Kshitij Thakur, Co-founder, Nanekarwadi, Rakshewadi Khed, Chakan, Pune, Maharashtra, describes how, from harvest to market, AI is helping streamline supply chains and create greater value for farmers, businesses, and consumers.
Harvests at a crossroads
India's fresh produce industry stands at a critical crossroads. While the country has made significant progress in increasing agricultural production, a substantial challenge remains hidden beyond the farm gate. Fruits and vegetables continue to face enormous losses after harvest, reducing farmer incomes, affecting food security, and limiting the efficiency of agricultural supply chains.
For decades, farmers, traders, aggregators, and consumers have experienced the consequences of an inefficient post-harvest ecosystem. However, the emergence of artificial intelligence, automation, robotics, sensor-based monitoring, and digital technologies is creating new opportunities to address these long-standing problems.
Today, India is witnessing the development of technologies specifically designed for Indian crops, Indian conditions, and Indian supply chains. These innovations have the potential to transform how fresh produce moves from farms to consumers while ensuring better quality, reduced losses, and improved value realisation for all stakeholders.
When value withers
One of the most pressing challenges in India's fresh produce sector is post-harvest loss. Estimates suggest that nearly 30 to 40 per cent of fruits and vegetables are lost before reaching consumers. These losses occur at multiple stages, including handling, transportation, storage, grading, sorting, and marketing.
Such losses represent more than just wasted produce. Every kilogramme of wasted produce also represents wasted water, labour, fertilisers, energy, and financial investment. Farmers devote months of effort to cultivating crops, yet a significant portion of their harvest never generates income. The economic impact is substantial. Even if agricultural productivity increases, the benefits are often offset by losses occurring throughout the supply chain. As India's population continues to grow, reducing post-harvest waste becomes essential not only for improving farmer incomes but also for ensuring long-term food security.
The challenge becomes even more significant in the case of perishable commodities such as tomatoes, onions, potatoes, citrus fruits, and other fresh produce. These products have limited shelf lives and require efficient handling to maintain quality and value.
Finding the right market
Market access remains another major concern for farmers and Farmer Producer Organisations (FPOs). Many producers struggle to identify the most suitable market for their produce. Information gaps often result in mismatches between supply and demand.
In many cases, farmers lack visibility regarding market requirements. They may not know which varieties, sizes, maturity levels, or quality grades are preferred by specific buyers. This absence of transparent communication often results in oversupply in some markets and shortages in others.
For example, fully ripened tomatoes may fetch attractive prices in nearby markets but are unsuitable for long-distance transportation. Semi-ripe tomatoes, on the other hand, may be more appropriate for distant markets because they can withstand transit without spoilage.
Without proper market intelligence, farmers may send produce to unsuitable destinations, leading to losses, reduced prices, and increased wastage. Better coordination between producers and markets is therefore essential for improving efficiency across the supply chain.
The measure of quality
Grading and sorting are fundamental activities that determine the marketability of fresh produce. Every market has specific requirements, and meeting these requirements directly influences pricing and demand.
The fresh produce industry spends thousands of crores annually on grading and sorting activities. These processes help categorise produce according to quality, size, maturity, appearance, and other market-driven factors.
When produce is not graded properly, buyers are forced to undertake additional sorting themselves. This additional effort reduces the price they are willing to pay. Even small variations in quality can significantly affect market value.
For instance, mixing smaller onions with larger onions can reduce the value of an entire consignment. Similarly, combining premium-grade produce with lower-grade produce often leads to lower overall pricing.
Effective grading and sorting improve transparency, facilitate price discovery, and ensure that produce reaches the most appropriate market. They also help reduce disputes between buyers and sellers by creating clear quality classifications.
Foundations for freshness
Infrastructure deficiencies continue to hinder the development of efficient post-harvest systems across India. Storage facilities remain insufficient in many agricultural regions. When large quantities of produce arrive simultaneously, farmers often have no choice but to sell immediately because they lack access to storage. This frequently results in distress sales at prices below production costs.
Collection centres equipped with grading, sorting, packing, and storage facilities could significantly improve this situation. Instead of travelling long distances to wholesale markets, farmers could process and sell produce closer to the farmgate.
