Artificial intelligence in agricultural value chain: review and future directions

Abstract

PurposeThis 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/approachThe 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.FindingsFifty 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/implicationsThe 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/valueEarlier 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.

An ensemble design of a cash crops-warehouse receipt system (WRS) based on blockchain smart contracts

“…Likewise, the present study may be complemented with an empirical investigation wherein perspectives from the farmers may be solicited to understand the benefits accrued from the improvised WRS. Finally, the present study provides a sounding base for further research vis-à-vis the harnessing of the Industry 4.0 catalysts like the AI in the agrarian value chain and the manner in which the latter may be improvised for realizing the potential benefits therefrom (Ganeshkumar et al. , 2021).…”

Observation of post-yield supply chain impediments for spoilage mitigation and revenue generation opportunities at countryside

“…The technique of food processing/value addition eventually increases farmers’ revenue by extending the shelf-life of the produce and market prospects (Bhatnagar et al , 2019; Anand and Barua, 2022; Priyadarshi et al , 2022a, 2022b; Ganeshkumar et al , 2023). The above types of losses have resulted in revenue losses in PYSC, leading to lesser returns on investment (Bhatnagar et al , 2019; Minten et al , 2021; Anand and Barua, 2022; Akpa et al , 2023; Ganeshkumar et al , 2023). Providing cost subsidies during yield or adopting a price realisation strategy for the produce instead of minimum support price (MSP) has been in discussion (Chintapalli and Tang, 2022; Naik and Mohan, 2023).…”

“…The losses that occur in a fresh supply chain are mainly categorised into phases such as at the cultivation/farm level (diseases, weeds, rodents, insects and mites, water, frost, grading, yielding and packing), during handling, storage and transportation (physical losses, shelf-life losses, weight losses, moisture losses, aerobic losses/temperature losses, quality losses, pilferage and network failure losses) and finally at retail (food outbreak, loss due to consumer behaviour/ product return, loss of brand value, loss in competition and stochastic demand/loss due to forecast accuracy). All of these are majorly categorised and merely cited as "post-yield losses" in business-oriented articles (Chandrasekaran and Ranganathan, 2017;Gunasekera et al, 2017;Bhatnagar et al, 2019;Priyadarshi et al, 2022aPriyadarshi et al, , 2022bAkpa et al, 2023;Di Noia et al, 2023;Ganeshkumar et al, 2023). A higher amount of waste has often been observed in longer downstream supply chains.…”

“…It is also reported that there is a demand and supply imbalance in local, national and international markets (Balaji and Arshinder, 2016). Therefore, it is vital to note that existing PYSCs need a modernisation drive to fulfil consumer demands based on future trends and use to achieve business sustainability (Gunasekera et al , 2017; Ganeshkumar et al , 2023). The adoption of best PYSC practices was a point of discussion at the recent G20 summit.…”

Envisioning digitalised value chains in the aftermath of COVID-19: a case study of Philippine coffee

“…In other words, digitalisation is not merely concerned with the use of digital technologies per se , but rather the ramifications of whether these advances provide overall benefits, and mitigating the deleterious effects such as workforce displacement, cyber-attacks and threats to business models (Mondragon et al ., 2021; Zeb et al ., 2021). In an AFVC context, digitisation can be understood as onboarding functions to an electronic environment, but digitalisation is to leverage on the potential of such tools, such as artificial intelligence to provide informed decision-making (Ganeshkumar et al ., 2021).…”