Plant Health Prediction Using RGB-D Method and VGG16 Model

Traditional plant disease detection methods rely on human expert visual inspection, which can be time-consuming and subjective. As a result, automated and objective methods for detecting plant health and disease are required. In this study, we offer a unique method for detecting plant health utilising RGB-D pictures and the VGG16 architecture. Plant health detection is a critical component of precision agriculture, which seeks to monitor and manage plants in an efficient and sustainable manner. In terms of precision and efficiency, our method exceeds previous solutions. On the validation set, our model achieves an accuracy of 98.9%, which is much greater than the accuracy of previous approaches. Moreover, in contrast to current approaches, our technique necessitates reduced computational resources and training/prediction time. Finally, utilising RGBD pictures and deep learning algorithms, we suggest a viable solution to the difficulty of plant health identification. Our method can give precise and timely information regarding plant health status, which can help to improve agricultural efficiency and sustainability.