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Role of Machine Learning in Modern Age Agriculture

Machine learning is a trending technology nowadays and it can be used in modern agriculture industry. The uses of ML in agriculture helps to create more healthy seeds.

The principle that Arthur Samuel used earlier in machine learning experiments are used in today’s modern agriculture. Artificial machine learning in agriculture is one of the fastest growing areas. Artificial techniques are being used in the agricultural sector to increase the accuracy and to find solutions to the problems.

Agriculture plays a very pivotal role in the global economy of the country. Due to the increase in population, there is constant pressure on the agricultural system to improve the productivity of the crops and to grow more crops.

A) Machine Learning Methods

In machine learning agriculture, the methods are derived from the learning process. These methodologies need to learn through experiences to perform a particular task. The ML consists of data that are based on a set of examples. An individual example is defined as a set of attributes. These sets of characteristics are known as variables or features. A feature can be represented as binary or numeric or ordinal. The performance of the machine learning is being calculated from the performance metric.

The performance of the ML model improves as it gains experience over time. To determine the performance of ML models and the machine learning algorithms agricultures various mathematical and statistical models are used. Once the learning process is completed, then the model can then be used to make an assumption, to classify and to test data. This is achieved after gaining the experience of the training process.

agriculture with Machine Learning

Image Source:- mindbowser.com

Machine Learning Functions

It can be divided into two categories, namely supervised and unsupervised learning.

  • Supervised Learning
  • In this machine learning agriculture method, the input data is represented with examples to the corresponding outputs. The primary goal of this function is to create a rule that will map the inputs to the corresponding outputs. In some cases, the inputs might not be available that may lead to missing output. The trained model is then used in supervised learning to predict the disappeared production and then the data is being tested.

  • Unsupervised Learning
  • In this machine learning agriculture technique, there is no difference between the trained models and the test sets, while unlabeled data is being used. The goal of this method is to find the hidden patterns.

Related Article:- How AI & ML have been a Blessing for the Banking and Financial sector

B) The Machine Learning (ML) Evolution in Different Areas

Machine learning is evolving along with big data technologies and other fast computing devices. They are growing to create new opportunities to understand the various data processes related to the environmental functions of agriculture. Machine learning can be defined as the scientific method that will allow machines the ability to learn without programming the devices. Machine learning is used in various scientific areas such as Bioinformatics, Biochemistry, Medicines, Meteorology, Economic Sciences, Robotics, Food Security and Climatology.

C) Uses of Machine Learning (ML) in Agriculture

Artificial Intelligence is being used in various sectors from home to office and now in the agriculture sectors. Machine learning in agriculture used to improve the productivity and quality of the crops in the agriculture sector.

  • Retailers
  • The seed retailers use this agriculture technology to churn the data to create better crops. While the pest control companies are using them to identify the various bacteria’s, bugs and vermins.

  • AI is used to boost the yield of crops
  • The AI technologies are used to determine which corn and which conditions will produce the best yield. It will also determine which weather condition will give the highest return.

  • AI helps to identify bug hunters
  • One of the companies named Rentokil is using AI to kill all the bugs and vermin. Other companies are making use of Android app which is developed by Accenture to find bugs. The app takes the pictures of the bug and runs the app called as PestID. When a bug is found app will provide an immediate solution which helps the technician to take further actions. It will also recommend the chemical to be used to kill the bugs.

Related Article:- Machine Learning Vs. Artificial Intelligence: Understanding The Key Differences

D) Most Popular Applications of Machine Learning (ML) in Agriculture

Let us look at the various applications of machine learning in agriculture.

  • Agriculture Robot
  • Most of the companies are now programming and designing robots to handle the essential task related to agriculture. This includes harvesting crops and works faster than then human laborers. This is the best example of machine learning in agriculture.

    machine learning farming robot

    Image Source:- yourvippartner.com

  • Monitor crop and soil
  • Companies are now making use of technologies and deep learning algorithms. The data are then collected using the drones and other software to monitor the crops and also the soil. They also use the software to control the fertility of the soil.

