A novel cloud computing based smart farming system for early detection of borer insects in tomatoes

Rupanagudi, Sudhir Rao; Ranjani, B. S.; Nagaraj, Prathik; Bhat, Varsha G.; Thippeswamy, G.

doi:10.1109/iccict.2015.7045722

Cited by 66 publications

(22 citation statements)

References 16 publications

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“…Automation of farms systems include agricultural robots, specialized sensors, real-time kinematics and internet of things (IOT) based setups. [5][6][7][8][9][10]. Talking more about these systems, the task is to control the application specific fertilizers, pest/water sprays, seed planters, fruit/vegetable picker, with the help of machine vision, cameras, range and distance sensors and actuators.…”

Section: Introductionmentioning

confidence: 99%

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Hardware Software Co-Design of a Farming Robot

Phanomchoeng¹,

Saadi²,

Sasithong³

et al. 2020

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Food means life and no one can think about living without food. This is the most fundamental human necessity and food security is one of the major global concern of this century. With the revolution and recent advancements in the field of electronics and communication, there has been a paradigm shift from conventional farming ways to the modern one. This paper talks about the development of hardware software co-design of agricultural farming robot. Our developed farming robot has two parts namely hardware part which further consists of mechanical, electrical, control and tools segments and the software part which allows user to interact with the farming robot via cloud service. Our proposed hardware architecture is compatible with commercial Farmbot product and the developed web-based software can be extended for more features and applications. Furthermore, the developed robot has been tested and it works well.

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Section: Introductionmentioning

confidence: 99%

“…Farm robots are getting a lot attention these days because of their several advantages over manual system [5][6][7][8][9]. This work is an extension of our previous works [14][15].…”

Section: Introductionmentioning

confidence: 99%

Hardware Software Co-Design of a Farming Robot

Phanomchoeng¹,

Saadi²,

Sasithong³

et al. 2020

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“…Currently, there have existed some methods to detect plant diseases or insect with image processing and computer vision technologies [3]. For example, Ali et al used color histogram and textural descriptors to detect citrus diseases [4].…”

Section: Introductionmentioning

confidence: 99%

Agricultural Robot for Intelligent Detection of Pyralidae Insects

Hu¹,

Liu²,

Zhao³

2019

Agricultural Robots - Fundamentals and Applications

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The Pyralidae insects are one of the main pests in economic crops. However, the manual detection and identification of Pyralidae insects are labor intensive and inefficient, and subjective factors can influence recognition accuracy. To address these shortcomings, an insect monitoring robot and a new method to recognize the Pyralidae insects are presented in this chapter. Firstly, the robot gets images by performing a fixed action and detects whether there are Pyralidae insects in the images. The recognition method obtains the total probability image by using reverse mapping of histogram and multi-template images, and then image contour can be extracted quickly and accurately by using constraint Otsu. Finally, according to the Hu moment characters, perimeter, and area characters, the contours can be filtrated, and recognition results with triangle mark can be obtained. According to the recognition results, the speed of the robot car and mechanical arm can be adjusted adaptively. The theoretical analysis and experimental results show that the proposed scheme has high timeliness and high recognition accuracy in the natural planting scene.

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“…For image processing, we have converted the image to the Cr color model using the method colorconversion(), [6], [5], [7] and then performed erosion and dilation on these images by calling the methods erosion() and dilation() respectively. Erosion and dilation are performed in order to remove the noise/external disturbances in the image.…”

Section: Methodsmentioning

confidence: 99%

“…Here we are attempting to develop the video oculography system [5] which would work on the system having minimum specification so that would help the lower economical class as well.…”

Section: Purposementioning

confidence: 99%

Video Oculographic System using Real-Time Video Processing

Gambhir¹,

Boppaiah²,

Subbaiah³

et al. 2015

IJCA

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Video Oculography (VOG) is a non-invasive, video based method of measuring horizontal, vertical and torsion position components of movements of both eyes using a head mounted mask that is equipped with small cameras. There are systems which use various techniques to detect the opening and closing of the eyes. The technique used in our project is simple and not time consuming. Using blinks to select a sentence or word and playing the sentence has not been used widely in the past. In our project we want to implement this and also build a system whose functionality can be extended to serve a variety of applications. In this system, we use a camera to take input from the user. The user blinks in front of the camera. This is taken as a video sequence and then it becomes the input to blink detection and counting code. The number of blinks is thus calculated and the sentence corresponding to this count is played. This system works with minimum specification. This would help the lower economical class. There are several methods which can be used to detect the eyes, the method which we have used in our project reduces the time taken for blink detection and also reduces the cost of the system by a large margin.

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A novel cloud computing based smart farming system for early detection of borer insects in tomatoes

Cited by 66 publications

References 16 publications

Hardware Software Co-Design of a Farming Robot

Hardware Software Co-Design of a Farming Robot

Agricultural Robot for Intelligent Detection of Pyralidae Insects

Video Oculographic System using Real-Time Video Processing

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