Cold storage and controlled atmosphere facilities can further extend shelf life, allowing farmers to wait for favourable market conditions rather than selling under pressure. Such infrastructure would provide greater flexibility and improve value realisation.
The development of decentralised infrastructure is particularly important. Rural collection centres can bring modern post-harvest capabilities closer to producers, reducing transportation costs and increasing efficiency.
Machines in the fields
Automation is rapidly transforming post-harvest operations. Technologies capable of cleaning, washing, drying, grading, sorting, and packing produce are becoming increasingly accessible.
Traditionally, these activities required significant manual labour. Processing large quantities of produce often took several days and involved substantial operational costs. Labour shortages frequently created bottlenecks, particularly during peak harvest seasons.
Modern automated systems are changing this reality. Using artificial intelligence and machine vision technologies, these systems can analyse produce characteristics and classify products according to predefined quality parameters.
Damaged, rotten, or defective produce can be identified and separated automatically. Premium-quality produce can then be directed towards high-value markets, while lower-grade produce can be sold through alternative channels. The result is greater efficiency, improved consistency, and reduced dependence on manual labour. Faster processing also helps preserve freshness and maintain product quality.
Intelligence at work
Artificial intelligence is playing an increasingly important role in agricultural supply chains. Its ability to process large volumes of data and identify patterns makes it particularly valuable in post-harvest management. AI-powered systems can assess quality, predict shelf life, identify defects, and support decision-making throughout the supply chain. They help transform subjective evaluations into objective assessments.
One of the most significant advantages of AI is its ability to establish trust. Traditionally, quality assessments often depended on personal judgement, leading to disagreements between buyers and sellers. AI-based systems provide consistent and measurable evaluations, reducing uncertainty and improving transparency. As adoption increases, AI is expected to become a key component of quality assurance systems across the fresh produce industry.
Watching over quality
Sensor technologies and computer vision are creating new possibilities for monitoring produce during storage and transportation. Spoilage often begins gradually and may remain unnoticed until substantial damage has occurred. Monitoring large storage facilities manually is both difficult and inefficient.
Sensors can continuously track environmental conditions such as temperature, humidity, and air quality. They can also help identify early signs of spoilage, enabling operators to take corrective action before losses escalate. In commodities such as onions and potatoes, early detection of rot can significantly reduce losses. Similar applications are emerging for fruits and other perishable products.
Building trust through technology
One reason why multiple intermediaries exist within agricultural supply chains is the absence of reliable quality assessment tools. Large buyers often rely on trusted intermediaries because they need assurance regarding product quality. Without objective measurement systems, purchasing decisions frequently depend on personal relationships and reputation.
AI-powered quality assessment technologies have the potential to change this dynamic. Mobile-based and sensor-based solutions can provide standardised quality reports, allowing buyers to make informed purchasing decisions directly.
This could strengthen direct linkages between farmers and buyers while reducing unnecessary layers within the supply chain. Greater transparency would benefit both producers and purchasers. Digital marketplaces are already beginning to incorporate quality assessment tools, enabling farmers to showcase their produce more effectively and connect with potential buyers.
Reading market signals
Predictive analytics represents another promising area for AI adoption.
Agricultural markets are heavily influenced by fluctuations in supply and demand. Unexpected surpluses can cause prices to collapse, while shortages can lead to sharp increases. AI systems can analyse historical data, market trends, weather patterns, and arrival information to generate forecasts that support better planning.
For farmers, access to reliable forecasts can improve marketing decisions. They can choose when to sell, where to sell, and how much produce to store based on anticipated market conditions. Agribusinesses can also benefit by planning procurement activities more effectively and reducing supply chain disruptions.
As predictive tools become more accessible, they may help create a more balanced and responsive agricultural marketplace.
Bridging fields and innovation
Despite the growing interest in artificial intelligence and automation, widespread adoption across the post-harvest sector is still at an early stage. The challenge is not necessarily a lack of awareness. Farmers, FPOs, aggregators, and agribusinesses increasingly recognise the value of technology. The greater challenge lies in making these solutions accessible, affordable, and relevant to local conditions.
Many technologies developed for international markets were designed for large-scale operations handling hundreds of tonnes of produce daily. Indian agricultural supply chains are often far more fragmented, with smaller volumes, diverse crop varieties, and different operating conditions. As a result, there is a growing need for solutions that are specifically designed for Indian users.
