Open Access

ARTICLE

Automatic Crop Expert System Using Improved LSTM with Attention Block

Shahbaz Sikandar¹, Rabbia Mahum¹, Suliman Aladhadh^2,*

1 Computer Science Department, University of Engineering and Technology, Taxila, Pakistan
2 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia

* Corresponding Author: Suliman Aladhadh. Email:

Computer Systems Science and Engineering 2023, 47(2), 2007-2025. https://doi.org/10.32604/csse.2023.037723

Received 14 November 2022; Accepted 18 April 2023; Issue published 28 July 2023

Abstract

Agriculture plays an important role in the economy of any country. Approximately half of the population of developing countries is directly or indirectly connected to the agriculture field. Many farmers do not choose the right crop for cultivation depending on their soil type, crop type, and climatic requirements like rainfall. This wrong decision of crop selection directly affects the production of the crops which leads to yield and economic loss in the country. Many parameters should be observed such as soil characteristics, type of crop, and environmental factors for the cultivation of the right crop. Manual decision-making is time-taking and requires extensive experience. Therefore, there should be an automated system for the right crop recommendation to reduce human efforts and loss. An automated crop recommender system should take these parameters as input and suggest the farmer’s right crop. Therefore, in this paper, a long short-term memory Network with an attention block has been proposed. The proposed model contains 27 layers, the first of which is a feature input layer. There exist 25 hidden layers between them, and an output layer completes the structure. Through these levels, the proposed model enables a successful recommendation of the crop. Additionally, the dropout layer’s regularization properties aids in reduction of overfitting of the model. In this paper, a customized novel long short-term memory (LSTM) model is proposed with a residual attention block that recommends the right crop to farmers. Evaluation metrics used for the proposed model include f1-score, recall, precision, and accuracy attaining values as 95.69%, 96.56%, 96.9%, and 97.26% respectively.

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Keywords

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