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How we improved yield for 15% using farm management system

Introduction

I am an agronomist for a large agricultural holding company that is responsible for over 250,000 acres (100,000 hectares) in the eastern part of Europe with crops consisting primarily of sugar beet, rapeseed, and maize. I left Canada to work for the company in the beginning of 2010, where I was put in charge of implementing new technologies and standardizing their business processes. I often travel between Ukraine and Russia.

Managing farms for land cultivation companies

Our company researched and then selected three systems of farm management for testing in 2012. All three were purchased and installed on various farms ranging from ten-to-twelve thousand acres (4500-5000 hectares) apiece, with enough historical documentation on each to allow us to compare each system’s results in the 2012-2013 season against historical yields.

The system we chose at the end of the process was Cropio, a system of farm management that uses satellite technology that gave us an increase of 15% in major crops’ yield. This system has a number of advantages competitively that measurably lengthens the functionality of the system.

Historical pattern of vegetation and analysis of yield

Our company looked at the productivity of each of the fields in the past and compared it with that of fields in the same category. The Cropio system gives vegetation history in a period of up to ten years. We split all fields with permanent vegetation and yields with below-average results into two groups: one with specific relief and another with all of the other fields. Specific-relief fields were correlated with the exercise described as follows.

We used the option offered by Cropio to have a very specific slope map on the specific-relief fields, and we chose those with slopes greater than five degrees to leave fallow.

For the slopes of five degrees or less, we did not show winter crops on those fields or areas, seeking to avoid the erosion caused by snow melt. We also used spring wheat with longer roots, or slope-adopted varieites, where rotation of crops was permitted. Although no particular preparations were made to prevent erosion, a seeder was used on every slope to avoid a need for ditches and to retain water, both of which were noticeably helpful.

Analysis of crop varieties.

Our companie studied various crops cultivated in one group of fields and measured the differences between the yield of each crop variety to the average land unit (group of fields). Only the two highest-performing varieties of each crop remained, with the addition of a slope-adopted variety of spring wheat as mentioned previously

Analysis of the condition of winter crop.

Cropio’s system of farm management offers frequent updates on the conditions of crops in the fields provided by low-resolution satellite images daily, and high-resolution images weekly (weather permitting). In this way we knew the overall conditions of the winter crops before we physically went out to the fields. As we received the high-resolution images, we provided them to the farmers so that they could actually see the damage to the fields. A number of those fields were reseeded with spring crops immediately, while the rest were left for observation. In the second application of fertilizer, additional nitrogen was added.

Management of failing zones and fields performing poorly.

Users of Cropio’s farm management system can receive notifications when any area of field vegetation falls below 15% compared with the rest of the same field. In all honesty, we did not succeed in using this function well. Generally, those notifications tend to come in during the middle of another process and those conditions may very well have changed by the time a user is able to attend to the notifications.

For this reason, we established a weekly review of all fields that were performing poorly. Analysts were assigned to put together a scouting report or review for each agronomist every week. Those agronomists then visited each of the non-performing fields, located detailed reasons for the poor results, and detailed the necessary steps to achieve improvement in those results. Lists of tasks were then assembled by the analysts, who then checked up on the progress of every field that needed more direct intervention. This process contributed greatly to our increase in productivity, because we knew right away which fields had abnormal vegetation and we were able to respond appropriately.

Precise Farming

Precision Agriculture (PA) is an approach to farm management that uses information technology (IT) to ensure that the crops and soil receive exactly what they need for optimum health and productivity.
Precision Farming…can help you meet large demands of your farm through the use of technology. You have the control from the tip of your fingers. You can enhance your farming efficiency and improve your everyday planning, decision making, and strategy. With precision farming solutions, you can tailor your resources to suit your needs and overall improve yields, accuracy and increase productivity. We teamed up with Case IH and New Holland to deliver you exceptional farming solutions through their equipment.

Precision farming helps to determine the right amount of fertilizer, in the right place, according to variable recommendations. It is an ideal solution to attract farming project investors who require an attractive return on revenue, while managing your risk in crop production.

