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span lang="EN-US">Price prediction and forecasting are common in the agriculture sector. The previous research shows that the advancement in prediction and forecasting algorithms will help farmers to get a better return for their produce. The selection of the best fitting algorithm for the given data set and the commodity is crucial. The historical...
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... the historical research, we can see that the researchers used different algorithms for a variety of agriculture commodity price predictions. Table 1 shows a few of them. ...Similar publications
Aquifer vulnerability has been assessed in the Painganga river basin area (Mehkar and Lonar Tehsil) of the Buldhana district, Maharashtra state located in the western part of India. The present paper focuses on the use of longitudinal unit conductance, GOD and GLSI models to measure the penetration and possibility of contamination of an aquifer. A...
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... There have been only a few studies (Zhang and Na, 2018;Hegde et al. 2023;Dash et al. 2024) conducted on forecasting agricultural price systems using fuzzy time series models. Fatih (2022) provided a comprehensive review of fuzzy logic and fuzzy time series models, focusing on their practical application in estimating and forecasting monthly international coffee prices. ...
Oilseed prices are inherently volatile and uncertain, making accurate predictions is important for the stakeholders. In time series forecasting, fuzzy techniques have proven effective for managing complex and uncertain datasets. This study introduces an innovative approach to predicting oilseeds prices by developing intuitionistic fuzzy based machine learning models. The model integrates intuitionistic fuzzy logic with stochastic and advanced machine learning techniques to enhance predictive accuracy. The main objective is to assess how this integration improves prediction accuracy, focusing on monthly wholesale prices of Sunflower from various markets in Karnataka, covering the period from January 2010 to June 2024 from the AGMARKNET portal (https://agmarknet.gov.in/). Comparative analysis with traditional models demonstrated the superior performance of the intuitionistic fuzzy based models, particularly in reducing prediction errors and accurately capturing market trends. This research underscores the potential of integrating fuzzy logic into machine learning frameworks, offering a valuable tool for stakeholders in agricultural economics and commodity trading. HIgHLIgHtS m A study investigated the impact of using fuzzy logic to manage uncertainty. m A new method for combining machine learning (ML) models with intuitionistic fuzzy logic is proposed. m A comparison between fuzzy-based ML and traditional models is conducted. m The prediction of monthly wholesale sunflower prices shows significant improvement.
... Most importantly, the gradient boosting algorithm is one of the best machine-learning techniques applied to the prediction of tasks. It has been applied in diverse fields, such as price prediction for agricultural commodities (Hegde et al. 2023), slope stability prediction in mining (Saadaari et al. 2020), and even in the development of models for prediction of soil moisture (Hwase and Fofanah 2021). The algorithm can learn functional mapping and, for this reason, it has been noted in several research findings to have very high prediction accuracy (Vanhaeren et al. 2020). ...
The aim of this study is to explore the influence of environmental, social, and governance (ESG) factors on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted principal component analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of seven variables that predict business failure. The results indicate that ESG factors significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making.
... Most importantly, the gradient boosting algorithm is one of the best machine learning techniques applied in the prediction of tasks. It has been applied in diverse fields, such as price prediction of agriculture commodities (Hegde et al., 2023), slope stability prediction in mining (Saadaari et al., 2020), and even in the development of models for prediction of soil moisture (Tesyon Korjo Hwase & Abdul Joseph Fofanah, 2021)s. The algorithm can learn functional mapping, and for this reason, it has been noted in several research findings to have very high prediction accuracy (Vanhaeren et al., 2020). ...
The aim of this study is to explore the influence of Environmental, Social, and Governance (ESG) factor on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted Principal Component Analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of 7 variables that predict business failure. The results indicate that ESG factor significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making.
p> This article investigates the transformative impact of low-cost internet of things (IoT) solutions on the agricultural sector, with a particular emphasis on integrating artificial intelligence (AI) and machine learning (ML) technologies. The study aims to illustrate how affordable IoT technologies, when combined with advanced AI and ML capabilities, can serve as a significant asset for small and medium-sized farms. It addresses the economic and technical barriers these farms face in adopting such technologies, including high initial costs and the complexity of implementation. By conducting a comprehensive evaluation of existing IoT hardware and software, the research identifies and highlights innovative, cost-effective solutions that have the potential to drive significant advancements in agricultural practices. The findings underscore how these integrated technologies can enhance operational efficiency, increase productivity, and support sustainable agricultural development. Additionally, the paper explores the potential challenges and limitations of adopting these technologies, offering insights into how they can be mitigated. Overall, the study demonstrates that the convergence of low-cost IoT with AI and ML presents a valuable opportunity for modernizing agriculture and improving farm management. </p
