Comparative analysis of machine learning techniques for predicting production capability of crop yield

Jain, Kishor S.; Choudhary, Naveen

doi:10.1007/s13198-021-01543-8

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…, 2022). Additionally, AI has been used for predicting crop yields (Jain and Choudhary, 2022; Saranya et al. , 2021), optimizing crop selection to improve agricultural planning (Kumar et al.…”

Section: Resultsmentioning

confidence: 99%

“…, 2022a), also they aid to forecast production such as in the case of Pakistani wheat (Ahmed and Hussain, 2022), to solve crop selection problems (Kumar et al. , 2015) and to boost crop yield (Fenu and Malloci, 2021; Jain and Choudhary, 2022). However, sustainability and agriculture production efficiency are intertwined, as AI could enhance sustainable digital transformation (Lugonja et al.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, AI plays a key role in enhancing the adoption of sustainable practices in agriculture practices to meet sustainable development goals (SDGs) (Abban and Abebe, 2022;Morales and Elkader, 2020). (Anand et al, 2022;Khan et al, 2022), livestock (Goel et al, 2022a), also they aid to forecast production such as in the case of Pakistani wheat (Ahmed and Hussain, 2022), to solve crop selection problems (Kumar et al, 2015) and to boost crop yield (Fenu and Malloci, 2021;Jain and Choudhary, 2022). However, sustainability and agriculture production efficiency are intertwined, as AI could enhance sustainable digital transformation (Lugonja et al, 2022) and farming practices (Alam et al, 2020).…”

Section: Bibliographic Coupling Analysismentioning

confidence: 99%

“…Previous research has explored the use of AI within the agri-food sector across various contexts, including smart farming, where it is employed to boost effective agricultural practices (Moreira et al, 2022), address challenges in suburban and rural areas (Doshi and Varghese, 2022), and bolster sustainable digital transformation (Lugonja et al, 2022). Additionally, AI has been used for predicting crop yields (Jain and Choudhary, 2022;Saranya et al, 2021), optimizing crop selection to improve agricultural planning (Kumar et al, 2015). Moreover, AI-based technologies and systems have proven effective in diagnosing and detecting plant and crop diseases, which can contribute to higher production levels (Moreira et al, 2022;Fenu and Malloci, 2021).…”

Section: Systematic Analysismentioning

confidence: 99%

See 3 more Smart Citations

Unleashing the value of artificial intelligence in the agri-food sector: where are we?

Trabelsi,

Casprini,

Fiorini

et al. 2023

BFJ

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PurposeThis study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.Design/methodology/approachThis study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.FindingsSix clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.Originality/valueThis study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

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Section: Resultsmentioning

confidence: 99%