Precision Farming Fundamental Tools:-

  • Global Positioning Systems (GPS)
  • Geographic Information System (GIS)
  • Remote sensing
  •  Satellite and airborne hyper-spectral imagery ◦
  • Radiometric and geophysical sensing
  • Yield monitors
  • Variable-rate technologies
  • Proximal soil sensors
  • Electromagnetic induction
  • Visible-near infrared spectroscopy

Precision farming services :-

  • Soil analysis to help manage soil fertility
  • Soil physical properties measurement to help determine crop potential and tillage practice, as well as farm layouts
  • Plant analysis to evaluate and manage the nutrient status of a crop during the growing season
  • Water analysis to help determine risks involved in Irrigation

Precision Farming gives you all five stages in farm management:-

  1. Precision Farming generates automatic, accurate farm data in real time from sensors, GPS devices, flow meters etc, and automatically uploads this information.
  2. Complementaty data can be added manually in the Precision system if required. With many other systems, farm data has to be entered by hand.
  3. Every modern farm information system stores data securely in the cloud. What’s important is that the data stored is accurate, complete and immediate. Inaccurate, incomplete paper records moved to the cloud are still inaccurate and incomplete.
  4. Precision reports are fact-based and can be audited back to their source data, whereas reports generated from imperfect manual records can result in compliance submissions to councils and other reports being inaccurate. And accurate reports are the foundation of accurate decisions.
  5. Precision enables farmers to then implement their decisions electronically, whether that be ordering a spreader, turning off a pump or starting an irrigator. These electronic orders then send completion records back to step 1 to start the cycle again.

Machine Learning and Cognitive Science

Computers are becoming smarter, as artificial intelligence and machine learning, a subset of AI, make tremendous strides in simulating human thinking. Creating computer systems that automatically improve with experience has many applications including robotic control, data mining, autonomous navigation, and bioinformatics.
A prime example of the application of machine learning is the autonomous vehicle. Sensors around the vehicle deliver thousands of data points which are analyzed and processed to move the vehicle toward its destination. Collective data from thousands of self-driving cars can be used to improve vehicle safety and prevent accidents.
Another simple example would be: An AI based system that can identify people, their gender, their various activities from the video. This is real time video processing application.
In other words, we can say that machine learns based on experience which is also known as training. The system generalizes based on the number of cases that it is exposed to and then later performs after unanticipated events.
The branch of machine learning also includes other data analysis areas which ranges from predictive analysis to data mining to pattern recognition. There are variety of algorithms that can be used depending on the type of input required.
One of the noticeable applications of machine learning is the automation of acquisition of various knowledge bases used by expert systems which aim to echo the process of decision making of human expertise in a field.
The major approach in machine learning scenarios include using case-based learning, rule indication, genetic algorithms, analytic learning and neural networks. In recent times, these models are used in a hybrid approach thus enabling the effective model development. The combination of these analytic method can ensure effective and reliable results which is required in the industry and business solutions.
Machine learning is based on the simple principle, which is

Learning=Representation+Evaluation+Optimization

  • Representation means a classifier element should be represented in a language which computer can interpret
  • Evaluation means a function which differentiates between good and bad classifiers
  • Optimization means a method which is used to search the highest scoring one classifier among these

The process of Machine learning comprises of good blend of methodologies, algorithms and mathematical techniques. Machine learning can be done by following two strategies:

1) Supervised strategy: Maps data inputs to model them against desired outputs
2) Unsupervised strategy: Maps data to model them to find new trends

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There are numerous applications, where machine learning can be used, having large volume of very different data.
Due to the enormous success in various areas like robot control, computer vision, speech recognition etc., the machine learning technologies have been adopted with the growing interest.

Various scenarios where machine learning plays a vital role are:

1) For the complex systems where it is difficult to design an algorithm
2) Complex systems that require to operate on complex data sets
3) Application requiring software to adjust to operational environment

Thus, machine learning plays an important role not only in the field of computer science but in applications which require in depth analysis like Big Data and Business Intelligence.

The following are the best use cases for machine learning in the enterprise:

1. Process Automation
2. Sales Optimization
3. Customer Service
4. Security
5. Collaboration

Cognitive Science is the study of mind and a multi-disciplinary field. It works on the concept of how it does and what it does. The applications of cognitive science are widespread:
1. Determine the relations at international level
2. Determine arrangement of products to boost the sales
3. Determine the best training for teachers
4. Methods of rehabilitation for addicts
The applications of machine learning and cognitive science vary across a wide range of fields and are not limited to above mentioned examples.

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