4 Expertise Must-haves for Agritech Software Development

4 Expertise Must-haves for Agritech Software Development
N-iX
1970-01-01T05:14:28+00:00

Nowadays farmers and technology companies are starting to adopt innovations and invest in agritech software development to meet the growing demand for more automated processes and greater yields. They look for IT providers that offer a variety of agritech software development services to create...

4 Expertise Must-haves for Agritech Software Development
By Yustyna Velykholova February 02, 2018

Nowadays farmers and technology companies are starting to adopt innovations and invest in agritech software development to meet the growing demand for more automated processes and greater yields. They look for IT providers that offer a variety of agritech software development services to create efficient solutions for farm management, process automation, drone field analysis, and precision farming.

However, before choosing an IT partner, it’s essential to consider what kind of expertise is necessary for building an agritech solution and what benefits you can derive from new technologies in this area. We’ve decided to answer these questions in our article.

IoT-enabled solutions for Agritech

Expertise in IoT and embedded software development is essential for creating solutions for crop scouting, NDVI analysis, and livestock monitoring. With the help of these technologies, farmers can track data on weather, soil, air quality, and crop maturity, and take appropriate actions. Among the most common IoT use cases in the agritech sector are farm management solutions for livestock monitoring and precision farming.

According to Markets and Markets, smart agriculture market (including areas such as precision farming, livestock monitoring, etc.) is expected to grow from $5.18 billion in 2016 to $11.23 billion by 2022 at a CAGR of 13.27%.

  • Livestock monitoring

With the help of wireless IoT applications, farmers can collect data regarding the health and location of their cattle. This information helps to identify sick animals, thus preventing the spread of disease. Moreover, it reduces labor costs since farmers can remotely identify where their cattle are located.

For instance, a Bovcontrol solution improves performance on meat, milk and genetics production by efficiently using data collection technologies, cloud computing, and information delivery.

Another agritech company, CattleWatch, uses sensors and communication platforms to remotely monitor the health status and location of livestock. The system provides valuable, real-time insights regarding animal behavior, herd location, walking, grazing and resting time, water consumption, as well as sends alerts if predatory animals are detected.

  • Plant & soil monitoring in precision farming

Monitoring plant and soil conditions can lead to greater ROIs for farmers. IoT solutions for precision farming are mostly used for reporting weather conditions, sensing for soil nutrients and controlling water usage for optimal plant growth. Moreover, embedded software for smart sensors can help determine custom fertilizer profiles based on soil chemistry and define the optimal time to plant and harvest.

For instance, Cropx utilizes data and sensor devices to help farmers better understand water usage across their fields. With the help of algorithms and pattern-recognition technology, Cropx also informs farmers about the amount of fertilizer and pesticide needed by each patch at specific times. In addition, this solution analyzes the farmland and determines various elevations of the field.

  • Drones for field monitoring

Drones are the key technology in agricultural IoT solution development. With the help of spectrums and high-precision imaging, they provide farmers with ultimate control over their crops and help increase yields by as much as 25%.

Embedded with various sensors, drones enable farmers to become more resource-efficient and achieve greater automation while ensuring the crops get the precise amount of moist, care, and nutrition. Digitally connected drones can receive commands from satellites, ground-based sensors, farmhouse data centers, tablets or smartphones, and then send the necessary information in a timely manner.

For more information on drones in agritech, read our article.

IoT in agritech software development

Big data for Smart farming

Big data engineering is becoming an integral part of agritech software development. Big data analytics software can open new prospects for modern farmers and agriculture business owners by helping them effectively collect, store, and analyze data generated by various devices.

Big data applications in agritech range from relatively simple feedback mechanisms (e.g. a thermostat regulating temperature) to deep learning algorithms for smart sensor machines (e.g. to implement the right crop protection strategy).

Big data and analytics have 4 major applications in agriculture:

  • Food Tracking

Field and crop sensors can now provide data on soil conditions, and detailed information on wind, fertilizer requirements, water availability, and pest infestations. With effective data aggregation and analysis, they help prevent spoilage (by moving products faster and more efficiently) and food borne illnesses. This also allows using better models to manage crop failure risk while boosting feed efficiency in livestock production.

