The Deep Learning-Crop Platform (DL-CRoP): For Species-Level Identification and Nutrient Status of Agricultural Crops.

Autor:	Urfan M; Crop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India., Rajput P; Crop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India., Mahajan P; Department of Computer Science & Engineering, Central University of Jammu, Jammu 181143, India., Sharma S; Crop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India., Hakla HR; Crop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India., Kour V; Department of Electronics, University of Jammu, Jammu 180006, India., Khajuria B; Crop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India., Chowdhary R; Crop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India., Lehana PK; Department of Electronics, University of Jammu, Jammu 180006, India., Karlupia N; Department of Computer Science & IT, University of Jammu, Jammu 180006, India., Abrol P; Department of Computer Science & IT, University of Jammu, Jammu 180006, India., Tran LSP; Institute of Genomics for Crop Abiotic Stress Tolerance, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA., Choudhary SP; Crop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India.
Jazyk:	angličtina
Zdroj:	Research (Washington, D.C.) [Research (Wash D C)] 2024 Oct 04; Vol. 7, pp. 0491. Date of Electronic Publication: 2024 Oct 04 (Print Publication: 2024).
DOI:	10.34133/research.0491
Abstrakt:	Precise and timely detection of a crop's nutrient requirement will play a crucial role in assuring optimum plant growth and crop yield. The present study introduces a reliable deep learning platform called "Deep Learning-Crop Platform" (DL-CRoP) for the identification of some commercially grown plants and their nutrient requirements using leaf, stem, and root images using a convolutional neural network (CNN). It extracts intrinsic feature patterns through hierarchical mapping and provides remarkable outcomes in identification tasks. The DL-CRoP platform is trained on the plant image dataset, namely, Jammu University-Botany Image Database (JU-BID), available at https://github.com/urfanbutt. The findings demonstrate implementation of DL-CRoP-cases A (uses shoot images) and B (uses leaf images) for species identification for Solanum lycopersicum (tomato), Vigna radiata (Vigna), and Zea mays (maize), and cases C (uses leaf images) and D (uses root images) for diagnosis of nitrogen deficiency in maize. The platform achieved a higher rate of accuracy at 80-20, 70-30, and 60-40 splits for all the case studies, compared with established algorithms such as random forest, K-nearest neighbor, support vector machine, AdaBoost, and naïve Bayes. It provides a higher accuracy rate in classification parameters like recall, precision, and F1 score for cases A (90.45%), B (100%), and C (93.21), while a medium-level accuracy of 68.54% for case D. To further improve the accuracy of the platform in case study C, the CNN was modified including a multi-head attention (MHA) block. It resulted in the enhancement of the accuracy of classifying the nitrogen deficiency above 95%. The platform could play an important role in evaluating the health status of crop plants along with a role in precise identification of species. It may be used as a better module for precision crop cultivation under limited nutrient conditions. Competing Interests: Competing interests: The authors declare that they have no competing interests. (Copyright © 2024 Mohammad Urfan et al.)
Databáze:	MEDLINE
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