Toward Precision Agriculture: Integrating Machine Learning Techniques for Smart Farming Systems

• Published in: IEEE access 2024, Vol.12, p.172910-172922
• Main Authors: Anwar Omer, Batool, Mabrouk Morsey, Mohamed, Hegazy, Islam, Taha Fayed, Zaki, El-Arif, Taha
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural data analytics; Agriculture; Agrochemicals; Artificial intelligence; Classification; Cluster analysis; Clustering; Crops; Data acquisition; Data analysis; Datasets; Farming; Feature extraction; K-means; Machine learning; Measurement; MobileNet; Resource utilization; Rice; Safety measures; Seeds; Smart agriculture; smart mapping; Support vector machines; Training; Vector quantization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3480868

Smart agriculture holds a transformative potential, driven by cutting-edge technologies, in revolutionizing the global food production sector and enhancing food safety measures. This is achieved by leveraging the capabilities of smart maps, artificial intelligence, and agricultural data analytics. Hence, farmers can benefit from it as it allows them to streamline resource utilization. This, in turn, leads to reduced inputs of fertilizers, labor, seeds, and water, while simultaneously amplifying crops. Therefore, this paper introduces a comprehensive system for advancing smart map agriculture, comprising five pivotal stages designed to yield accurate and meaningful results. Notable progress is achieved through meticulous data acquisition by employing the rice seedling and WeedNet datasets, coupled with feature extraction utilizing the MobileNet architecture. The classification stage is critical for distinguishing between rice and land in the first dataset and between crops and weeds in the second one. It further enhances the system's efficacy. Additionally, segmentation using K-means clustering to detect rice or crop is employed. Notably, the current approach exhibits promising performance levels. It results in 99.7%, 100%, 99.4%, and 99.72% for accuracy, precision, recall, and F1_score, respectively, in the rice seedling dataset, as well as achieving 98.72%, 97.3%, 100%, and 98.63% in the WeedNet dataset. The primary contribution of this study lies in the integration of MobileNet and SVM architectures for feature extraction and classification in the agriculture field. It also presents the innovative use of K-means clustering for segmenting agricultural fields.