Maximizing Profits in Crop Planning Using Socio Evolution and Learning Optimization

Brindha, G.; Gopi, E. S.

doi:10.1007/978-981-13-6569-0_8

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…All three plans feature yield, water, fertilizer, insecticides, and profit estimates in addition to suggested agricultural actions. Three-year plans are making it easier to see the effects of various agricultural practices, such as the adoption of a no-tillage approach, and the associated environmental, financial, and technical advantages [14].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

Alattab¹,

Ibrahim²,

Irshad³

et al. 2023

Computers, Materials &Amp; Continua

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This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.

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Section: Introductionmentioning

confidence: 99%

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

Alattab¹,

Ibrahim²,

Irshad³

et al. 2023

Computers, Materials &Amp; Continua

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Socio-inspired evolutionary algorithms: a unified framework and survey

2023

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Evolutionary computation has gone through vast and diverse research endeavors in the past few decades. Although the initial inspiration came from Darwin's ideas of biological evolution, the field has moved thereon to ideas from the collective intelligence of insects, birds, and fish, to name a few. A variety of algorithms have been proposed in the literature based on these ideas and have been shown to perform well in various applications. More recently, inspiration from human behavior and knowledge exchange and transformation has given rise to a new evolutionary computing paradigm. It is well recognized that human societies and problem-solving capabilities have evolved much faster than biological evolution. Many research endeavors have been reported in the literature inspired by diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in a plethora of algorithms worded differently from each other, but the underlying mechanisms could be more or less similar, causing immense confusion for a new reader. This paper presents a generalized framework for these Socio-inspired Evolutionary Algorithms (SIEAs) or Socio-inspired Metaheuristic Algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description of each SIEA enables a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. In that sense, this paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research.

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Optimization model to support sustainable crop planning for reducing unfairness among farmers

Esteso

Alemany

Ortíz

et al. 2021

Cent Eur J Oper Res

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Inherent uncertainty surrounding the agri-food sector negatively impacts the supply chain's (SC) sustainability and performance. A main consequence of this uncertainty is the imbalance between supply and demand with volatility in prices and high quantities of waste and unmet demand. Usually, farmers are the most affected by the negative impact of uncertainty. To improve their competitive position, it is necessary to implement new business models that encourage the collaboration among farms, try to reduce the number of intermediaries between farms and markets, reduce the activities related to the management of perishable crops and their associated costs, and enable mechanisms to sell the oversupply of crops such as their settlement. In this paper, a novel multi-objective model is proposed to support the crop planning under uncertainty for the proposed business model. Three objectives aligned with the triple bottom lines are considered: SC profit maximization (economic), waste minimization (environmental) and unfairness minimization (social). The last objective reduces the unwillingness of farms to cooperate with the crop planning. The model is solved with the weighted sum method and compared to an equivalent model considering only economic objectives, concluding that environmental and social aspects can be highly improved by little decreasing profits.

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Maximizing Profits in Crop Planning Using Socio Evolution and Learning Optimization

Cited by 5 publications

References 12 publications

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

Socio-inspired evolutionary algorithms: a unified framework and survey

Optimization model to support sustainable crop planning for reducing unfairness among farmers

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