article · Alexandria Engineering Journal
Modern agriculture increasingly incorporates unmanned aerial vehicles and artificial intelligence to address complex farming challenges in precision crop management. A conceptual unmanned aerial vehicle sensing system and classification framework has been designed to identify and monitor crops through a multistage process. The framework can be applied to diverse crop varieties, categorising distinct rice species and identifying weed infestations. Evaluation using three real drone image datasets demonstrated high performance compared to baseline machine learning models such as Naive Bayes, Decision Tree, Bagging, and Random Forest. On the WeedNet dataset, the framework achieved perfect scores of 100 percent across accuracy, precision, recall, and F1-score. For rice seedling detection, it attained 99.5 percent across all metrics, while achieving 97.99 percent accuracy on the rice varieties dataset. These results exceed standard benchmarks, illustrating the viability of automated aerial image analysis for precision crop management.
Managing crops efficiently requires precise monitoring to identify weeds and track plant growth. Applying aerial drone imagery alongside machine learning enables automated identification of crop species and unwanted vegetation with high accuracy. This capability supports precision agriculture, particularly in developing regions, by offering more reliable data to guide crop management interventions without requiring manual field inspection.
The framework targets precision agriculture applications, enabling automated weed detection and rice variety classification from drone imagery. Potential users include agricultural management services, drone software providers, and farm operators seeking automated monitoring tools. Evaluated on real drone image datasets alongside traditional classifiers, the work represents an applied and tested algorithmic model, though it remains framed as a conceptual sensing system requiring software integration for field deployment.
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Utilizing Wireless Sensor Networks (WSNs), Internet of Things (IoTs) sensors, and Unmanned Aerial Vehicles (UAVs), in conjunction with optimization techniques and machine learning algorithms, can present a novel approach to Precision Agricultural (PA) crops. Agriculture has transitioned from traditional legacy systems to incorporate advanced smart technologies. Several complex farming problems are utilized to promote a variety of UAV sensing and Artificial Intelligence (AI) algorithms in PA applications of smart agriculture, especially in developing countries. This paper proposes the design of a conceptual UAV sensing system for crop management using a novel classification framework. UAV-based image datasets can be implemented and expanded to various types of crops. In addition, it can classify various types of rice species and detect weed farms as it consists of a multistage process for identifying and monitoring different crops. The proposed classification framework is evaluated using three different real UAV image datasets compared with Naive Bayes, Decision Tree (DT), Bagging, and Random Forest (RF) techniques. The classification performance metrics obtained are as follows: i) the WeedNet dataset with 100 % F1-score, 100 % recall, 100 % precision, and 100 % accuracy; ii) the Rice Seedling dataset with 99.5 % F1-score, 99.5 % recall, 99.5 % precision, and 99.5 % accuracy; and iii) the Rice Varieties dataset with 97.99 % F1-score, 97.99 % recall, 98.14 % precision, and 97.99 % accuracy. The success rates achieved by the proposed classification framework outperform those of other recent state-of-the-art techniques, demonstrating its efficacy in crop management applications.
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DOI: 10.1016/j.aej.2024.08.076
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