Encouragingly, a number of Indian startups are addressing this gap by developing grading, sorting, packing, and quality assessment systems that work effectively in local environments. These technologies are helping to democratise access to advanced post-harvest capabilities that were previously available only to large enterprises.
Another important aspect of adoption is capacity building. Farmers and rural entrepreneurs must be trained to understand and utilise these technologies effectively. The success of any innovation depends not only on the technology itself but also on the confidence and skills of the people using it.
The development of rural collection centres equipped with modern infrastructure can play a crucial role in this transition. Such facilities can serve as local hubs where farmers gain access to grading, sorting, storage, quality assessment, and market linkage services. This decentralised approach reduces dependence on distant markets and creates opportunities for greater value addition closer to the farm.
As technology becomes more affordable and infrastructure continues to improve, the gap between production and market realisation can gradually be reduced. This will strengthen supply chains, improve farmer incomes, and create a more resilient agricultural ecosystem.
Seeds of future growth
The future of post-harvest management in India depends on the successful integration of technology, infrastructure, and market connectivity.
Artificial intelligence alone cannot solve every challenge. Storage facilities, collection centres, transportation networks, and market linkages remain equally important. However, AI has the potential to make these systems significantly more efficient and effective.
Encouragingly, many Indian startups are developing solutions tailored specifically to domestic requirements. Unlike imported technologies designed for large-scale foreign operations, these innovations address the realities of Indian agriculture. Affordable grading systems, mobile quality assessment tools, automated packing technologies, and intelligent monitoring systems are making advanced capabilities available to a wider range of users.
The benefits extend across the entire supply chain. Farmers can achieve higher incomes through better quality management and market access. Agribusinesses can improve consistency and reduce operational costs.
As AI, automation, robotics, and digital technologies continue to evolve, India has a unique opportunity to transform its fresh produce supply chains. By combining innovation with practical infrastructure development, the country can create a more resilient, transparent, and efficient farm-to-fork ecosystem that benefits everyone involved.
Contact details
Kshitij Thakur
Co-founder, Nanekarwadi, Rakshewadi Khed, Chakan, Pune, Maharashtra
M: 8369599377
E: kshitij@agrograde.com
Harvests at a crossroads
India's fresh produce industry stands at a critical crossroads. While the country has made significant progress in increasing agricultural production, a substantial challenge remains hidden beyond the farm gate. Fruits and vegetables continue to face enormous losses after harvest, reducing farmer incomes, affecting food security, and limiting the efficiency of agricultural supply chains.
For decades, farmers, traders, aggregators, and consumers have experienced the consequences of an inefficient post-harvest ecosystem. However, the emergence of artificial intelligence, automation, robotics, sensor-based monitoring, and digital technologies is creating new opportunities to address these long-standing problems.
Today, India is witnessing the development of technologies specifically designed for Indian crops, Indian conditions, and Indian supply chains. These innovations have the potential to transform how fresh produce moves from farms to consumers while ensuring better quality, reduced losses, and improved value realisation for all stakeholders.
When value withers
One of the most pressing challenges in India's fresh produce sector is post-harvest loss. Estimates suggest that nearly 30 to 40 per cent of fruits and vegetables are lost before reaching consumers. These losses occur at multiple stages, including handling, transportation, storage, grading, sorting, and marketing.
Such losses represent more than just wasted produce. Every kilogramme of wasted produce also represents wasted water, labour, fertilisers, energy, and financial investment. Farmers devote months of effort to cultivating crops, yet a significant portion of their harvest never generates income. The economic impact is substantial. Even if agricultural productivity increases, the benefits are often offset by losses occurring throughout the supply chain. As India's population continues to grow, reducing post-harvest waste becomes essential not only for improving farmer incomes but also for ensuring long-term food security.
The challenge becomes even more significant in the case of perishable commodities such as tomatoes, onions, potatoes, citrus fruits, and other fresh produce. These products have limited shelf lives and require efficient handling to maintain quality and value.
Finding the right market
Market access remains another major concern for farmers and Farmer Producer Organisations (FPOs). Many producers struggle to identify the most suitable market for their produce. Information gaps often result in mismatches between supply and demand.
