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.
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Machine Learning Functions
It can be divided into two categories, namely supervised and unsupervised learning.
- Supervised Learning
- Unsupervised 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.
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.
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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.
- AI is used to boost the yield of crops
- AI helps to identify bug hunters
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.
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.
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.
D) Most Popular Applications of Machine Learning (ML) in Agriculture
Let us look at the various applications of machine learning in agriculture.
- Agriculture Robot
- Monitor crop and soil
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.
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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.
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.