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An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

Sanjeevi

Kumar

Prasanna

et al. 2020

Complex Intell. Syst.

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Constituting the agriculture solid substance manufacture, the post-harvest sector processing schema is direct to preventing reduce the losses in intelligent agriculture. Many processing schemata will be preventing post-harvest losses on the agriculture solid substance manufacture, especially sekai-ichi apple is the regularly used fruit also used to make active in human-related activities of the sensory and control function consisting of an agricultural industry. Sekai-ichi apple is being a definite number of diseases induce, but it is to the highest degree of wastage involving in the Post-Harvest process. Especially sekai-ichi apple count loss is an unsafe many time because it not critically post-harvest. Regardless of consideration, the existing hierarchical model specified post-harvest losses prevention research has deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. This paper suggests a "Hierarchical Model within Ontology Enabled IoT" for distinguishable healthy separation of sekai-ichi apple by using Boosted Continuous Non-spatial whole Attribute Extraction (BCNAE). Sekai-ichi apple count loss is always safe on critically post-harvest. Proposed Post-Harvest hierarchical model specified post-harvest losses prevention and deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. In these suggestions, the separation cognitive operation takes the three levels of processing schemes such as lower level, middle level, and higher level. Firstly, the lower level is express agreements with the dynamic functioning for maintaining the definite number of manual induces. This lower level showing an absorption with the activity of manual separation by the human reliability determination. Secondly, the middle level is an express arrangement with the dynamic functioning for reducing the overfitting and accommodate to fitting the right shape deliberation. Middle level is establishing being generalized by concentrating the time-varying features in the occurrence of a change for the worse identification. Finally, the upper level is express for features refining with the help of the function of sekai-ichi apple image segmentation connection. This interpretability process helps to make the proven position of a prominent classification in a particular fruit on the agriculture solid substance. These three processing flow constructs the ontology structure with manually collected sekai-ichi apple images from a 3D sensor. The observational consequences express that the proposed BCNAE framework recognizes a detection performance carrying out with an optimized-separation ratio for time-variant of the separation process.

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“…In [6], a smart farm platform called AgriTalk was developed for soil cultivation with a large number of sensors. The produced sensor data were used in several AI models to provide precise farming for soil microbiome and fertility, disease regulation, irrigation regulation, and pest regulation [7]. In this application, it is important to identify sensor failure and to calibrate or replace the failed sensors in real time so that the data measured by the sensors are accurate.…”

Section: Introductionmentioning

confidence: 99%

SensorTalk: An IoT Device Failure Detection and Calibration Mechanism for Smart Farming

Lin

Hung

2019

Sensors

Self Cite

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In an Internet of Things (IoT) system, it is essential that the data measured from the sensors are accurate so that the produced results are meaningful. For example, in AgriTalk, a smart farm platform for soil cultivation with a large number of sensors, the produced sensor data are used in several Artificial Intelligence (AI) models to provide precise farming for soil microbiome and fertility, disease regulation, irrigation regulation, and pest regulation. It is important that the sensor data are correctly used in AI modeling. Unfortunately, no sensor is perfect. Even for the sensors manufactured from the same factory, they may yield different readings. This paper proposes a solution called SensorTalk to automatically detect potential sensor failures and calibrate the aging sensors semi-automatically. Numerical examples are given to show the calibration tables for temperature and humidity sensors. When the sensors control the actuators, the SensorTalk solution can also detect whether a failure occurs within a detection delay. Both analytic and simulation models are proposed to appropriately select the detection delay so that, when a potential failure occurs, it is detected reasonably early without incurring too many false alarms. Specifically, our selection can limit the false detection probability to be less than 0.7%.

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RiceTalk: Rice Blast Detection Using Internet of Things and Artificial Intelligence Technologies

Cited by 157 publications

References 13 publications

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

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

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

SensorTalk: An IoT Device Failure Detection and Calibration Mechanism for Smart Farming

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