In many cases, farmers lack visibility regarding market requirements. They may not know which varieties, sizes, maturity levels, or quality grades are preferred by specific buyers. This absence of transparent communication often results in oversupply in some markets and shortages in others.
For example, fully ripened tomatoes may fetch attractive prices in nearby markets but are unsuitable for long-distance transportation. Semi-ripe tomatoes, on the other hand, may be more appropriate for distant markets because they can withstand transit without spoilage.
Without proper market intelligence, farmers may send produce to unsuitable destinations, leading to losses, reduced prices, and increased wastage. Better coordination between producers and markets is therefore essential for improving efficiency across the supply chain.
The measure of quality
Grading and sorting are fundamental activities that determine the marketability of fresh produce. Every market has specific requirements, and meeting these requirements directly influences pricing and demand.
The fresh produce industry spends thousands of crores annually on grading and sorting activities. These processes help categorise produce according to quality, size, maturity, appearance, and other market-driven factors.
When produce is not graded properly, buyers are forced to undertake additional sorting themselves. This additional effort reduces the price they are willing to pay. Even small variations in quality can significantly affect market value.
For instance, mixing smaller onions with larger onions can reduce the value of an entire consignment. Similarly, combining premium-grade produce with lower-grade produce often leads to lower overall pricing.
Effective grading and sorting improve transparency, facilitate price discovery, and ensure that produce reaches the most appropriate market. They also help reduce disputes between buyers and sellers by creating clear quality classifications.
Foundations for freshness
Infrastructure deficiencies continue to hinder the development of efficient post-harvest systems across India. Storage facilities remain insufficient in many agricultural regions. When large quantities of produce arrive simultaneously, farmers often have no choice but to sell immediately because they lack access to storage. This frequently results in distress sales at prices below production costs.
Collection centres equipped with grading, sorting, packing, and storage facilities could significantly improve this situation. Instead of travelling long distances to wholesale markets, farmers could process and sell produce closer to the farmgate.
Cold storage and controlled atmosphere facilities can further extend shelf life, allowing farmers to wait for favourable market conditions rather than selling under pressure. Such infrastructure would provide greater flexibility and improve value realisation.
The development of decentralised infrastructure is particularly important. Rural collection centres can bring modern post-harvest capabilities closer to producers, reducing transportation costs and increasing efficiency.
Machines in the fields
Automation is rapidly transforming post-harvest operations. Technologies capable of cleaning, washing, drying, grading, sorting, and packing produce are becoming increasingly accessible.
Traditionally, these activities required significant manual labour. Processing large quantities of produce often took several days and involved substantial operational costs. Labour shortages frequently created bottlenecks, particularly during peak harvest seasons.
Modern automated systems are changing this reality. Using artificial intelligence and machine vision technologies, these systems can analyse produce characteristics and classify products according to predefined quality parameters.
Damaged, rotten, or defective produce can be identified and separated automatically. Premium-quality produce can then be directed towards high-value markets, while lower-grade produce can be sold through alternative channels. The result is greater efficiency, improved consistency, and reduced dependence on manual labour. Faster processing also helps preserve freshness and maintain product quality.
Intelligence at work
Artificial intelligence is playing an increasingly important role in agricultural supply chains. Its ability to process large volumes of data and identify patterns makes it particularly valuable in post-harvest management. AI-powered systems can assess quality, predict shelf life, identify defects, and support decision-making throughout the supply chain. They help transform subjective evaluations into objective assessments.
One of the most significant advantages of AI is its ability to establish trust. Traditionally, quality assessments often depended on personal judgement, leading to disagreements between buyers and sellers. AI-based systems provide consistent and measurable evaluations, reducing uncertainty and improving transparency. As adoption increases, AI is expected to become a key component of quality assurance systems across the fresh produce industry.
Watching over quality
Sensor technologies and computer vision are creating new possibilities for monitoring produce during storage and transportation. Spoilage often begins gradually and may remain unnoticed until substantial damage has occurred. Monitoring large storage facilities manually is both difficult and inefficient.
Sensors can continuously track environmental conditions such as temperature, humidity, and air quality. They can also help identify early signs of spoilage, enabling operators to take corrective action before losses escalate. In commodities such as onions and potatoes, early detection of rot can significantly reduce losses. Similar applications are emerging for fruits and other perishable products.
