Neural Networks for Field Boundaries Detection

Using Neural Networks for Field Boundaries Detection

Today, agriculture faces many challenges, many of which are related to climate change. At the same time, the industry has a severe impact on the climate and the environment, as it is a source of pollution and greenhouse gas emissions. 

Neural Networks Boundaries Detection

Innovative technologies, including geospatial data analytics are helping to develop solutions that reduce negative impacts on wildlife and biodiversity. Defining field boundaries is one such solution. The decision is intended to protect uncultivated land from agricultural inputs, including pesticides and fertilizers.

The technological basis for automatically determining field boundaries is AI and machine learning, one of the most critical elements is artificial neural networks. They are designed to the likeness of the structure of the human brain, and the range of their application in agriculture and other industries is quite broad. 

This technology is used to predict yields, track diseases and pests, control weeds, and more. AI helps in optimizing many farm processes, decision-making and management. Special software is required to implement machine learning methods on a farm because a massive amount of data should be processed. The development of digital technologies and precision farming leads to more and more growers turning to tools based on artificial intelligence.

Deep Learning with Artificial Neural Networks

Machine learning aims to allow machines to learn from data and extract information without being explicitly programmed. Such algorithms can analyse and interpret large amounts of data. Deep learning algorithms (a field of machine learning) are complex and are applied as practical tools for image recognition. The most popular of these algorithms are convolutional neural networks.

In the geospatial data analytics market, companies use AI and machine learning to develop their products and create valuable features. EOS Data Analytics provides AI-powered satellite imagery analyticsand uses innovations to build its software products. EOSDA Crop Monitoring is a precision farming platform that helps growers to make data-based decisions, enhance farm management and decrease the negative impact of agriculture on the environment. 

EOSDA solutions also help clients solve various prediction and classification problems due to the ability of the neural network to detect patterns. Data scientists train neural networks on large sets of images to recognize and distinguish between objects on the Earth’s surface. It enables the classification of crop types, the study of land cover, and obtaining information about soil and vegetation health from satellite imagery.

Main Stages in the Building Process of a Neural Network

The development of a neural network consists of three main stages. The first step is to create an image database to train the network. It is important to note that the fundamental step in machine learning is data collection. The process will be smooth with proper preparation, but it is possible to carry out further actions effectively. It is vital to have many high-quality images to provide the network with what it will observe during its application. Like geospatial data analysis in agriculture, this complex process brings many benefits.

Then it would help if you chose the network architecture. Models with proven effectiveness already exist and are used as a basis for further application development. The third step is network training, on which its specialization and, accordingly, the tasks to be performed will depend. The first three stages are the most time-consuming. 

Field Delineation In Action

Many valuable options exist for applying field boundary detection to governments and businesses. This feature helps save a lot of money and time in calculating how cropland is distributed across a country or region since the determination is automated. Thus, governments can use this technology solution to monitor production, assess the status of crops and determine subsidies. 

The farming business today relies heavily on information. Accurate maps of field boundaries can become a reliable basis for databases related to crop production. Along with the benefits of using geospatial data in analytics, machine learning technologies greatly empower growers, agronomists and other stakeholders to reduce costs and make better management decisions to increase productivity. 

EOSDA Success Stories

As part of the EOSDA Crop Monitoring platform, the company’s innovative method, including CNN and artificial intelligence technologies, is used to delimit fields and automatically detect agricultural land. Obtaining data on the area of arable land and the boundaries of farmlands makes it possible to classify crop types in small and large territories.

The company identified the boundaries of sugarcane fields in Brazil, applying its custom algorithms. The country is the largest exporter of sugar cane. At the same time, outdated methods are often still used in the cultivation of this crop, which not only does not help increase productivity but can also harm wildlife. 

EOS Data Analytics is committed to increasing the sustainability of the industry and the application of space and other technologies for the benefit of mankind. Therefore, it provides expert assistance in the implementation of new sustainable methods. 

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