Purpose: This paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.Design/methodology/approach: The authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.Findings: Fifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.Research limitations/implications: The authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.Originality/value: Earlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

The purpose of this study is to examine the value chain analysis of, consumers' awareness level of, and buying motives toward mushroom products. The primary data were collected through face-to-face interviews and by using a questionnaire filled out by 70 consumers from various major mushroom production districts, selected by non-random sampling. Secondary data were collected from various sources. Based on the data analysis, it was found that around 73% of consumers preferred to buy their mushroom products in the supermarket, and 71% of consumers responded that factors other than the shape, color, and size of mushrooms motivated them to buy them. The research concludes that urban consumers were well aware of the nutrition value of mushrooms, but their consumption level was very low compared to non-vegetarians urban consumer in the studied area. Nearly 30% of respondents believed a myth about mushrooms having a mold and were prepared from compost, which causes a bad smell. It was found that that while producers put forth more effort and energy than other actors into the mushroom value chain they received less revenue and profit than wholesalers and retailers. Therefore, policy makers should develop new norms to remove barriers and others issues to safeguard mushroom producers for sustainable growth of the mushroom sector in India. In the future, there will be an increase in the consumption of processed foods. Hence, food companies have to concentrate on mushroom processing and fresh mushroom production.

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Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.