A review on the applications of machine learning and deep learning in agriculture section for the production of crop biomass raw materials

Peng, Wei; Sadaghiani, Omid Karimi

doi:10.1080/15567036.2023.2232322

Cited by 5 publications

(3 citation statements)

References 146 publications

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“…These methodologies not only enhance the sustainability of agricultural outputs but also optimize resource use, such as water and fertilizers, contributing to environmental stewardship [15]. For instance, ML algorithms have been utilized to optimize fertilizer application rates, striking a balance between maximizing crop productivity and minimizing environmental impacts [16]. Additionally, ML is crucial in soil health assessment, evaluating critical soil properties to inform crop rotation and soil management strategies, thus preserving soil fertility and promoting robust crop yields [17].…”

Section: Ml's Techniques In Agricultural Practicesmentioning

confidence: 99%

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A Review of Machine Learning Techniques in Agroclimatic Studies

Tamayo-Vera,

Wang,

Mesbah

2024

Agriculture

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The interplay of machine learning (ML) and deep learning (DL) within the agroclimatic domain is pivotal for addressing the multifaceted challenges posed by climate change on agriculture. This paper embarks on a systematic review to dissect the current utilization of ML and DL in agricultural research, with a pronounced emphasis on agroclimatic impacts and adaptation strategies. Our investigation reveals a dominant reliance on conventional ML models and uncovers a critical gap in the documentation of methodologies. This constrains the replicability, scalability, and adaptability of these technologies in agroclimatic research. In response to these challenges, we advocate for a strategic pivot toward Automated Machine Learning (AutoML) frameworks. AutoML not only simplifies and standardizes the model development process but also democratizes ML expertise, thereby catalyzing the advancement in agroclimatic research. The incorporation of AutoML stands to significantly enhance research scalability, adaptability, and overall performance, ushering in a new era of innovation in agricultural practices tailored to mitigate and adapt to climate change. This paper underscores the untapped potential of AutoML in revolutionizing agroclimatic research, propelling forward the development of sustainable and efficient agricultural solutions that are responsive to the evolving climate dynamics.

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Section: Ml's Techniques In Agricultural Practicesmentioning

confidence: 99%

“…The most commonly used algorithms in agriculture-related ML and DL studies are Artificial Neural Networks (ANNs), Random Forest, and Support Vector Machine (SVM) [16]. These techniques are applied in various agro-meteorological applications like maximizing crop yield and minimizing water use [64].…”

Section: Algorithms and Metrics Used In Agriculture Applicationsmentioning

confidence: 99%

A Review of Machine Learning Techniques in Agroclimatic Studies

Tamayo-Vera,

Wang,

Mesbah

2024

Agriculture

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“…and save a lot of money for the cultivation of industrial varieties. With complex and multi-dimensional processes, biomass utilization is difficult to predict, and ML has been applied in parts of biomass utilization [35]. To our best knowledge, there have been no studies on MCC and CNC synthesis prediction using machine learning; we implemented the first application of predicting CNC yields based on biomass quality traits.…”

Section: Extra Trees Modeling For Yields Of MCC and Nccmentioning

confidence: 99%

Screening of Miscanthus Genotypes for Sustainable Production of Microcrystalline Cellulose and Cellulose Nanocrystals

Liu,

You,

Wang

et al. 2024

Agronomy

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Miscanthus spp. has been regarded as a promising industrial plant for the sustainable production of bio-based materials. To assess its potential for microcrystalline cellulose (MCC) and cellulose nanocrystals (CNCs) production, 50 representative clones of M. sinensis and M. floridulus were selected from a nationwide collection showcasing the extensive diversity of germplasm resources. Descriptive analysis indicates that the dry biomass weight of M. floridulus is advantageous whereas M. sinensis demonstrates higher MCC and CNCs yields as well as a smaller CNCs particle size. Correlation analyses indicated that MCC yield is solely influenced by the cellulose content whereas the yield of CNCs is affected by both the cellulose content and CrI. Comparative analyses of the chemical composition, physical features (degree of polymerization, crystalline index, particle size distribution and zeta potential), and scanning electron microscopy indicated that the MCC and CNCs extracted from M. sinensis and M. floridulus exhibited remarkable stability and quality. Additionally, the CNCs derived from M. sinensis and M. floridulus exhibited a distinctive ball-shaped structure. Notably, machine learning has demonstrated its efficacy and effectiveness in the high-throughput screening of large populations of Miscanthus spp. for predicting the yield of MCC and CNCs. Our results have also laid the theoretical foundation for the exploration, cultivation, and genetic breeding of M. sinensis and M. floridulus germplasm resources with the purpose of MCC and CNCs preparation.

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