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 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.

Frontier: Autonomy in Detection, Actuation, and Planning for Robotic Weeding Systems

2021 ◽  
Vol 64 (2) ◽  
pp. 557-563
Author(s):  
Piyush Pandey ◽  
Hemanth Narayan Dakshinamurthy ◽  
Sierra N. Young
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Research And Development ◽  
Weed Control ◽  
List Type ◽  
Automated Systems ◽  
Weed Detection ◽  
Detection And Identification ◽  
Key Technologies ◽  
Robotic Weeding

HighlightsRecent research and development efforts center around developing smaller, portable robotic weeding systems.Deep learning methods have resulted in accurate, fast, and robust weed detection and identification.Additional key technologies under development include precision actuation and multi-vehicle planning. Keywords: Artificial intelligence, Automated systems, Automated weeding, Weed control.

scholarly journals Automated Weed Detection Systems: A Review

2020 ◽  
Author(s):  
Saraswathi Shanmugam ◽  
Eduardo Assunção ◽  
Ricardo Mesquita ◽  
André Veiros ◽  
Pedro D. Gaspar
Keyword(s):  
Weed Control ◽  
Food Production ◽  
Crop Production ◽  
Control Methods ◽  
Weed Detection ◽  
Conventional Agriculture ◽  
Detection Systems ◽  
Efficient Reduction ◽  
Production Research ◽  
And Control

A weed plant can be described as a plant that is unwanted at a specific location at a given time. Farmers have fought against the weed populations for as long as land has been used for food production. In conventional agriculture this weed control contributes a considerable amount to the overall cost of the produce. Automatic weed detection is one of the viable solutions for efficient reduction or exclusion of chemicals in crop production. Research studies have been focusing and combining modern approaches and proposed techniques which automatically analyze and evaluate segmented weed images. This study discusses and compares the weed control methods and gives special attention in describing the current research in automating the weed detection and control. Keywords: Detection, Weed, Agriculture 4.0, Computational vision, Robotics

scholarly journals OpenWeedLocator (OWL): an open-source, low-cost device for fallow weed detection

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Guy Coleman ◽  
William Salter ◽  
Michael Walsh
Keyword(s):  
Open Source ◽  
Weed Control ◽  
Technology Development ◽  
Low Cost ◽  
Yield Potential ◽  
Low Density ◽  
Weed Detection ◽  
Site Specific ◽  
South Wales ◽  
Control Community

AbstractThe use of a fallow phase is an important tool for maximizing crop yield potential in moisture limited agricultural environments, with a focus on removing weeds to optimize fallow efficiency. Repeated whole field herbicide treatments to control low-density weed populations is expensive and wasteful. Site-specific herbicide applications to low-density fallow weed populations is currently facilitated by proprietary, sensor-based spray booms. The use of image analysis for fallow weed detection is an opportunity to develop a system with potential for in-crop weed recognition. Here we present OpenWeedLocator (OWL), an open-source, low-cost and image-based device for fallow weed detection that improves accessibility to this technology for the weed control community. A comprehensive GitHub repository was developed, promoting community engagement with site-specific weed control methods. Validation of OWL as a low-cost tool was achieved using four, existing colour-based algorithms over seven fallow fields in New South Wales, Australia. The four algorithms were similarly effective in detecting weeds with average precision of 79% and recall of 52%. In individual transects up to 92% precision and 74% recall indicate the performance potential of OWL in fallow fields. OWL represents an opportunity to redefine the approach to weed detection by enabling community-driven technology development in agriculture.

scholarly journals OpenWeedLocator (OWL): an open-source, low-cost 1 device for fallow weed detection.

2021 ◽  
Author(s):  
Guy Coleman ◽  
William Salter ◽  
Michael Walsh
Keyword(s):  
Open Source ◽  
Weed Control ◽  
Technology Development ◽  
Low Cost ◽  
Yield Potential ◽  
Low Density ◽  
Weed Detection ◽  
Living Plant ◽  
Detection Technology ◽  
Assembly Instructions

Abstract The use of a fallow phase is an important tool for maximizing yield potential in moisture limited environments. There is a focus on ensuring these phases are maintained weed-free as even low weed densities can be detrimental to fallow efficiency. Repeated whole field herbicide treatment to control low-density weed populations is expensive and wasteful. Site-specific application of herbicide treatments to low density fallow weed populations is currently facilitated by sensor-based devices that detect chlorophyll fluorescence from living plant tissue. The use of image-based weed detection technology for fallow weed detection is an opportunity to develop an approach that can be translated for in-crop weed recognition. Here we present the OpenWeedLocator (OWL), an open-source, low-cost image-based approach for fallow weed detection that improves accessibility to this technology for the weed control community. A comprehensive repository, containing all code and assembly instructions, has been developed that will allow for community driven improvement over time. Four different colour-based weed detection algorithms were tested with the OWL system over seven fallow field scenarios under varying light, soil and stubble conditions. Across all scenarios, the four algorithms were similarly effective in detecting fallow weeds with average precision and recall of 79% and 52%, respectively. In individual transects, precision and recall values of up to 92% and 74%, respectively, suggest the potential fallow weed detection performance of the colour-based system. OWL represents an opportunity to redefine the approach to weed detection by enabling community-driven technology development and implementation in the weed control industry.

scholarly journals Anti-Smuggling Alarm System for Trees in Forest using Raspberry pi

2021 ◽  
Vol 9 (VI) ◽  
pp. 190-195
Author(s):  
C. Varalakshmi
Keyword(s):  
Forest Fires ◽  
Preventive Measures ◽  
Base Station ◽  
Raspberry Pi ◽  
Alarm System ◽  
Water Pump ◽  
Cosmetic Products ◽  
Mems Sensor ◽  
System Data ◽  
Gps System

