Coconuts are commonly harvested by judging their maturity based on colour, shape, timeframe, shaking sound, and other growth characteristics of changes as they grow. Currently, solutions involving image-processing techniques have substantial challenges involving the identification of the maturity stages of coconuts. Accordingly, an improved faster region-based convolutional neural network (Faster R–CNN) model is proposed for the detection of two important maturity stages for coconuts in complex backgrounds. The detection of the maturation stages of coconuts for harvesting without human intervention involves challenges because of the complexity of the environment and the similarity between fruits and their backgrounds. Images of coconut and mature coconut bunches were collected from coconut farms. These images were augmented using rotation and colour transformation techniques. These augmented images were used along with original images during model training. The Faster R–CNN algorithm with the ResNet-50 network was used to enhance the detection score of nuts with two major maturity stages. Following training, the detection performance was tested with a dataset that included real-time images as well as Google images. The test results showed that the detection performance achieved using the improved Faster R–CNN model was greater than that for other object detectors such as the single shot detector (SSD) you only look once (YOLO-V3) and Region-based Fully Convolutional Networks (R–FCN). The promising results obtained from this study provided the motivation to develop an application tool for detecting coconut maturity from real-time images on farms.

通常通过根据椰子的颜色，形状，时间范围，摇动声和其他随着生长而变化的生长特征来判断椰子的成熟程度，从而收获椰子。当前，涉及图像处理技术的解决方案具有重大挑战，涉及鉴定椰子的成熟阶段。因此，提出了一种改进的基于快速区域的卷积神经网络（Faster R–CNN）模型，用于检测复杂背景下椰子的两个重要成熟阶段。由于环境的复杂性以及水果与其背景之间的相似性，要在没有人工干预的情况下检测椰子的成熟阶段就面临挑战。从椰子农场收集了椰子和成熟椰子束的图像。使用旋转和颜色转换技术增强了这些图像。在模型训练期间，这些增强图像与原始图像一起使用。使用具有ResNet-50网络的Faster R–CNN算法来提高具有两个主要成熟阶段的坚果的检测得分。训练后，使用包含实时图像和Google图像的数据集测试了检测性能。测试结果表明，使用改进的Faster R–CNN模型实现的检测性能要优于其他对象检测器，例如单发检测器（SSD），您只需看一次（YOLO-V3）和基于区域的完全卷积网络（R–FCN）。