Soil Based Prediction for Crop Yield using Predictive Analytics

Chandraprabha, M.; Dhanaraj, Rajesh Kumar

doi:10.1109/icac3n53548.2021.9725758

Cited by 21 publications

(6 citation statements)

References 10 publications

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“…Predictive analytics support planning for risk management, gaining apex productivity, improving farmers’ performance, fulfilling customer demands, and improving crop management ( Vijayabaskar et al, 2017 ; Gupta and Malik, 2022 ). In addition to this, predictive analytics finds its application in precision farming, soil nutrients level estimation, predictions of crop yields, inventory management, and the facilitation of DSS ( Kolipaka, 2020 ; Chandraprabha and Dhanaraj, 2021 ; Kumar et al, 2022 ). Mostly agricultural firms from developed countries are exploring this domain of AI for increased gains and better supply chain management.…”

Section: Applications Of Ai In Agriculturementioning

confidence: 99%

AI can empower agriculture for global food security: challenges and prospects in developing nations

Ahmad,

Liew,

Venturini

et al. 2024

Front. Artif. Intell.

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Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030—Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.

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Section: Applications Of Ai In Agriculturementioning

confidence: 99%

AI can empower agriculture for global food security: challenges and prospects in developing nations

Ahmad,

Liew,

Venturini

et al. 2024

Front. Artif. Intell.

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“…The best performance among these models is shown by SVR model, while KNN model showed poor performance among them. Furthermore, several other studies have discussed the influence of soil factors on crop yield prediction such as [18,19]. P. Das et al [20] presented a novel hybrid approach of combining the soft computing algorithm, multivariate adaptive regression spline (MARS) for feature selection with SVR and ANN models to predict grain yield.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Nikhil,

Pandiyan,

Raja

et al. 2024

Computers

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Agriculture is one of the most important activities that produces crop and food that is crucial for the sustenance of a human being. In the present day, agricultural products and crops are not only used for local demand, but globalization has allowed us to export produce to other countries and import from other countries. India is an agricultural nation and depends a lot on its agricultural activities. Prediction of crop production and yield is a necessary activity that allows farmers to estimate storage, optimize resources, increase efficiency and decrease costs. However, farmers usually predict crops based on the region, soil, weather conditions and the crop itself based on experience and estimates which may not be very accurate especially with the constantly changing and unpredictable climactic conditions of the present day. To solve this problem, we aim to predict the production and yield of various crops such as rice, sorghum, cotton, sugarcane and rabi using Machine Learning (ML) models. We train these models with the weather, soil and crop data to predict future crop production and yields of these crops. We have compiled a dataset of attributes that impact crop production and yield from specific states in India and performed a comprehensive study of the performance of various ML Regression Models in predicting crop production and yield. The results indicated that the Extra Trees Regressor achieved the highest performance among the models examined. It attained a R-Squared score of 0.9615 and showed lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 21.06 and 33.99. Following closely behind are the Random Forest Regressor and LGBM Regressor, achieving R-Squared scores of 0.9437 and 0.9398 respectively. Moreover, additional analysis revealed that tree-based models, showing a R-Squared score of 0.9353, demonstrate better performance compared to linear and neighbors-based models, which achieved R-Squared scores of 0.8568 and 0.9002 respectively.

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“…As technology advanced, the scope of predictive analytics in agriculture expanded. Chandraprabha and Dhanaraj (2021) discuss the application of predictive analytics in soil analysis, a critical aspect of crop yield determination. By leveraging machine learning algorithms, such as Naïve Bayes and Bayes Net, predictive models could analyze soil data to recommend suitable crops based on nutrient levels and pH values.…”

Section: Historical Evolution Of Predictive Analytics In Agriculturementioning

confidence: 99%

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Oluwafunmi Adijat Elufioye,

Chinedu Ugochukwu Ike,

Olubusola Odeyemi

et al. 2024

Comput. sci. IT res. j.

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This study provides a comprehensive review of the integration and impact of Artificial Intelligence (AI) in agricultural supply chains, focusing on its role in enhancing demand forecasting and optimizing supply. The primary objective was to assess how AI-driven predictive analytics transforms agricultural practices, addressing challenges, and shaping future trends. A systematic literature review and content analysis methodology were employed, utilizing academic databases and digital libraries to source peer-reviewed articles and conference papers published between 2014 and 2024. The inclusion criteria focused on studies related to AI applications in agricultural supply chains, while exclusion criteria filtered out non-peer-reviewed and irrelevant literature. Key findings reveal that AI significantly improves the accuracy and efficiency of demand forecasting and supply chain operations in agriculture. AI technologies, including machine learning and big data analytics, have led to advancements in real-time data analysis, predictive maintenance, and resource optimization. However, challenges such as data quality, infrastructure development, and skill gaps among agricultural professionals persist. The future landscape of AI in agriculture is marked by growth opportunities and challenges, including the need for equitable AI technology access and ethical considerations. The study recommends that industry leaders and policymakers invest in infrastructure, promote AI research and development, and provide training to facilitate AI adoption. Future research should focus on developing robust AI models tailored to agriculture, exploring AI's integration with emerging technologies, and assessing AI's long-term socio-economic impacts. This study contributes to understanding AI's current applications and future potential in transforming agricultural supply chains, offering valuable insights for stakeholders in the agricultural sector. Keywords: Artificial Intelligence, Agricultural Supply Chains, Predictive Analytics, Demand Forecasting.

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Soil Based Prediction for Crop Yield using Predictive Analytics

Cited by 21 publications

References 10 publications

AI can empower agriculture for global food security: challenges and prospects in developing nations

AI can empower agriculture for global food security: challenges and prospects in developing nations

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

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