Commercial Crops (Jute, Cotton and Sugarcane)

Kar, Gouranga; Blaise, D.; Srivastava, T. K.; Kar, Chandan Sourav; Verma, P.; Reddy, A. R.; Singh, Pushpa

doi:10.1007/978-981-19-7997-2_14

Trajectory of 75 Years of Indian Agriculture After Independence 2023

DOI: 10.1007/978-981-19-7997-2_14

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Commercial Crops (Jute, Cotton and Sugarcane)

Gouranga Kar,

D. Blaise,

T. K. Srivastava

et al.

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2024

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Cited by 1 publication

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Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools

Malashin,

Tynchenko,

Gantimurov

et al. 2024

Sustainability

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Optimizing agricultural productivity and promoting sustainability necessitates accurate predictions of crop yields to ensure food security. Various agricultural and climatic variables are included in the analysis, encompassing crop type, year, season, and the specific climatic conditions of the Indian state during the crop’s growing season. Features such as crop and season were one-hot encoded. The primary objective was to predict yield using a deep neural network (DNN), with hyperparameters optimized through genetic algorithms (GAs) to maximize the R2 score. The best-performing model, achieved by fine-tuning its hyperparameters, achieved an R2 of 0.92, meaning it explains 92% of the variation in crop yields, indicating high predictive accuracy. The optimized DNN models were further analyzed using explainable AI (XAI) techniques, specifically local interpretable model-agnostic explanations (LIME), to elucidate feature importance and enhance model interpretability. The analysis underscored the significant role of features such as crops, leading to the incorporation of an additional dataset to classify the most optimal crops based on more detailed soil and climate data. This classification task was also executed using a GA-optimized DNN, aiming to maximize accuracy. The results demonstrate the effectiveness of this approach in predicting crop yields and classifying optimal crops.

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Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools

Malashin,

Tynchenko,

Gantimurov

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Commercial Crops (Jute, Cotton and Sugarcane)

Cited by 1 publication

References 32 publications

Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools

Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools

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