  • Precision Farming

The basis of precision farming rests in observing, measuring and responding to inter and intra-field variability in crops. Big data takes advantage of information derived from precision farming (aggregated from many farms), which results in valuable insights and enhanced decision making.

  • Supply Chains

Big data unlocks new opportunities for increasing the efficiency of modern agricultural supply chains, including production, processing and distribution operations. For example, security tracking, real-time crop monitoring, pinpoint irrigation and application of nutrients demonstrate great potential towards establishing a well-regulated food supply chain with big data.

  • Predictive modelling

Big data enables real-time assisting reconfiguration, which can be used for predictive modelling, especially in emergency cases of suddenly changed operational conditions or other circumstances such as weather or disease alert.

AI and machine learning for Agritech software development

According to Markets and Markets, the AI in agriculture market is expected to grow by 22.5% to reach $2.6bn by 2025 from $518.7m in 2017.

The rapid growth of AI in agritech can be attributed to a number of factors such as:

  • growing demand for agricultural production;
  • increasing adoption of information management systems;
  • rise of technologies for improving crop productivity.

In combination with big data, AI  and machine learning allow implementing innovative solutions for various purposes including:

  • Agricultural Robots

AI-based autonomous robots are used for handling essential agricultural tasks such as harvesting crops and spraying herbicides faster and with more precision than real farmers. For instance, Blue River Technology has developed a robot called See & Spray. It leverages AI and computer vision to monitor and precisely spray weeds only where needed and with exactly what’s needed, which helps to prevent herbicide resistance.

  • Crop and Soil Monitoring

Companies are leveraging computer vision and deep-learning algorithms to process data captured by drones and IoT devices to monitor crop and soil health. For instance, Berlin-based agricultural startup PEAT has developed a deep learning soil analysis application called Plantix. This image recognition app reportedly identifies potential defects and nutrient deficiencies in soil based on algorithms.

  • Predictive Analytics

Machine learning models are developed to track and predict various environmental impacts such as weather changes on crop yield. For instance, a Colorado-based company aWhere uses machine learning algorithms in connection with satellites to predict weather, analyze crop sustainability and evaluate farms for diseases and pests.  

AI in agritech software development

Proactive decision-making with BI

Once an agricultural company has incorporated BI expertise into its operations, it automatically gains competitive advantages. For instance, it can gain accurate insights, forecast results and pre-plan many agricultural activities.

There is a variety of uses cases of BI solutions in agriculture such as:

  • Reporting and Planning

Accurate timing and efficiency are of crucial importance when it comes to harvesting crops and growing animals or plants. With the help of data extraction, aggregation, and visualization tools offered by BI, agricultural businesses can significantly benefit from comprehensive reports on crop yields. The data presented by BI consultants can help identify exactly when to plant and harvest crops, which nutrients they need, and which processes could be automated. BI reporting can shed light on the possible risks so that decision makers could mitigate them.

For example, Agility Crops, a powerful business intelligence platform for arable farming, provides actual insights based on data from farms across the UK. It allows farmers to perform analysis by crop, soil type, region, cropping stage, and other indicators to identify trends, threats and opportunities as they occur.

  • Enhanced Decision Making

BI provides accurate data, analysis and reporting, which allows decision makers to identify what is lacking from certain divisions and which areas are sustainable without extra income and attention. Thus the agricultural business owners can make informed decisions regarding farm management operations.

For instance, Farmeron utilizes BI to help farmers manage their farming data online and analyze farm performance. This online web portal allows tracking animal physical characteristics including milk production, medical treatments, and even particular feeding group schedules in a form of reports and statistics. Thus farmers can monitor production performance and adjust their production plans accordingly.

Afterword

On the whole, technology is transforming every industry today and agriculture is no exception. Big data and analytics, AI, machine learning and IoT are the driving forces behind agritech software development. They lie at the core of modern farming strategies, enabling faster and greater automation and proactive decision making. Smart solutions, powered by these technologies, help farmers remotely monitor the fields, receive notifications on crop health and weather conditions to take appropriate actions. This not only saves labour costs and increases ROI but enables agricultural businesses to stay ahead of the competition in the long run.

If you want to find out more about efficient implementation of innovative technologies in agriculture, feel free to contact our experts.

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By Yustyna Velykholova February 02, 2018
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