How agritech is enhancing seed performance

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The role of technology in seed performance

Farming is a labor-intensive occupation that requires thorough planning to reap rich dividends. Not too long ago, farming was done with rudimentary tools and was dependent on the vagaries of nature. However, things have changed with the digitization in agriculture. Agriculturists have turned to smart farming to reduce time, labor, and dependence on nature, as well as to enhance yields. The need to transform agriculture with technology has become a necessity as the UN Food and Agriculture Organization (FAO) predicts that by 2050, 70% more food will have to be produced, while land availability will increase by just 5% to feed the growing population.

How big data comes to play in seed performance

The foundation of healthy crops is quality seeds that increase yield. Seed production companies face multiple challenges in developing the best seeds. Data accuracy, gradation of seed varieties, seed traceability and certification, demand forecasting and go-to-market, and farmer literacy and engagement are just some of them.

Seed production companies face the tough job of developing genetically engineered seeds to suit a variety of soils, weather conditions, and environments. Fortunately, Agritech has equipped them with the tools to develop seeds with traceability, disease resilience, vigor, and assured yields.

Big data supports the development and selection process of seeds. Deployment of IoT-enabled sensors and drones helps seeding companies collect precise crop- and site-specific information from various locations, such as data on weather, fertilization cycles, soil moisture, and seed quality. This information is transmitted to cloud computing hubs for analysis.

How predictive intelligence for farming supports the selection of the best seed variety

Advanced data analytics engines sort the collated data across locations and years to help seed production companies pick winners that will perform consistently across soils, climatic zones, and environments. Descriptive, predictive, and prescriptive analytics combine to analyze which seeds performed the best in the past, which will be winners for the current year, and which seeds will provide consistent yields over the next 2-3 years.

The predictive intelligence in farming thus developed helps seeding companies advise farmers where to plant specific hybrids to get the best yields. Predictive intelligence also helps with accurate yield estimation.

How prescriptive analytics drives smart farming practices

Seed production companies also collaborate with farmers in the data aggregation process. Digital monitors on fertilizer applicators, harvesters, and planters generate real-time in-field data overlaid on maps displaying tillage practices, prior yield history, plant population, and soil types. Prescriptive analytics consolidate these data to provide farmers with information on the optimum use of fertilizers, insecticides, and pesticides required.

Satellite-referenced data and real-time in-season satellite images play a significant role in providing precision farming inputs for seed development. Farmers receive reliable weather alerts and actionable insights to optimize water usage.

How AI and ML ease the job of seed selection

As per a Deloitte report, 'Transforming agriculture through digital technologies, the global market for AI in agriculture was valued at $240 million in 2017 and is expected to reach $790 million by 2023'.

AI-based seed phenotyping can classify critical parameters of a seed in a matter of minutes. ML algorithms and models can analyze seeds for phenotypic characteristics and predict seed health and yield outcomes. Their shape, size, texture, and color can be extracted from images and their unique signatures identified through classifications. Compared to manual inspection, AI/ML automation can provide an efficient method for seed sorting with real-time, readily available reports.

By plotting growth parameters into AI/ML-based systems, seed production companies have access to accurate tracking and prediction mechanisms, thereby reducing the time to market for new hybrid varieties. Seed varieties with digital footprints allow the selection of seeds with high-quality traits and provide seed-to-shelf traceability.

Cropin’s role in enhancing seed performance

Cropin, a pioneer in the Agritech segment, is transforming end-to-end operations of seed enterprises with cutting-edge technology. Building the first global Intelligent Agriculture Cloud, Cropin enables various stakeholders in the agri-ecosystem to leverage digitization and AI at scale to make decisions that increase efficiency, enhance productivity, and strengthen sustainability.

Cropin has worked with over 250 customers and has digitized 16 million acres of farmland, improving the livelihoods of more than 7 million farmers. Spearheading a global ‘Ag-intelligence’ movement with a knowledge graph of over 500 crops and 10,000 crop varieties in 92 countries, Cropin has computed 0.2 billion acres of farmland covering 24 major commodities.

Some of the Agtech services that the company provides in the seed sector include seed production management, end-to-end food traceability, farmer engagement, seed health analysis, lead indicators on seed efficacy, seed performance optimization, seed yield optimization, and quality compliance reports.

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Cropin Apps is an integrated portfolio of highly customizable apps and solutions that capture and digitise agri-data from the farm to the warehouse to the fork. These applications are designed to scale digital transformation across agriculture and allied industries including forestry, commodity, banking and insurance.

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Cropin Data Hub is designed to deliver the power of unified data by enabling interfacing with all agri-data sources from on-the-field farm management apps, IOT devices and drones, mechanization data from farming resources, remote sensing satellite information, weather data and many more.                                                      

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Cropin Intelligence enables access to over 22 of Cropin’s contextual deep-learning AI models to help agri-businesses with insights and predictive intelligence. Built using the world's largest crop knowledge graph, these models have been field-tested and deployed worldwide while being fine-tuned to work with a range of specific crop varieties, conditions, and locations.