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At the heart of India's livelihood lies agriculture, a foundational and ever-evolving sector. The realm of agriculture faces modern challenges, encompassing unpredictable climate fluctuations, insufficient irrigation infrastructure, and erratic weather conditions. Amidst these challenges, machine learning has emerged as a valuable tool for evaluating soil fertility and Crop assessment in agriculture. Ensemble machine learning methodologies have garnered attention for their potential to enhance predictive capabilities. These techniques involve constructing meta-classifiers that collectively contribute to improved predictive accuracy. This paper centers on the analysis of soil data acquired from testing laboratories, to predict fertility based on a comprehensive dataset. The study employs prominent ensemble machine learning algorithms, specifically focusing on boosting techniques, to elevate predictive accuracy and ensure heightened consistency. The assessment of soil fertility categories involved the examination of 12 carefully chosen attributes. Diverse soil parameters were measured to facilitate the prediction of soil fertility levels. The outcomes of the experimentation revealed that the application of the boosting technique using the Xgboost algorithm yielded superior accuracy compared to alternative ensemble classifiers, achieving a remarkable 96% accuracy rate. [ABSTRACT FROM AUTHOR]

Organic waste disposal methods, notably landfilling, not only deplete resources but also contribute to environmental challenges. This research looks at potential barriers to organic waste management solutions. The objective of this study is to identify the barriers to organic waste management solutions from an actor's perspective, and to explore their causal relationships to overcome the organic waste management problem from a system perspective. Several key challenges were identified regarding organic waste management solution, the current intervention overview indicates that promoting and tracking attention towards "value to waste" would be an effective solution approach. Waste collection fees, unethical behavior, and a lack of engagement and commitment in activities show a subsequent effect on consumer-household solutions, which are currently acting as priority barriers in this research. In order to have a better understanding of this complex issue, a detailed knowledge of barriers (leading to organic waste) is discovered and evaluated with the application of fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL). The data for this research has been taken in the context of a developing economy like India. This work can provide structural support to the managers by knowing the cause (influencing) and effect-group (influencing) barriers to the effective implementation of an organic waste management system in a circular economy context. [Display omitted] • The research explores potential barriers to organic waste management solutions. • A fuzzy decision making method is used to evaluate the complex problem. • The method evaluates the key barriers in context of expert-made fuzzy judgments. • The study has been carried out and is focused in context of developing countries. • The study promotes flexible strategies to provide support to managers involved. [ABSTRACT FROM AUTHOR]

Food waste management is a growing environmental and economic issue for a developing country like India. This exploratory paper aims to prioritize key practices as "criteria" for food supply chains that lead to food waste reduction to ensure green waste management. Twenty-five criteria from the existing literature and expert inputs were assessed to resolve food waste reduction issues in food supply chains, especially in the agricultural sectors. The study identifies and ranks the intensities of each of the listed criteria. In the study, the Best-Worst Method, a multi-criteria decision-making approach, is applied. According to the findings, waste prevention at the earlier stages of food supply chains is given more priority compared to food waste minimization and treatment at the later stages. Hence, the study concludes "skill and training" as the highest ranked criteria, followed by "protection and infrastructure." Further, the study infers that understanding the individual intensities of criteria within a food supply chain can affect waste generation at each level, having a significant impact on waste reduction. The data in this research was evaluated in the Indian context. • Explores the green waste management problem in food supply chains. • 25 sub-criteria & 5 main criteria are listed in the circular economy context. •Conducted an empirical case study in the Uttarakhand region in India. • The Best Worst Method is used to evaluate the outcomes. • Training of growers is ranked as the topmost criteria. [ABSTRACT FROM AUTHOR]