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  • Open Access

    REVIEW

    An Integrated Analysis of Yield Prediction Models: A Comprehensive Review of Advancements and Challenges

    Nidhi Parashar1, Prashant Johri1, Arfat Ahmad Khan5, Nitin Gaur1, Seifedine Kadry2,3,4,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 389-425, 2024, DOI:10.32604/cmc.2024.050240 - 18 July 2024

    Abstract The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The… More >

  • Open Access

    ARTICLE

    AI Method for Improving Crop Yield Prediction Accuracy Using ANN

    T. Sivaranjani1,*, S. P. Vimal2

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 153-170, 2023, DOI:10.32604/csse.2023.036724 - 26 May 2023

    Abstract Crop Yield Prediction (CYP) is critical to world food production. Food safety is a top priority for policymakers. They rely on reliable CYP to make import and export decisions that must be fulfilled before launching an agricultural business. Crop Yield (CY) is a complex variable influenced by multiple factors, including genotype, environment, and their interactions. CYP is a significant agrarian issue. However, CYP is the main task due to many composite factors, such as climatic conditions and soil characteristics. Machine Learning (ML) is a powerful tool for supporting CYP decisions, including decision support on which… More >

  • Open Access

    ARTICLE

    Optimized Deep Learning Methods for Crop Yield Prediction

    K. Vignesh1,*, A. Askarunisa2, A. M. Abirami3

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1051-1067, 2023, DOI:10.32604/csse.2023.024475 - 15 June 2022

    Abstract Crop yield has been predicted using environmental, land, water, and crop characteristics in a prospective research design. When it comes to predicting crop production, there are a number of factors to consider, including weather conditions, soil qualities, water levels and the location of the farm. A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting. The combination of data mining and deep learning creates a whole crop yield prediction system that is able to connect raw data to predicted crop yields. The suggested study uses a Discrete Deep… More >

  • Open Access

    ARTICLE

    Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum

    S. Vinson Joshua1, A. Selwin Mich Priyadharson1, Raju Kannadasan2, Arfat Ahmad Khan3, Worawat Lawanont3,*, Faizan Ahmed Khan4, Ateeq Ur Rehman5, Muhammad Junaid Ali6

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5663-5679, 2022, DOI:10.32604/cmc.2022.027178 - 21 April 2022

    Abstract The exponential growth of population in developing countries like India should focus on innovative technologies in the Agricultural process to meet the future crisis. One of the vital tasks is the crop yield prediction at its early stage; because it forms one of the most challenging tasks in precision agriculture as it demands a deep understanding of the growth pattern with the highly nonlinear parameters. Environmental parameters like rainfall, temperature, humidity, and management practices like fertilizers, pesticides, irrigation are very dynamic in approach and vary from field to field. In the proposed work, the data… More >

  • Open Access

    ARTICLE

    Modeling of Chaotic Political Optimizer for Crop Yield Prediction

    Gurram Sunitha1,*, M. N. Pushpalatha2, A. Parkavi3, Prasanthi Boyapati4, Ranjan Walia5, Rachna Kohar6, Kashif Qureshi7

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 423-437, 2022, DOI:10.32604/iasc.2022.024757 - 15 April 2022

    Abstract Crop yield is an extremely difficult trait identified using many factors like genotype, environment and their interaction. Accurate Crop Yield Prediction (CYP) necessitates the basic understanding of the functional relativity among yields and the collaborative factor. Disclosing such connection requires both wide-ranging datasets and an efficient model. The CYP is important to accomplish irrigation scheduling and assessing labor necessities for reaping and storing. Predicting yield using various kinds of irrigation is effective for optimizing resources, but CYP is a difficult process owing to the existence of distinct factors. Recently, Deep Learning (DL) approaches offer solutions… More >

  • Open Access

    ARTICLE

    A Hybrid Approach of TLBO and EBPNN for Crop Yield Prediction Using Spatial Feature Vectors

    Preeti Tiwari1, *, Piyush Shukla1

    Journal on Artificial Intelligence, Vol.1, No.2, pp. 45-58, 2019, DOI:10.32604/jai.2019.04444

    Abstract The prediction of crop yield is one of the important factor and also challenging, to predict the future crop yield based on various criteria’s. Many advanced technologies are incorporated in the agricultural processes, which enhances the crop yield production efficiency. The process of predicting the crop yield can be done by taking agriculture data, which helps to analyze and make important decisions before and during cultivation. This paper focuses on the prediction of crop yield, where two models of machine learning are developed for this work. One is Modified Convolutional Neural Network (MCNN), and the… More >

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