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MCYP-DeepNet: Nutrition and Temperature Based Season Wise Multi Crop Yield Prediction Using DeepNet 230 Classifier

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Abstract

A vital source of nutrition and a major contributor to the nation’s economic expansion is agriculture. Due to numerous complex factors such as environment, humidity, soil nutrients, and soil moisture, multi crop yield forecasting was very challenging. Because crop prediction is a complicated process, improving performance is challenging. To address these problems, an advance deep learning model was developed to predict crop types and its yields in a particular soil. A real time data were created, which contain various parameters such as soil nutrition’s, weather, data, seasons and temperature. The created dataset is pre-processed using outlier detection as well as normalization because it contains unwanted rows and columns. After that, the pre-processed data were given as input for the DeepNet230 model to analyze the input parameters like soil nutrition and temperature to predict the multi crop type and its yield quantity. DeepNet230 have the capacity of automatic feature learning and rapid unstructured process, so it provides an efficient prediction performance of crop yield and its types. The performance analysis of crop prediction for the proposed model are 93.7% accuracy, 93.4% recall, 92.8% precision and 92.9% specificity. Then, the performance of yield prediction for the identified crops are 95.5% accuracy, 91.6% recall, 93% precision and 94.2% specificity. In addition, the developed method was compared with several opposing methods for validation. The observed results show that the suggested method performed significantly better in real time due to its improved predictive capabilities.

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Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Authors and Affiliations

  1. Yeshwantrao Chavan College of Engineering, 441110, Nagpur, Maharashtra, India

    Nilesh U Sambhe

  2. Saraswati College of Engineering, 410210, Navi Mumbai, Maharashtra, India

    Manjusha Deshmukh

  3. Panimalar Engineering College, 600123, Chennai, Tamil Nadu, India

    L. Ashok Kumar

  4. Bharati Vidyapeeth College of Engineering, 400614, Navi Mumbai, Maharashtra, India

    Sandip Chavan

  5. Vasantdada Patil Pratishthan’s College of Engineering, University of Mumbai, 400022, Mumbai, Maharashtra, India

    Nidhi P. Ranjan

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  1. Nilesh U Sambhe
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  2. Manjusha Deshmukh
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  3. L. Ashok Kumar
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  4. Sandip Chavan
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The corresponding author claims the major contribution of the paper including formulation, analysis and editing. The co-authors provides guidance to verify the analysis result and manuscript editing.

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Correspondence to Nilesh U Sambhe.

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Nilesh U Sambhe, Deshmukh, M., Kumar, L.A. et al. MCYP-DeepNet: Nutrition and Temperature Based Season Wise Multi Crop Yield Prediction Using DeepNet 230 Classifier. Opt. Mem. Neural Networks 33, 236–253 (2024). https://doi.org/10.3103/S1060992X24700115

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