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| Author (year) | Title | Findings | Relevance |
|
| Farmer suicide in Punjab |
| Grover et al. (July 2019) [8] | Farmer suicides in Punjab: causes and suggestions study sponsored by Ministry of Agriculture and Farmers Welfare Agroeconomic Research Centre, Department of Economics and Sociology. | (i) This study depicts the different causes of the farmer’s suicide | Farmer suicide in Punjab: here, we came up with data on farmers’ suicide in Punjab and their underlying reasons. |
| (ii) It provided the suggestions to improve the status of the farmers by providing them awareness of the new government policies and farming tools. |
|
| Mechanization |
| Sarkar (2020) [21] | Agricultural mechanization in India: a study on the ownership and investment in farm machinery by cultivator households across agroecological regions | (i) Mechanized plowing does not substantially reduce labor used for land preparation and in turn increases labor usage for other operations, according to marginal users of agricultural machinery. | Mechanization: we derived the need of mechanization and automation in India from these studies. The emphasis of farm mechanization is on improving agricultural productivity and enhancing availability of tools and equipment and also reducing manpower scarcity. |
| Ghosh (2010) [22] | Determinants of farm mechanization in modern agriculture: A case study of Burdwan district of West Bengal | (i) Mechanization’s importance in agriculture has grown as it improves productivity by increasing input production, speeding up agricultural operations, reducing drudgery, and lowering the cost of cultivation. | |
|
| Custom hiring center in Punjab |
| Sidhu and Vatta (2012) [23] | Improving economic viability of farming: a study of cooperative agromachinery service centers in Punjab | (i) This study emphasized on the various cooperative agromachinery service centers in Punjab. | We developed the concept of previously existing bespoke recruiting centers in India based on these studies. Additionally, it is necessary to educate farmers who reside in rural regions about custom hiring centers in India. |
| (ii) It addressed the importance of custom hiring centers in Punjab |
| Yarazari (2019) [38] | Custom hiring services of farm machinery in India. | (i) This study focuses on various custom hiring services of farm machinery in India. | |
| (ii) Due to their economic situation, small/marginal farmers are unable to acquire agricultural equipment on their own or via institutional finance. |
| (iii) As a consequence, custom hiring must be aggressively pushed in order for small/marginal landowners to access agricultural equipment. |
|
| S. S. Chahal, P. Kataria, S. Abbott, and B. S. Gill (2014) [44] | Role of cooperatives in institutionalization of custom hiring services in Punjab | (i) According to this study, bespoke hiring enables needy farmers to gain the advantages of automation via the utilization of costly equipment. | |
| (ii) Some cooperative organizations have taken the initiative to offer agricultural equipment services to farmers on a rental as well as a personalized hire basis |
|
| Renting and hiring of equipment |
| B. Jothi Jahnavi, R. Monica, N. Sripriya C. [42] | Efficient farming, hiring equipment for farmers | (i) Hire service equipment may also have several features, providing the farm family with even more advantages. | From these studies, we deduced the need of developing an uberized model for equipment rental and sharing. |
| (ii) Farming becomes more competitive as yields and time savings increase, and costs can be reduced. |
| Sukhpal singh (2017) [53] | Inclusive and effective are farm machinery rental services in India | (i) In some parts of India, realisation and local creativity have resulted in a trend of custom farm machinery rentals, which began in Punjab. | |
|
| Machine learning algorithm |
| H. H. Patel and P. Prajapati (2018) [58] | Study and analysis of decision tree-based classification algorithms | (i) The aim of this study is to provide an overview of machine learning techniques that are currently being used or considered by methodological departments across the country. | From these studies, we introduced the concept of the machine learning algorithm: decision tree classifier/regressor |
| (ii) In today’s world, a vast amount of information is readily available. |
| (iii) As a consequence, it is important to evaluate such data in order to extract some valuable information and to build an algorithm based on this research |
| H. Sharma and S. Kumar (2016) [55] | A survey on decision tree algorithms of classification in data mining | (i) To address the current issues in agriculture, a variety of strategies have been proposed, ranging from database development to selection assistance frameworks. | |
| (ii) Structures that use decision tree classifiers have been found to be the most excellent performers in terms of accuracy and robustness among these solutions. |
|
| Recommendation system |
| M. B. Santosh kumar and K. Balakrishnan (2019) [61]. | Development of a model recommender system for agriculture using the Apriori algorithm | (i) In this study, two approaches, content-based filtering and collaborative-based filtering, are discussed by the researcher. | From this study, we incorporated the concept of the recommendation system on the basis of rating and searching of products |
| (ii) Collaborative-based filtering is widely used in the building of recommender systems. |
| (iii) CBL approaches are referred to be memory-based and model-based. |
|
| Supply-demand-based optimization |
| W. Zhao, L. Wang, and Z. Zhang (2019) [63] | Supply-demand-based optimization: a novel economics-inspired algorithm for global optimization. | (i) In this study, authors focused on equilibration methods which may be utilized to address a range of supply and demand issues are implemented in this study. | We presented the idea of demand and supply algorithms and demonstrated how to optimize demand based on supply and seasonal variations through these studies. |
| (ii) This kind of algorithm attempts to equilibrate the whole system by equilibrating sequentially each supply market (producer) or each demand market (consumer). |
|
| Supply-demand-based optimization |
| L. Fleischer, R. Garg, S. Kapoor, R. Khandekar, and A. Saberi (2016) [64]. | A simple and efficient algorithm for computing market equilibria | (i) The supply-demand-based optimization (SDO) method, which is a new meta-heuristic optimization technique, is described in detail in this study. | |
| (ii) SDO is a swarm-based optimizer designed to optimize supply-demand relationships. |
| Jianjun Yu, Qiangqiang Zhu (2015) [62] | Agriculture production planning under supply uncertainty and demand uncertainty | (i) This study focuses on the demand and supply algorithm optimization on a particular market of a crop. | Demand-supply optimization with seasonal fluctuations |
| (ii) The study focuses on how the market and the company make optimal decision to improve the productivity |
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