Digitization in agriculture: How modernizing untouched verticals spells success for food processing industry

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Improved agriculture technologies and large-scale farm production spells growth time for the food processing industry. The food processing market is projected to reach USD 235.67 billion by 2028, growing at a Compound Annual Growth Rate (CAGR) of 6.6% from 2021 to 2028. To be a part of this growth, it is essential for agribusiness verticals to upgrade to smart farming and farm digitalization from conventional methods.

The nature of digitization

The industry has responded with alacrity to the growth opportunity by going in for digitization of its operations. Surveys covering major food processing companies reveal that 55% of all data collection is digitized as of 2022; up to 91% of data collection will be digitized by 2024.

The thrust in digitalization in agriculture, however, has been largely on the upstream end of operations — factory to fork. The downstream side of the industry, tracing every process from the farm to the factory through various stakeholders in between, has not received the same focus.

The reasons for these are many. Shortage of talent and lack of localized solutions are impediments. A lack of workflow customization and unstructured data sets are other challenges on the downstream side of operations.

With rising customer consciousness on traceability in agriculture, quality, and ethicality of the products they consume, coupled with the sheer size of operations, there is a definite need of food processing automation.

Transforming the downstream value chain via digital transformation

Internal research done by Cropin has identified several clear verticals that need modernization. These are:

Warehousing and traceability in agriculture

52% of survey respondents felt that warehousing topped the need for a digital transformation. The latter can help overcome challenges with traditional warehousing. For example, digitization of food grains, commodity inventory and transaction systems will build an economic balance at storage facilities. It will allow:

  • Real-time monitoring of supply and demand supported by Artificial Intelligence (AI)
  • Better sales decision-making based on insights into market linkages and access to service providers and advisories
  • Transparent quality data and freight tracking, essential for maintaining farm-to-fork traceability

Moreover, traditional warehousing and traceability measures lacking automated assistance can lead to items getting damaged, lost, and even adulterated on the way to the market/consumer. Digital solutions like Cropin Cloud are necessary to monitor these processes and ensure traceability in agriculture.

Farming and processing

49% of respondents agree that farming and processing are future priorities for industry. Operations at the farm level and post-harvest stage can immensely benefit from Machine Learning (ML), AI and Robotic systems. For example:

  • AI sensors can detect and target weeds and decide which herbicide to apply within the region (leading to reduced herbicide usage and better cost-savings)
  • Cropin's ML-powered solutions monitor and survey crops, give yield and acreage forecasts, and deliver insights at a fraction of the traditional cost and effort

It becomes critical while considering food security needs for a growing population, climate-linked challenges to yield optimization, the urgency to adopt sustainable farming practices, and more.

Supply chain management in agriculture

45% of the respondents believe this aspect of operation needs end-to-end digitization to ensure:

  • Appropriate selection of shipping modes, carriers, and schedules based on yield quantity, health, requirement, etc.
  • Automatic processing of purchase orders to reduce labor-intensive activities
  • Higher customer engagement and satisfaction fueled by streamlined processes

With multiple vendors involved in the production of one food product, there is a need for an integrated approach like Cropin Cloud that can deliver high visibility at all levels – optimization, authenticity and transparency, and potential for unlocking growth.

Agriculture Procurement

Sourcing remains critical to the end consumer experience and hence, demands urgent digitization; complete transparency in ethical sourcing, food integrity, and carbon footprint are becoming core to retaining discerning customers and entering conscious markets. Digital data capture and analysis technologies like Cropin Intelligence and Cropin Data Hub can help:

  • Optimize seed procurement and produce-sourcing
  • Cut food loss and waste
  • Ensure farmers receive fair price for their yields

35% of those surveyed stated that agriculture procurement was an essential aspect of the downstream value chain that is due for transformation. The survey also indicates that producers are keen to have a single platform that can align the various digital interventions across verticals and democratize access to information, which otherwise tends to remain within silos.

Case point

An international brewing giant, which sources 60% of its raw material from the African region, was able to make smarter agriculture procurement strategies by deploying Cropin solutions. The product leverages AI and ML capabilities to detect, minimize, and transform risk at every stage. The brewing manufacturer was able to monitor the cultivation of over 25 million hectares and detect nine different crops simultaneously, with yield prediction accuracy of 84%. The smart intervention sorted out regular issues of mismatches between manual data collection and actual yield. These discrepancies were not just because of human error but often due to data manipulation that went undetected. For the producer, who whippets a premium on the traceability of its raw material, Cropin provides less labor-intensive and more accurate solutions.

Click here to download the case study.

Driving farm digitalization in the downstream value chain of food processing industry via Cropin Cloud

Each of the apps on Cropin Cloud enables the digital transformation of complex on-field operations such as farm monitoring, data management, location scouting and farmer engagement. Cropin Data Hub complements them. It effectively handles multiple data pipelines from structured and unstructured sources connected in the field through apps, Internet of things (IoT) devices, weather data, remote sensing data from satellites and other data sources. The solution enables transparency in downstream operations via remote crop monitoring, IoT technologies, farm digitization and mechanization data, apart from QR-code-enabled tracking solutions. It also accelerates data engineering efforts significantly for organizations focused on building independent data ecosystems. Intelligence – the final and critical layer to the agricultural technological advantage - is addressed with Cropin Intelligence’s set of 22 contextual deep learning models.

They work to address downstream transformation in areas such as yield estimation, irrigation scheduling, disease prediction, nitrogen uptake, estimating harvest dates and predicting pests and diseases. In a nutshell, they enable agile, bird's eye-view decision-making from a remote standpoint.

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Cropin Apps

Applications for

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.


ML-ready data pipelines for enhanced analytics

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, mechanization data from farming resources, drones in agriculture, remote sensing satellite information, weather data, and many more.                                                      


Access to field-tested machine learning models

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.

Abishek Keerthi Narayan

About the Author

A results-driven and strategic leader with over 18 years of experience in customer success, SaaS, management consulting and technology enablement. Proven track record of implementing customer-centric strategies to realize the key business outcomes of clients. Strong leader with a track record of building and scaling high-performing teams. Passionate about solving customer problems, creating value, and driving success.