Department of Electronics and Electrical Engineering, Universiti Kuala Lumpur British Malaysian Institute (UniKL BMI), Batu 8, Jalan Sungai Pusu, Gombak 53100, Malaysia.
National Centre for Big Data and Cloud Computing, Ziauddin University, Karachi 74600, Pakistan.
Sensors (Basel). 2022 Nov 7;22(21):8567. doi: 10.3390/s22218567.
Rice is one of the vital foods consumed in most countries throughout the world. To estimate the yield, crop counting is used to indicate improper growth, identification of loam land, and control of weeds. It is becoming necessary to grow crops healthy, precisely, and proficiently as the demand increases for food supplies. Traditional counting methods have numerous disadvantages, such as long delay times and high sensitivity, and they are easily disturbed by noise. In this research, the detection and counting of rice plants using an unmanned aerial vehicle (UAV) and aerial images with a geographic information system (GIS) are used. The technique is implemented in the area of forty acres of rice crop in Tando Adam, Sindh, Pakistan. To validate the performance of the proposed system, the obtained results are compared with the standard plant count techniques as well as approved by the agronomist after testing soil and monitoring the rice crop count in each acre of land of rice crops. From the results, it is found that the proposed system is precise and detects rice crops accurately, differentiates from other objects, and estimates the soil health based on plant counting data; however, in the case of clusters, the counting is performed in semi-automated mode.
水稻是世界上大多数国家的主要粮食作物之一。为了估算产量,作物计数用于指示生长不良、识别壤土和控制杂草。随着对粮食供应的需求增加,有必要健康、精确和高效地种植作物。传统的计数方法存在许多缺点,例如延迟时间长、灵敏度高,并且容易受到噪声干扰。在这项研究中,使用无人机(UAV)和带有地理信息系统(GIS)的航空图像来检测和计数水稻植株。该技术在巴基斯坦信德省坦多·亚当的四十英亩水稻作物区实施。为了验证所提出系统的性能,将获得的结果与标准植物计数技术进行比较,并在对土壤进行测试以及监测每英亩水稻作物的计数之后,由农艺师批准。从结果中可以发现,所提出的系统精确且能准确检测水稻作物,能与其他物体区分开来,并根据植物计数数据估算土壤健康状况;但是,在存在集群的情况下,计数是在半自动模式下进行的。