Published by: Albert Gibosse

Artificial Intelligence in Agriculture[AI Transforming Agriculture]Artificial Intelligence in Agriculture

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As small farmers around the world continue to use traditional farming practices due to lack of access to scientific understanding of crop lifecycle, pests, quality metrics and the latest micro-fertilizers, AI solutions offers advanced image technology as well as deep learning algorithms to provide valuable insights on crops’ health during the growing season and their final harvested quality by analyzing images.

AI solutions can address Crop and Soil Monitoring, Predictive Analaytics, and Supply Chain Efficiencies

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  1. Crop and Soil Monitoring

Companies are leveraging sensors and various IoT-based technologies to monitor crop and soil health.

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Using AI to Maximize per-Acre Value and to create solutions that help in remote sensing and weather advisory, scheduling and monitoring farm activities for complete traceability, educating farmers on adoption of right package of practices

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Artificial Intelligence in Agriculture

and inputs, monitoring crop health and harvest estimation, and alerts on pest, diseases etc.

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AI can also help farmers analyze and interpret data to derive real-time actionable insights on standing crop and projects spanning geographies. Agri-business intelligence solutions like SmartRisk leverage agri-alternate data and provide risk mitigation and forecasting for effective credit risk assessment and loan recovery assistance.

Additionally, proprietary machine learning algorithm built on satellite and weather data can also be used to give insights at plot and region level.

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Specific services that can be provided

Agricultural Product Grading: An accurate and reliable method for grading fresh products (fruits, grains, vegetables, cotton etc.) characterized by color, size and shape through automated quality analysis of images of food products. This solution, with no manual interventiont, reads images that farmers send from their phones, and determines the product quality in real time.

Artificial Intelligence in Agriculture

Alerts on Crop Infestation: Farmers take pictures of their crops and use their solution to understand the pests, diseases, and foreign plants (weeds) growing in their farms and receive automatic recommendations from the solution on how that disease can be cured and prevented from increasing further.

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  1. Predictive Agricultural Analytics

Various AI and machine learning tools, currently, are being utilized to predict the optimal time to sow seeds, get alerts on risks from pest attacks, and beyond.

Microsoft India and its AI-based Sowing App

Determining the right time to sow when drought and excess rainfall can be equally serious challenges. Microsoft in collaboration with ICRISAT (International Crops Research Institute for the Semi-Arid Tropics), developed an AI Sowing App that uses machine learning and business intelligence from the Microsoft Cortana Intelligence Suite.

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Artificial Intelligence in Agriculture

The app sends sowing advisories to participating farmers on the optimal date to sow. Farmers don’t need to install any sensors in their fields or incur any capital expenditure. All they need is a feature phone capable of receiving text messages.

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Crop-sowing period calculations include historic climate data (spanning years) for a specific area to be analyzed using AI toward determining the optimal sowing period and the Moisture Adequacy Index (MAI). MAI is the standardized measure

used for assessing the degree of adequacy of rainfall and soil moisture to meet the potential water requirement of crops.

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Artificial Intelligence in Agriculture

AI-powered Pest Risk Prediction Apps can leverage machine learning capabilities can be utilized to predict the risk of pest attack.

  1. Supply Chain Efficiencies

Companies are using real-time data analytics on data-streams coming from multiple sources to build an efficient and smart supply chain.

AI-optimized automated pipelines can be utilized to dramatically increase the efficiency of agri supply chains. Data-driven online agri-marketplace can afford best prices for both the producers and buyers at their fingertips, thereby, ensuring a higher profit margin than the traditional companies.

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Artificial Intelligence in Agriculture

Gobasco, an Indian company, uses AI and related technologies in the various stages of the agri supply chain to ensure it is efficient and fast. Some of them are listed below:

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Transition Discovery: Real-time data analysis on multiple data-streams along with crowd-sourced data from producer/buyer marketplaces and transporters feeds their automatic transaction discovery algorithm to obtain high-margin transactions.

Quality Maintenance: Computer vision and AI-based automatic grading and sorting is done for vegetables and fruits for creating an international agri-commodity standard for reliable trading across country boundaries.

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Credit Risk Management: Crowd-sourced data, algorithms and analytics overcome the credit default problem, the most challenging problem of current supply-chain, to ensure a very low risk operation.

Artificial Intelligence in Agriculture

Agri-Mapping: Deep-learning based satellite image analysis and crowd-sourced information fusion obtains a real-time agri map of commodities at a resolution of 1 sq-km.

AI is helpful to the agricultural sector which is heavily dependent on climatic conditions that are often unpredictable.

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