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Advanced agronomics model with species classification, minimum support price prediction, and profit suggestion using enhanced deep learning strategy

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Abstract

Minimum support price (MSP) is an advisory price signal, which is a component of a huge collection of agricultural policies in parts of India. The agricultural commodities evaluate the derivatives by noticing the climate changes regarding external factors like economic and weather conditions. The severe changes in these factors result in significant price changes. Here, it may experience more cost-efficiency in analyzing the agricultural species. The problem arises because the mixed species in the single pixel increases the pixel size. The high-dimensional problem of input and outputs occurs in the hyper-spectral data. Hence, this research implements a new MSP method in Agronomics or Agriculture through deep learning algorithms. In the species prediction phase, the input remote sensing images are gathered and processed in pre-processing phase using median filtering and contrast limited adaptive histogram equalization. Here, the pre-processed images are fed to feature extraction using the gray-level co-occurrence matrix (GLCM) and spatial feature extraction techniques. Further, the deep feature extraction is analyzed using convolutional neural network (CNN) by considering the input as the pre-processed images and extracting GLCM and spatial features. These deep features are forwarded to the enhanced recurrent neural-long short-term memory (ERN-LSTM), where the parameters of RNN and LSTM are tuned by self-adaptive dingo optimizer (SA-DOX). Finally, the species prediction outcomes are attained by enhanced RNN + LSTM. Secondly, in the MSP prediction phase, the major aim is to predict the MSP based on the species detected. Here, the gathered price data, along with the extracted CNN-based deep features, are processed to select the significant optimal features and are carried out by the same improved DOX. The selected features are given to enhanced RNN + LSTM for predicting the MSP price related to the crop type. Thirdly, the predicted prices are split into four shares. Fourthly, profit suggestion is carried out by training the location and regional crop data, and thus, the enhanced RNN + LSTM model gives the best profitable harvests. Through the experimental results, the accuracy of species classification using SA-DOX-based ERN-LSTM was 10.9, 8.3, 6.85, and 4.7%, accordingly advanced than SVM, LSTM, RNN, and RNN-LSTM. From the given findings, the better accuracy rate of the given designed method is 96.75%. Accordingly, the better sensitivity and precision rates are 95.9 and 95.5%. Finally, this study explores competitive performance through the experimental results relative to the traditional approaches.

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Authors and Affiliations

  1. Computer Science Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India

    S. Visnu Dharsini

  2. Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

    S. Babu

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Visnu Dharsini, S., Babu, S. Advanced agronomics model with species classification, minimum support price prediction, and profit suggestion using enhanced deep learning strategy. Knowl Inf Syst 65, 1243–1285 (2023). https://doi.org/10.1007/s10115-022-01787-1

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