    By making use of new technologies in agriculture, farmers can find effective ways to save their crop and also protect them from weeds. Companies are developing robots and automation tools to achieve them. Agricultural spray machines are designed, See and Spray robot that is being developed by Blue River Technology will monitor and spray accurate weeds on the plant like cotton. The precise amount of spraying can help to reduce herbicide expenditures.

    Plant breeders are looking out for a particular trait on a regular basis. They look up for the qualities that will help the crops to use more water efficiently, use the nutrients and also adapt to the climate changes or any diseases. If the plant needs to give the desired result the scientist need to find the right gene. Find the correct sequence of the gene is difficult.

E) Machine Learning (ML) Models Used in the Agriculture Industry

  • The agricultural farmers are now taking advantage of the machine learning models and their innovations. Using AI and machine learning is good for the food tech segments.
  • The Farmers Business Network that is being created for the farmers a social network will make use of the ML and the analytic tools to drive the results of data on pricing.
  • Robots are now managing the crops and also monitoring them.
  • Sensors are helping to collect the data related to crops.
  • According to research if AI and ML are being used in agriculture, then the agriculture sector will grow in the coming years.

F) Rising Opportunities of Machine Learning (ML) in Digital Agriculture

There is a rise in digital agriculture, which uses a secured approach to give maximum agricultural productivity by reducing the impact on the environment. The data that is generated in modern agriculture is based on various sensors that will help in better understanding of an environment like the crop, soil and the weather conditions and also about the agricultural machines. These data will help us to take quick and fast result-oriented decisions. To yield more, we need to apply machine learning to agriculture data.

G) Real-life ML Example

A Mexico based Startup Company Descartes Labs are combining the satellite images, ML, Cloud computing and sensors to a better understanding of industries related to agriculture and energy. The company uses new technology in agriculture to discover where crops are situated and how good and healthy the crops are.

The machine learning tools which were reserved for some institutions are now accessible to all small and capable members. A small startup is making use if AI and machine learning to bring change in the modern agriculture sector. They are trying to reshape the contemporary agriculture sector by making use of innovative technologies.

Moving Forward

If you are looking for progressive Machine Learning solutions, you have come to the precise place. We at Technostacks have the right capabilities to build clear-cut machine learning solutions that are supported by our in-depth acquaintance of industry applications, business-based services and the linked assortment of our diverse range of technologies.

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Technostacks: Meet us at CEBIT ASEAN Thailand – 18th to 20th October, 2018

CEBIT Thailand is ASEAN’s business platform, festival for innovation and digitization. The event will be held from October 18 to 20 at IMPACT Exhibition and Convention Centre in Muang Thong Thani. It is hosted by the Ministry of Digital Economy & Society and the Minister of Science and Technology (TBC). The event will pull technology professionals in Southeast Asia, from varied industries demonstrating the entire breadth of the technology sector.

CEBIT ASEAN will feature an exhibition and a conference programme. Admission to the CEBIT ASEAN THAILAND is constrained to trade professionals only. And the dress code for entry is firmly business-wear.

Business Networking Opportunities at CEBIT ASEAN Thailand

There has never been a more imperative time to do business in the swiftly evolving world of technology and this event offers boundless possibilities for businesses wishing to be at the front of innovation, technology, infrastructure and culture. It will cover Cloud Technology, IT Security, Big Data, IOT, Software and Hardware Solutions; to Emerging Technologies.

And will attract technology professionals and business leaders from assorted industries including financial services, healthcare and government, manufacturing and media, indicative of the complete breadth of the technology sector.

Technostacks will be participating at CEBIT ASEAN Thailand

Technostacks, who specializes in mobile app development, software development, web and e-commerce solutions, digital marketing and cloud based services will exhibit at CEBIT ASEAN Thailand 2018 at Hall 7-8, IMPACT Exhibition & Convention Centre, Bangkok, Thailand.

The company has its business presence in the USA, Germany, UK and India. It works on advanced technologies covering Angular JS & Node JS, Liferay Development, .Net Development, IOT (Internet of Things), AR, VR, ML, AI, and Salesforce. The company is highly committed to supporting digital industry development and progression. It innovates solutions in Augmented Reality, Embedded System, Wearables, Beacons (Context Awareness), Blueetooth low energy (BLE) Artificial Intelligence as well as in Machine Learning.

