Artificial Intelligence in the Food Processing Industry in India
Hon’ble Prime Minister of India recently said that India has an overdue food processing revolution that must have occurred two or three decades back. Artificial Intelligence can act as the stepping stone to such a revolution in the years to come. India has been primarily an agricultural nation since the early times. Indian spices and their demand in the western world led to major revolutions and explorations by the colonials. To date, more than 70% of India is directly or indirectly engaged in agriculture. As India is a major producer of grains and spices in the world, it makes the nation an ideal place for food processing units. The food processing industry in India is growing at a high rate (CAGR~20%) with the industry accounting for 32% of India’s total food market according to the Indian Brand Equity Foundation. Multiple unicorns in the food sector, with the likes of Swiggy, Zomato, BigBasket makes us realize the true potential of the organized food sector in the agrarian economy and the huge scope for growth of the industry in the upcoming years.
India is a developing country and there has been a huge growth in terms of technology and infrastructure in the past few decades, yet there is a greater scope of improvement in the times to come. There is an emphasis on increasing the production by the government through various schemes such as the upcoming Production Linked Incentive Scheme where technological influence can make a major change. In the scheme, INR 10,900 crore has been allotted to the food processing industries, as it is a priority sector. Artificial intelligence is an upcoming field and has played a pivotal role in revolutionizing the internet as we see it, along with many other industries, ranging from letter delivery systems to Netflix recommendation algorithms.
Wastage Prevention and Crop Selection
According to a report by the Central Institute of Post-Harvest Engineering and Technology (CIPHET), around 16% of India’s total agricultural produce is wasted. Alternatively, they could have been processed, or maybe more demanded crops could have been grown in place of them, which could be further exported, leading to an increase in the farmer’s income and nation’s GDP at the same time. The majority of crop selection in agriculture in the nation is done on the basis of region and visible options, whereas the soil may be more optimized to grow other crops that are not in the knowledge of the farmers. This is where Machine Learning can come to the rescue. We can feed data from various states, even different nations, regarding a number of parameters which may be diverse, ranging from soil type, crop yield, cost of growing, selling price expectations, previous data, wastage percentage, and various other parameters that can be used to fit a prediction model which can analyze the parameters through neural networks which may predict the best crop on the basis of your parameters and also an expected profit after growing that particular crop. As the success of this model is based on the data inputted into the neural network, the prediction model will get more efficient with time and give better predictions in the years to come. Thus if there is a central agency monitoring the demand of particular crops in the nation and also the export requirements, then feeding this demand into the system can make the distribution of all the crops demanded a very optimized process.
Supply Chain and Crop Management
Artificial intelligence can play a role in crop management in various innovative ways. Recently, a food-tech startup in Switzerland, Gayama, developed a surveillance system using drones to map the growth of crops and report regarding changes in water, fertilizer, pests, and yield using Computer Vision. Computer vision is widely used in the analysis of physical entities and there is further scope of research in this revolutionary technology and its application in the agriculture and food industries. Supply chain management can also be optimized to prevent food wastage as artificial intelligence can be used to tackle the inefficiency of primitive supply chains. There have been various startups such as LogiNext and Locus.sh working on improving the efficiency of the supply chains in the food processing industry, from the farms where crops are grown to the manufacturing units where they are converted into finished goods, ready for retail markets. Exploring unobserved domains in the food processing industry and finding opportunities to enhance the efficiency of food production in the nation can be a revolutionary step. Hopefully, the industry will have rapid technological advancements leading to a sustainable shift in the years to come.
India is a developing nation and there have been rapid strides of development in the infrastructure and industry-specific facilities in the nation over the last few years. An essential facility for the food processing industries and other similar units is cold storage capacity. According to a report by the National Bank for Agriculture and Rural Development Consultancy Services (NABCONS), the current cold storage capacity of India stands at 374.25 Lakh MT as of September 2020, and there is a requirement of at least 350 Lakh MT more, which is somewhere around 193% of the current capacity. The government is infusing huge amounts of capital into building such facilities and infrastructure for supporting the food processing industries. The Pradhan Mantri Kisan Sampada Yojana (PMKSY) has specific provisions for financial support in form of a grant-aid in the scheme for integrated cold chain and value addition infrastructure. Machine learning can come in handy for this step also. On the basis of the production statistics of various states and the amount of wastage of crops due to lack of infrastructure, models can be implemented which can define a loss function proportional to the wastage and optimize the location of upcoming cold storage plants, so as to enhance the utilization of resources.
Artificial Intelligence is an important development in the world of computers and has brought revolutions in the world through various innovations, and this will be witnessed in the food processing industry also in the times to come. We discussed how Artificial Intelligence, namely Machine Learning can be used to select and save crops, optimize processes and many more possibilities in the field and McKinsey reported that: Efficient use of artificial intelligence to prevent food wastage, is an economic opportunity worth $127 bn by 2030. Increasing investor sentiment towards the food-tech startup ecosystem and phenomenal success of various agri-tech startups is just the beginning of the development of a bigger economy in the innovative food processing sector and we hope to see further capital infusion in the industry in the times to come.
This blog has been co-authored by Aryansh Singh and Sanya Talwar.