Adopting AI based solutions to transform seed value chains
In the recent years, artificial intelligence (AI) based technologies have been driving innovations in the agricultural industry by creating pathways for analyzing data in ways not used earlier. These tools helps in developing agricultural produce in a more sustainable, efficient and affordable manner. Over the last decade the absorption of such technologies across the agriculture value chain has grown considerably and witnessed transforming results. To witness the next level of paradigm shift in the seed industry wide scale adoption of such technologies across the seed industry value chain is essential. The Indian seed industry today is mature and has standardized maneuvers over the course of time, however, is inherently slowed down by low level of automation across the seed supply chain. Seeds are primary building blocks for ensuring improved productivity across crops. The predominant current rate of genetic improvement for crop productivity enhancement is not sufficient to meet the global demand of sustainable food security. Further, operational challenges related to time consuming and labor intensive operations, biotic and abiotic stresses, weather dependency, as well as quality issues hinders efficiency of the business.
Modern AI based technologies today target at shrinking breeding time and costs, increase the probability of achieving breeding targets, multiple trait detection, augmenting physical and morphological seed data, rapid results and secure platform for systematic data storage. These cost effective technologies help in better and accurate selection through seed by seed analysis and thereby ensures seed uniformity. Seed industry players today are partnering with technology providers for acquiring AI based technologies for various stages of the seed value chain (R&D, Production Operations, Supply Chain Processing & Packaging, and Sales & Market Development).
Many innovative and game changing AI and machine learning (ML) based technologies have been developed and are finding increased usage across seed value chain today that include:
- Phenotypic assessment for advancing the pipeline: Advanced machine vision technology and customized deep learning algorithms are being developed to revolutionize seed breeding and production cycles. Today such modern and breakthrough AI based tools are available that allow genotype analysis without doing molecular genetics tests and is more importantly non-destructive to seeds. These cloud based AI powered tools combines AI, algorithms and computer vision to analyze the seed’s phenotype to detect genetic characteristics or traits (Eg. fruit & grain colour, size, resistance to viruses, germination ability, male fertility, etc.) on the seed level. This can replace expensive genetic lab tests or plant level phenotyping which is time and resource consuming. This kind of tool helps researchers and breeders to gain accurate seed related insights without need for genomic information, DNA extraction, or any molecular markers.
- Accelerating breeding: Line selection for specific traits for crop improvement related to pest and disease resistance, adaptation to climate change, nutrient content, and effectiveness of water and nitrogen use is a tedious and time consuming process of breeding. AL and ML based technological advancements take decades of field data to analyze crops performance in various climates. Based on this data breeders can build probability model that would predict which genes will most likely contribute a beneficial trait to a plant.
- Assessing seed quality: Efficient & uniform seed germination & its appropriate establishment are key for achieving maximum crop productivity. It is mandatory for seed suppliers to test seed samples to ensure a certain germination rate is met. Commercial scale germination tests and large scale experiments being laborious, time consuming and prone to human errors demands for automated solutions. Limitations of current seed imaging and scoring approaches have prevented automated and scalable analysis of seed germination. A new method integrates costeffective hardware and user-friendly software for performing seed imaging and ML-based analysis for measuring establishment and germination related traits including seed germination frequency and seed vigour measurements. This next generation high throughput phenotyping platform for analysis of crop seed germination has been tested on different crops such as corn, brassica, pepper and tomato. This tool will be groundbreaking addition to the seed value chain for ensuring seed quality.
- Precision Sowing for seed production: AI based solution platforms are available for providing advisories related to weather forecasting, satellite based real time monitoring and alerts for potential pest infestation and crop diseases, agronomic, crop and pest management practices which helps in providing precise advisory for sowing and thereby help in improving crop yields in farmer fields.
- Seed Traceability: Technological intervention through usage of QR code tagging during packaging and warehousing process of the seeds ensures maintaining seed traceability and farmers are aware of the seed quality and credibility of the seed producers. Recently an AI-based solutions for Seed Potato Traceability was applied to curb the sale of counterfeit, low-quality seeds and improving the quality of potato seeds. The effort helped in capturing farm data at critical points for establishing end -to-end traceability to the life cycle of seeds. Following tests and certification, these seed potato packets have been QR coded for distribution to farmers, thereby ensuring purchase of high quality seeds by the farmers. The technology is expected to be further replicated across other crops including wheat, rice among others.
Amalgamation of such AI based technologies across the seed industry value chain can not only bring about the needed time and cost effective solutions for the industry but also improve operational efficiency across seed business operations. Adoption of such AI based processes would mean better, quicker, informed and accurate decision making regardless of rapid changes in the environment. Wide scale adoption and deployment of such technologies by several seed companies will trigger accelerated introduction of game changing solutions for the farmers and achieving enhanced productivity. On the overall agricultural front, this would help in mitigation of the environment risks since the most critical agricultural input seeds will be precisely delivered.
Published in: The Pioneer Hyderabad – March 2, 2021
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