Nowadays, There are trees like Sandal; Sagwan etc are going to be smuggled. These trees are very costly and meagre. These are used in the medical and cosmetic products. To restrict their smuggling and to save forests around the globe some preventive measures needs to be deployed. To restrict the smuggling, we have developed a system which consists of two units i.e. Tree Unit and Main Server unit. The Tree Unit consists of MEMS sensor (to detect the inclination of tree when its being cut), Fire sensor (to detect forest fires) and GPS system. Data generated from these sensors is continuously monitored with the raspberry pi. Through the sensors, their output devices are activated via relay switch. The data of different tree units collected by a base station (Main Server Unit) by using WIFI module. For MEMS sensor a buzzer is activated and for Fire sensor a water pump is activated. Generated data is stored in Server over the Wi-Fi module. Forest officials get the alert when any event occurs so that appropriate action taken place. Camera is used to capture Image and send to Gmail. The location where smuggling happens can be tracked by GPS system.

Weed Detection Using Fractal-Based Low Cost Commodity Hardware Raspberry Pi

2016 ◽  
Vol 2 (2) ◽  
pp. 426 ◽  
Author(s):  
Mohamad Iqbal Suriansyah ◽  
Heru Sukoco ◽  
Mohamad Solahudin
Keyword(s):  
Power Consumption ◽  
Personal Computer ◽  
Precision Agriculture ◽  
Low Cost ◽  
Computation Time ◽  
Raspberry Pi ◽  
Speed Ratio ◽  
Chemical Waste ◽  
Desktop Computer ◽  
Weed Detection

Conventional weed control system is usually used by spraying herbicides uniformly throughout the land. Excessive use of herbicides on an ongoing basis can produce chemical waste that is harmful to plants and soil. The application of precision agriculture farming in the detection process in order to control weeds using Computer Vision On Farm becomes interesting, but it still has some problems due to computer size and power consumption. Raspberry Pi is one of the minicomputer with low price and low power consumption. Having computing like a desktop computer with the open source Linux operating system can be used for image processing and weed fractal dimension processing using OpenCV library and C programming. This research results the best fractal computation time when performing the image with dimension size of 128 x 128 pixels. It is about 7 milliseconds. Furthermore, the average speed ratio between personal computer and Raspberry Pi is 0.04 times faster. The use of Raspberry Pi is cost and power consumption efficient compared to personal computer.

Sensor-Controlled Hooded Sprayer for Row Crops

1998 ◽  
Vol 12 (2) ◽  
pp. 308-314 ◽  
Author(s):  
James E. Hanks ◽  
James L. Beck
Keyword(s):  
Weed Control ◽  
Crop Production ◽  
Production Systems ◽  
Bare Soil ◽  
Weed Detection ◽  
Row Crops ◽  
Spray System ◽  
Sensing Technology ◽  
Continuous Application ◽  
Commercial Size

Methods were developed and evaluated that utilize state of the art weed-sensing technology in row-crop production systems. Spectral differences in green living plants and bare soil allowed ‘real-time’ weed detection, with intermittent spraying of herbicide only where weeds were present. Sensor units were mounted in 0.7-m-wide hooded sprayers providing sensors with an unobstructed view of the area between soybean rows. Single hood and commercial-size eight-row systems were evaluated, and savings in glyphosate spray solution applied using sensors ranged from 63 to 85%, compared to conventional hooded spray systems with continuous application. Weed control by the sensor-controlled spray system was equal to the conventional system. This technology can significantly reduce herbicide usage and decrease production cost without reducing weed control.

A Blockchain-empowered Access Control Framework for Smart Devices in Green Internet of Things

2021 ◽  
Vol 21 (3) ◽  
pp. 1-20
Author(s):  
Liang Tan ◽  
Na Shi ◽  
Keping Yu ◽  
Moayad Aloqaily ◽  
Yaser Jararweh
Keyword(s):  
Internet Of Things ◽  
Access Control ◽  
Basic Unit ◽  
Smart Devices ◽  
Smart Device ◽  
Raspberry Pi ◽  
Management Platform ◽  
Control Framework ◽  
User Access ◽  
And Control

Green Internet of things (GIoT) generally refers to a new generation of Internet of things design concept. It can save energy and reduce emissions, reduce environmental pollution, waste of resources, and harm to human body and environment, in which green smart device (GSD) is a basic unit of GIoT for saving energy. With the access of a large number of heterogeneous bottom-layer GSDs in GIoT, user access and control of GSDs have become more and more complicated. Since there is no unified GSD management system, users need to operate different GIoT applications and access different GIoT cloud platforms when accessing and controlling these heterogeneous GSDs. This fragmented GSD management model not only increases the complexity of user access and control for heterogeneous GSDs, but also reduces the scalability of GSDs applications. To address this issue, this article presents a blockchain-empowered general GSD access control framework, which provides users with a unified GSD management platform. First, based on the World Wide Web Consortium (W3C) decentralized identifiers (DIDs) standard, users and GSD are issued visual identity ( VID ). Then, we extended the GSD-DIDs protocol to authenticate devices and users. Finally, based on the characteristics of decentralization and non-tampering of blockchain, a unified access control system for GSD was designed, including the registration, granting, and revoking of access rights. We implement and test on the Raspberry Pi device and the FISCO-BCOS alliance chain. The experimental results prove that the framework provides a unified and feasible way for users to achieve decentralized, lightweight, and fine-grained access control of GSDs. The solution reduces the complexity of accessing and controlling GSDs, enhances the scalability of GSD applications, as well as guarantees the credibility and immutability of permission data and identity data during access.

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