Building trust through technology
One reason why multiple intermediaries exist within agricultural supply chains is the absence of reliable quality assessment tools. Large buyers often rely on trusted intermediaries because they need assurance regarding product quality. Without objective measurement systems, purchasing decisions frequently depend on personal relationships and reputation.
AI-powered quality assessment technologies have the potential to change this dynamic. Mobile-based and sensor-based solutions can provide standardised quality reports, allowing buyers to make informed purchasing decisions directly.
This could strengthen direct linkages between farmers and buyers while reducing unnecessary layers within the supply chain. Greater transparency would benefit both producers and purchasers. Digital marketplaces are already beginning to incorporate quality assessment tools, enabling farmers to showcase their produce more effectively and connect with potential buyers.
Reading market signals
Predictive analytics represents another promising area for AI adoption.
Agricultural markets are heavily influenced by fluctuations in supply and demand. Unexpected surpluses can cause prices to collapse, while shortages can lead to sharp increases. AI systems can analyse historical data, market trends, weather patterns, and arrival information to generate forecasts that support better planning.
For farmers, access to reliable forecasts can improve marketing decisions. They can choose when to sell, where to sell, and how much produce to store based on anticipated market conditions. Agribusinesses can also benefit by planning procurement activities more effectively and reducing supply chain disruptions.
As predictive tools become more accessible, they may help create a more balanced and responsive agricultural marketplace.
Bridging fields and innovation
Despite the growing interest in artificial intelligence and automation, widespread adoption across the post-harvest sector is still at an early stage. The challenge is not necessarily a lack of awareness. Farmers, FPOs, aggregators, and agribusinesses increasingly recognise the value of technology. The greater challenge lies in making these solutions accessible, affordable, and relevant to local conditions.
Many technologies developed for international markets were designed for large-scale operations handling hundreds of tonnes of produce daily. Indian agricultural supply chains are often far more fragmented, with smaller volumes, diverse crop varieties, and different operating conditions. As a result, there is a growing need for solutions that are specifically designed for Indian users.
Encouragingly, a number of Indian startups are addressing this gap by developing grading, sorting, packing, and quality assessment systems that work effectively in local environments. These technologies are helping to democratise access to advanced post-harvest capabilities that were previously available only to large enterprises.
Another important aspect of adoption is capacity building. Farmers and rural entrepreneurs must be trained to understand and utilise these technologies effectively. The success of any innovation depends not only on the technology itself but also on the confidence and skills of the people using it.
The development of rural collection centres equipped with modern infrastructure can play a crucial role in this transition. Such facilities can serve as local hubs where farmers gain access to grading, sorting, storage, quality assessment, and market linkage services. This decentralised approach reduces dependence on distant markets and creates opportunities for greater value addition closer to the farm.
As technology becomes more affordable and infrastructure continues to improve, the gap between production and market realisation can gradually be reduced. This will strengthen supply chains, improve farmer incomes, and create a more resilient agricultural ecosystem.
Seeds of future growth
The future of post-harvest management in India depends on the successful integration of technology, infrastructure, and market connectivity.
Artificial intelligence alone cannot solve every challenge. Storage facilities, collection centres, transportation networks, and market linkages remain equally important. However, AI has the potential to make these systems significantly more efficient and effective.
Encouragingly, many Indian startups are developing solutions tailored specifically to domestic requirements. Unlike imported technologies designed for large-scale foreign operations, these innovations address the realities of Indian agriculture. Affordable grading systems, mobile quality assessment tools, automated packing technologies, and intelligent monitoring systems are making advanced capabilities available to a wider range of users.
The benefits extend across the entire supply chain. Farmers can achieve higher incomes through better quality management and market access. Agribusinesses can improve consistency and reduce operational costs.
As AI, automation, robotics, and digital technologies continue to evolve, India has a unique opportunity to transform its fresh produce supply chains. By combining innovation with practical infrastructure development, the country can create a more resilient, transparent, and efficient farm-to-fork ecosystem that benefits everyone involved.
Contact details
Kshitij Thakur
Co-founder, Nanekarwadi, Rakshewadi Khed, Chakan, Pune, Maharashtra
M: 8369599377
E: kshitij@agrograde.com