Let’s Meet Technostacks for your requirements

The event will offer an assortment of networking opportunities through personal meetings. Technostacks will be a focus for technology professionals and business leaders from varied industries including finance, medical, manufacturing, media and other technology domains. Businesses or individuals looking for any category of IT solutions are welcome to connect with us. So, let’s fix up a meeting for your IT requirements at below contact details.

Contact Details:

Website: https://technostacks.com/
Email: info@technostacks.com
+919909012616

If your organization is in the technology business and/or digitization you should reserve your booth today itself!

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YOLO Vs. SSD: Choice of a Precise Object Detection Method

Ten years ago, researchers thought that getting a computer to tell the distinction between different images like a cat and a dog would be almost unattainable. However, today, computer vision systems do it with more than 99 % of correctness. But how? Joseph Redmon worked on the YOLO (You Only Look Once) system, an open-source method of object detection that can recognize objects in images and videos swiftly. This is important as it can be implemented for applications including robotics, self-driving cars and cancer recognition approaches.

Deep learning working with real-life problems

As per the research on deep learning covering real-life problems, these were totally flushed by Darknet’s YOLO API. In one of the sessions of TEDx, Mr. Joseph Redmon presented triumphs of Darknet’s implementation on a smartphone. Multiclass object detection in a live feed with such performance is captivating as it covers most of the real-time applications. But without ignorin g old school techniques for fast and real-time application the accuracy of a single shot detection is way ahead.

The presented video is one of the best examples in which TensorFlow lite is kicking hard to its limitations. A Mobile app working on all new TensorFlow lite environments is shown efficiently deployed on a smartphone with Quad core arm64 architecture. The specialty of this work is not just detecting but also tracking the object which will reduce the CPU usage to 60 % and will satisfy desired requirements without any compromises.

In this blog post, We have described object detection and an assortment of algorithms like YOLO and SSD. We shall start with fundamentals and then compare object detection, with the perceptive and approach of each method.

You only Look Once (YOLO)

For YOLO, detection is a straightforward regression dilemma which takes an input image and learns the class possibilities with bounding box coordinates. YOLO divides every image into a grid of S x S and every grid predicts N bounding boxes and confidence. The confidence reflects the precision of the bounding box and whether the bounding box in point of fact contains an object in spite of the defined class. YOLO even forecasts the classification score for every box for each class. You can merge both the classes to work out the chance of every class being in attendance in a predicted box.

So, total SxSxN boxes are forecasted. On the other hand, most of these boxes have lower confidence scores and if we set a doorstep say 30% confidence, we can get rid of most of them.

Single Shot Detector (SSD)

SSD attains a better balance between swiftness and precision. SSD runs a convolutional network on input image only one time and computes a feature map. Now, we run a small 3×3 sized convolutional kernel on this feature map to foresee the bounding boxes and categorization probability.

SSD also uses anchor boxes at a variety of aspect ratio comparable to Faster-RCNN and learns the off-set to a certain extent than learning the box. In order to hold the scale, SSD predicts bounding boxes after multiple convolutional layers. Since every convolutional layer functions at a diverse scale, it is able to detect objects of a mixture of scales.

There are many algorithms with research on them going on. So which one should you should utilize?

YOLO Vs SSD

SSD is a healthier recommendation. However, if exactness is not too much of disquiet but you want to go super quick, YOLO will be the best way to move forward. First of all, a visual thoughtfulness of swiftness vs precision trade-off would differentiate them well.

SSD is a better option as we are able to run it on a video and the exactness trade-off is very modest. While dealing with large sizes, SSD seems to perform well, but when we look at the accurateness numbers when the object size is small, the performance dips a bit.

Moving Forward

Technostacks has successfully worked on the deep learning project. We consider the choice of a precise object detection method is vital and depends on the difficulty you are trying to resolve and the set-up.

Object detection is the spine of a lot of practical applications of computer vision such as self-directed cars, backing the security & surveillance devices and multiple industrial applications.

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