Case Study on Application of image processing in robotics..

  WHAT IS IMAGE PROCESSING?

Image processing involves changing the nature of an image in order to:                                                          1. Improve its pictorial information for human interpretation                                                                      2. Render it more suitable for autonomous machine perception.

We shall be concerned with digital image processing, which involves using a computer to analyze the nature of a digital image. Modern applications of industrial automation and robotics are increasingly relying on image processing techniques. Many definitions of this term specify mathematical operations or algorithms as tools for the processing of an image. It is a form of signal processing for which the input is an image, such as photographs or frames of videos and the output can either be an image or a set of characteristics or parameters related to the image. In  today’s  scenario,  the  robot  with  high  accuracy,  high output,  and no error  is in  demand, the precise work or repetitive work is better done with robots, for the robot the sensor or camera is common sense for the machine-like image processing to detect and identify an object and its characteristic which helps to perform a required task. 

  AGRICULTURE APPLICATIONS OF IMAGE PROCESSING WITH THE HELP OF ROBOTS: 

India is a land of agriculture and mainly known for growing variety of crops. Around half of the population in India depend on agriculture. Diseases to the crops may affect the livelihood of the farmers. In order to overcome this major problem, a robot that detects the leaf disease using image processing and Machine learning is deployed. This robot also monitors the field condition such as soil moisture, quality of crops and sprays the required amount of water and pesticides for achieving the good yield in agriculture. The robotic model with Image processing is trained with feature extraction, Segmentation using Mean Shift Algorithm and classification of disease using SVM classifier. 

 For detecting plant disease

An Agricultural robot is used that moves around the field and captures the image of the leaf and performs the disease detection operation. Here a camera is placed on a robotic car that captures the images that is transferred to the system wirelessly using RF module. In system the captured images are run on MATLAB for detection of the disease. After the detection of the disease pesticide sprayer is used for spraying of the pesticide. 

 For leaf disease detection 

an agricultural robot for leaf disease detection is used. In this paper a robot captures the image using a digital camera. After capturing the image is subjected to preprocessing and removal of noise and distraction. After preprocessing the RGB image is converted to grey and then threshold segmentation is done. Isolation of band on grey scale image is done. After the isolation RGB image is converted to HSI, based on the HSI value of the image the classification of image is done whether it is diseased or not. 

 seed-planting drones

Aerial imagery can save farmers a lot of time by giving them a bird’s eye view of crops; that way, they can quickly get a sense of vegetation’s health, insect issues, irrigation layouts and weed growth. It even allows them to precisely determine how much pesticide the crops require. Farmers can use a variety of subscription services to access these valuable flyover images of their fields.

 

 Weed detection

Precision farming provides a way to solve this challenge by involving weeding mechanisms to perform the treatment on an individual plant-level or a small weed cluster. Automated weed control, including weed detection and removal, has gained significant popularity in the community of precision farming over recent years due to its great potential to improve weeding efficiency while reducing environmental and economic costs. Robotic weed control systems have great potential to deliver much more precise results, even down to the plant scale. They enable direct chemical or cultivation tools to directly target weed plants. Many such systems have been introduced in the past, focusing primarily on single tactics: selective chemical spraying, mechanical weeding, flaming, and electrical discharging.

Companies are using automation and robotics to help farmers find more efficient ways to protect their crops from weeds. Blue River Technology has developed a robot called See & Spray which reportedly leverages computer vision to monitor and precisely spray weeds on cotton plants. Precision spraying can help prevent herbicide resistance.

In September 2017, major manufacturing company John Deere announced its acquisition of Blue River Technology. John Deere is reportedly investing $305 million to complete the transition. The company claims that the original Blue Technology firm and current staff will remain in Sunnyvale where John Deere hopes to continuing growing the firm.

 

 Crop and Soil Health Monitoring

soil quality remain significant threats to food security and have a negative impact on the economy. Domestically, the USDA has estimated that the annual cost of soil erosion is approximately $44 billion dollars. So we can get an idea of how much soil erosion takes place and it is really an importance disease to be cure. Berlin-based agricultural tech startup, has developed a deep learning application that reportedly identifies potential defects and nutrient deficiencies in soil. Analysis is conducted by software algorithms which correlate particular patterns with certain soil defects, plant pests and diseases. The image recognition app identifies possible defects through images captured by the user’s smart phone camera. This disesse detection software can rapidly achieve pattern detection with an estimated accuracy of up to 95 percent.

     

                                                                                                                                                                      

 CONCLUSION:

Agricultural robots are poised to become a highly valued application of AI in this sector. It is feasible that agricultural robots will be developed to complete an increasing diverse array of tasks in the next three to five years. Crop and soil monitoring technologies will also be important applications going forward as climate change continues to be researched and evaluated. The amount of data that can potentially be captured by technologies such as drones, and satellites on a daily basis will give agricultural business a new ability to predict changes and identify opportunities. It will be important that farmers are equipped with training that is up-to-date to ensure the technologies are used and continue to improve 

References:

• https://www.ripublication.com/ijcir18/ijcirv14n7_03.pdf

• https://homebusinessmag.com/management/technology-management/use-robots-agriculture/

• http://troindia.in/journal/ijcesr/vol4iss7/10-16.pdf