Gokkus Goksel, Keten Gokkus Mualla
Electrical and Electronics Engineering, Nevsehir Haci Bektas Veli University, Nevsehir, 50300, Nevsehir, Turkey.
Biosystems Engineering, Nevsehir Haci Bektas Veli University, Nevsehir, 50300, Nevsehir, Turkey.
Heliyon. 2024 Jul 5;10(14):e34149. doi: 10.1016/j.heliyon.2024.e34149. eCollection 2024 Jul 30.
Leaf area is one of the important parameters for plant canopy development. It is used as an indicator closely related to plant growth in several studies on plant production. However, most leaf area meters used today are costly and rely on human observations. This situation may be limiting for researchers in terms of having proper leaf area measuring devices. The reliance on human-focused measurements leads to human errors. Digital scanners and cameras, digital image processing-based estimation methods, paper weighing, grid counting, regression equations, width and height correlation models, planimeters, laser optics, and handheld scanners can be used to determine leaf area. However, some of these methods are expensive and unnecessary for simple studies. Therefore, this study aims to design and implement an embedded system with a simpler, cheaper alternative to the currently used methods and devices, minimizing human errors. The proposed embedded system serves as a tool for measuring leaf area using a photovoltaic panel (PV) and an Adaptive Neuro-Fuzzy Inference System (ANFIS). In the study, geometric shapes with known areas are used as the learning data, and real plant leaves with known areas are used in the testing process. As a result, the prediction made by ANFIS is observed to have an accuracy of R = 0.99.
叶面积是植物冠层发育的重要参数之一。在多项关于植物生产的研究中，它被用作与植物生长密切相关的指标。然而，当今使用的大多数叶面积仪成本高昂且依赖人工观测。就拥有合适的叶面积测量设备而言，这种情况可能会限制研究人员。对以人为中心的测量的依赖会导致人为误差。数字扫描仪和相机、基于数字图像处理的估计方法、纸张称重、网格计数、回归方程、宽度和高度相关模型、求积仪、激光光学仪器以及手持式扫描仪都可用于确定叶面积。然而，其中一些方法对于简单研究来说既昂贵又不必要。因此，本研究旨在设计并实现一种嵌入式系统，为当前使用的方法和设备提供一种更简单、更便宜的替代方案，将人为误差降至最低。所提出的嵌入式系统用作一种使用光伏板（PV）和自适应神经模糊推理系统（ANFIS）测量叶面积的工具。在该研究中，将已知面积的几何形状用作学习数据，并在测试过程中使用已知面积的真实植物叶片。结果，观察到ANFIS做出的预测准确率为R = 0.99。
