scholarly journals Weed Detection and Removal based on Image Processing

2020 ◽  
Vol 8 (6) ◽  
pp. 347-352
Keyword(s):  
Image Processing ◽  
Weed Control ◽  
Raspberry Pi ◽  
Image Processing Algorithm ◽  
Weed Detection ◽  
Agriculture Sector ◽  
Turn On ◽  
Weed Growth ◽  
Cutting System ◽  
Removal System

Agriculture, although known as the backbone of the Indian economy, is facing crisisin terms of production. One of the major issues in the agriculture sector is the growth of weeds among the crops. They compete with the desired plants for various resources and hence their growth must be inhibited. At present weeds are removed either manually, which is a time consuming and labour intensive task, or herbicides are being sprayed uniformly all over the field to keep them under check. Spraying of herbicide is very inefficient as the chemical contributes less to weed control and cause contamination of the environment. The main objective of this work is a weed control system that differentiates the weed from crops and restricts weed growth alone by the precise removal of the weed. This is implemented by capturing the images of the field at regular intervals and processing them with a Raspberry Pi board by making use of an image processing algorithm to differentiate the desired plants from the weeds. This is based on various features like colour and size of the crop and weed. Once the weeds are identified and located correctly through image processing, a signal is transmitted from the Raspberry Pi board to turn on the weed cutting system. The selective activation of the weed removal system helps in the precise removal of the weeds and this provides a better environment for the desired plants to grow well.

scholarly journals Designing the Yellow Head Virus Syndrome Recognition Application for Shrimp on an Embedded System

2019 ◽  
Vol 6 (2) ◽  
pp. 48-63
Author(s):  
Truong Quoc Bao ◽  
Tran Chi Cuong ◽  
Nguyen Dinh Tu ◽  
Le Hoang Dang ◽  
Luu Trong Hieu
Keyword(s):  
Image Processing ◽  
Embedded System ◽  
Food Industry ◽  
Virus Disease ◽  
Shrimp Farming ◽  
Raspberry Pi ◽  
Disease Group ◽  
Image Processing Algorithm ◽  
Yellow Head Virus ◽  
Farming Industry

One of the most serious problems confronted by the shrimp farming industry is the disease caused by the yellow head virus (YHV). This research proposes an image processing algorithm to detect, identify and eliminate shrimp with the yellow head virus from the Litopenaeus vannamei gathering lines. Using a Raspberry Pi 3 module with the support of the OpenCV library which may be associated with Niblack’s algorithm is primarily suitable for segmentation. First, the shrimp object was identified and separated from the background using the image segmentation technique and the boundary that surrounds the object. Then, identification of diseased shrimp was analysed based on colour threshold. In this study, the sample of shrimp disease group had the highest amount of ratio, with about 6% to 11%. Most of the samples without the disease had a ratio of 0%. The experimental results show that the system can identify and accurately determine the coordinates of shrimp with yellow head virus disease and send information to the shrimp classification system in the food industry.

scholarly journals Automatic Weed Detection and Smart Herbicide Sprayer Robot

2018 ◽  
Vol 7 (3.6) ◽  
pp. 115 ◽  
Author(s):  
G Y. Rajaa Vikhram ◽  
Rakshit Agarwal ◽  
Rohan Uprety ◽  
V N.S. Prasanth
Keyword(s):  
Weed Control ◽  
Raspberry Pi ◽  
Water Pump ◽  
Weed Detection ◽  
Ordinary Method ◽  
Control Framework

The ordinary method for murdering weeds (unwanted plants) in a harvest manor is to shower herbicides all over the estate. This outcomes in defilement of the sustenance crops and furthermore the yield turns out to be less as a portion of the production plants pass on alongside the weeds. In this way, there is a requirement for a brilliant weed control framework. In this venture, a picture handling calculation is utilized to take pictures of the manor columns at consistent interims and after recognizing the weeds in the captured image, the weed killer chemical is showered specifically and just on the weeds. The herbicide is put away in a compartment fitted with water pump engines joined to shower spouts. After the weeds are recognized, a flag is signaled from Raspberry Pi to the motor driver IC governing the water pump motors to shower the chemicals over the unwanted vegetation. 

scholarly journals AUTOMATED WEED REMOVAL SYSTEM USING CONVOLUTIONAL NEURAL NETWORK

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Mounashree J P ◽  
N Sanjay ◽  
Sushmitha B S ◽  
Usha B G ◽  
Anupama Shivamurthy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Weed Control ◽  
Raspberry Pi ◽  
Use Of Technology ◽  
Neural Network Algorithm ◽  
Removal System

Weeds are very annoying for farmers and also not very good for the crops. Its existence might damage the growth of the crops. Therefore, weed control is very important for farmers. Farmers need to ensure their agricultural fields are free from weeds for at least once a week, whether they need to spray weed herbicides to their plantation or remove it using tools or manually. The aim of this research is to build an automated weed control system. The system consists of motors, Raspberry pi and a camera which we use to capture the image of the crops and weeds. An automated image classification system has been designed to differentiate between weeds and crops. For the image classification method, we employ the convolutional neural network algorithm to process the image of the object. Deep learning is used to analyze the relevant features from the agricultural images. The dataset is trained for the classification of weed and crop. Therefore, by the use of technology, farmers can reduce the amount of workload and workforce they need to monitor their plantation. In addition, this technology also can improve the quality of the crops.

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

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