Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

• Published in: Journal of food quality Vol. 2022; pp. 1 - 19
• Main Authors: Rakhra, Manik, Bhargava, Amitabh, Bhargava, Deepshikha, Singh, Ramandeep, Bhanot, Astha, Rahmani, Abdul Wahab
• Format: Journal Article
• Language: English
• Published: Cairo Hindawi 15.03.2022; John Wiley & Sons, Inc; Hindawi Limited; Hindawi-Wiley
• Subjects: Agribusiness; Agricultural economics; Agricultural production; Agricultural societies; Agricultural technology; Agriculture; Artificial intelligence; Automation; Costs; Crops; Farmers; Farmers markets; Farms; Food quality; Food security; Harvesting; Innovations; Machine learning; Machinery; Magneto-electric machines; Microorganisms; Productivity; Raw materials; Resource management; Supply chains; India
• ISSN: 0146-9428; 1745-4557
• DOI: 10.1155/2022/4496449

Several industries have recently seen the replacement of human labor by automated machinery and equipment. Across the globe, farmers’ attitudes on the use of technology in agriculture are divergent. However, although some people are excited and ready to embrace technology, others are cautious and wary of trying new technologies for the first time. The third category is particularly prevalent in underdeveloped nations such as India, owing to a lack of competence, a lack of effective translation, and most crucially, a lack of financial resources. It is fruitless for the government to attempt to resolve these difficulties due to the fact that they do not take into consideration the changing circumstances and input needs of each agricultural group. Smart Tillage is a cutting-edge framework that was developed to solve the challenges listed above. In India, a decision-based smart engine for the rental and sharing of tools and equipment has been developed, which leverages machine learning methods to proceed towards a selection of tools and equipment. The option is entirely reliant on a variety of input variables, including crop kind, harvest time/month, crop equipment needed, harvest type, and the amount of money available for rental. Additionally, an ideal recommendation engine driven by content and collaborative-based filtering will provide the farmer’s requirements depending on their requirements. In terms of escalation, the proposals would be cost-effective and excellent since they would need little changes in training, technique improvements, and resource management via a new rent-share model similar to that used by Uber. In this work, demand and supply algorithms are used to define market equilibrium, and the results are shown in graphs. This includes discussion of a variety of demand and supply parameters, their impact on market equilibrium prices and quantities, and their effect on shifting demand and supply curves. The many sorts of elasticities (demand, cross-price, supply, income, and so on) are examined, as well as the ramifications for pricing systems that may result from these elasticities.