Design of Green Agriculture System Using Internet of Things and Image Processing Techniques

Tran, Hung Thuan; Ngo, Ha Quang Thinh; Nguyễn, Thanh Phương; Nguyen, Hung T.

doi:10.1109/gtsd.2018.8595663

Cited by 16 publications

(4 citation statements)

References 3 publications

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“…On entering the required data the application sends the necessary data to the rover via IoT platform for completing the setup process of the rover. After successful set-up of the rover, the application checks for the successful communication between the rover and the application [6]. The application send control commands to the rover inaccordance to the arrow keys pressed by the operator.…”

Section: Design Methodologymentioning

confidence: 99%

Crop State Analysis Using Image Processing

Khandagale¹,

Eranjikal²,

Parab³

et al. 2022

ITM Web Conf.

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Agriculture is the primary source of livelihood for the majority of India’s population. In the traditional agriculture system, farmers have to manually check their crops in every section of the farm. This may at times leave some sections unchecked. Due to this, crop health may deteriorate considerably. If may not be treated in time, the farmer will face a huge loss and the crop yield will also reduce. To overcome this problem, a system can be employed to monitor the crops and out the sections where crop condition is worsening. For this, an image processing algorithm can be implemented. Through this system, the received data from the algorithm will be manipulated to enhance the crop’s health.

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Section: Design Methodologymentioning

confidence: 99%

Crop State Analysis Using Image Processing

Khandagale¹,

Eranjikal²,

Parab³

et al. 2022

ITM Web Conf.

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“…Procesiranje podataka dobijenih posredstvom IoT koncepta predstavljeno je u radovima [6], [7] i [8], gde je korišćena analiza slike kako bi se dobile nove informacije. Slična primena može se sagledati i kod analize slike semena malih dimenzija koje je teže ručno izmeriti za kratko vreme, što se može videti na primeru nadgledanje klijavosti semena koja pripadaju familiji kupusnjača (Brassica) [9].…”

Section: Uvodunclassified

Detection of oil rape seed losses before harvest by Image analysis within Fog computing

Marković¹,

Koprivica²,

Veljković³

et al. 2022

Poljoprivredna tehnika

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Losses in the production of oilseed rape can occur before harvest, caused by the spontaneous opening of mature pods and the fall of seeds on the ground. Different seed pods, among other things, ripen on the same plant at different times. So, in the cultivation of similar crops, one of the most critical moments is determining the right time for harvesting, because late harvest implies overripeness and opening of the shell, which leads to seed spoilage, losses and mechanical damage during threshing. One way of monitoring seed shedding and thus potential losses is by placing a container between rows of oilseed rape plants and monitor the number of seeds that fall from open shells. The presented model of the system, which consists of sensor devices with associated cameras, positioned above the position, has a function to transmit images of the current state. Central to this paper is an image analysis application that can be performed near sites on computer-aided devices within Fog Computing. In this way, the results of the analysis of images on the number of seeds in the container are obtained almost immediately and can be forwarded to the Cloud platform or directly to the user who will take appropriate action. By obtaining timely information on the number of scattered seeds, it is possible to organize the harvest in an optimal way in order to avoid losses and prevent over-ripeness of oilseed rape.

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“…Image processing techniques help to identify diseases more accurate and its results can be obtained in lesser time [8]. Many researchers have used image processing in detecting diseases at early stages for agricultural or economic crops [9][10][11]. Islam et al [12] used a technique that lead to a faster way of identifying and classifying the rice diseases in agrofield.…”

Section: Literature Reviewmentioning

confidence: 99%

Bacterial Leaf Blight (BLB) Disease Detection Using Hue, Saturation, Value (HSV) Band Threshold Method

Husin

Taujuddin

Sari

et al. 2021

Lecture Notes in Electrical Engineering

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In this research, image processing is used to enhance the traditional way of paddy diseases detection which is done through manual observation by the farmers at the paddy field. Manual observation by farmers requires deep knowledge and full understanding on recognizing the paddy diseases. The knowledge in detecting the paddy diseases is gained through years of observation and experience. Therefore, an appropriate technique is proposed and expected to assist the farmers in detecting the paddy diseases. This paper discussed the method on capturing the sample images using drone, and a technique used in image processing to detect Bacterial Leaf Blight (BLB) disease. The Hue, Saturation, Value (HSV) Band Threshold method consists of pre-processing process, HSV band threshold estimation value, masking, and morphological operation process. In estimating the HSV band threshold, the

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Design of Green Agriculture System Using Internet of Things and Image Processing Techniques

Cited by 16 publications

References 3 publications

Crop State Analysis Using Image Processing

Crop State Analysis Using Image Processing

Detection of oil rape seed losses before harvest by Image analysis within Fog computing

Bacterial Leaf Blight (BLB) Disease Detection Using Hue, Saturation, Value (HSV) Band Threshold Method

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