Machine learning-based agriculture

Khan, Rijwan; Khan, Mohammad Ayoub; Ansari, Mohammad Aslam; Dhingra, Niharika; Bhati, Neha

doi:10.1016/b978-0-323-90550-3.00003-5

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…These AI-based technologies help predicting and monitoring crop diseases (Anand et al. , 2022; Khan et al. , 2022), livestock (Goel 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%

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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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“…These AI-based technologies help predicting and monitoring crop diseases (Anand et al. , 2022; Khan et al. , 2022), livestock (Goel et al.…”

Section: Resultsmentioning

confidence: 99%

Section: Bibliographic Coupling Analysismentioning

confidence: 99%

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

Trabelsi,

Casprini,

Fiorini

et al. 2023

BFJ

View full text Add to dashboard Cite

show abstract

“…Among them pickling is one of the best methods for processing of these vegetables and it can be used around the year. When any food (fruits, vegetables, fish or meat) is preserved by natural preservatives like salt, organic vinegar and edible oil along with lactic acid fermentation, then the processed food is called pickles [3,4]. Pickles are good appetizers and add to the palatability of a meal.…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment of Mixed Pickle (Carrot, Pea and Ginger) Fermented by Lactic Acid Bacteria

Rymbai

Chaurasiya²

2022

AJAHR

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“Studies on preparation and quality evaluation of mixed pickle of carrot, pea and ginger” was made with seven treatments and three replications with the equal proportion of carrot, pea and ginger (500 gm each), Salt 10% and lakadong turmeric powder of 10 gm for all treatments with the objective to access the physico-chemico characteristics of fresh vegetables as well as stored mixed pickle at normal room temperature. The biochemical and sensory changes were analysed at monthly interval of initial day, 30 days and 60 days. During storage the TSS, total sugar, acidity, ascorbic acid, β- carotene acid content decreased gradually. The prepared mixed pickle was judged by the 15 panellists during the organoleptic test where using Five-Point hedonic scale in which treatment T7 (2% lemon juice + 2% acetic acid + mustard oil (250 ml) + red chilli (20 g) recorded the maximum value with 4.53 followed by treatment T5 (2% acetic acid + red chilli powder (20 g) + mustard oil (250 ml) with 4.32 up to 60 days of storage. Overall assessment of quality revealed that with respect to conjugal study of all the parameters of total soluble solid, total sugar, acidity and ascorbic acid and β- carotene, the treatment T5 prepared with (2% acetic acid + red chilli powder (20 g) + mustard oil (250 ml) proved considerably superior over other treatments with the maximum acceptability.

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“…FIG-2-Visual Abbreviation[10] Farmers often follow the processes listed below when carrying out agricultural operations.Step 1: Selecting a Crop Step 2: Preparing the Land Step 3: Seeding Step 4: Fertilizing and Watering…”

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confidence: 99%

Artificial Intelligence Applications for Innovative Farming and IoT-based Agriculture

Pandey,

Bargavi

2024

Int. J. Res. Publ. Rev.

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With the rise of Internet of Things (IoT) technology, there has been a profound transformation in various aspects of daily life. Among the many areas benefiting from IoT, smart agriculture stands out as a field of significant interest for researchers. Leveraging IoT and Machine Learning (ML), innovative research in this domain has been flourishing.IoT-based smart agriculture offers a range of benefits, from optimizing input costs to reducing losses and enhancing resource efficiency. By harnessing data-driven farm management strategies, agricultural productivity can be significantly improved. The IoT generates vast amounts of data with diverse attributes, influenced by factors such as time and location. Effective analysis and processing of this data are crucial for implementing intelligent farm management practices.As more data is collected, the high-performance computing capabilities of ML unlock new possibilities for data-intensive scientific endeavours. This synergy between IoT and ML holds promise for further advancements in agricultural productivity and sustainability.

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Machine learning-based agriculture

Cited by 6 publications

References 23 publications

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

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

Quality Assessment of Mixed Pickle (Carrot, Pea and Ginger) Fermented by Lactic Acid Bacteria

Artificial Intelligence Applications for Innovative Farming and IoT-based Agriculture

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