The Faster R-CNN exhibited a regional recognition accuracy of 89%, and the Mask R-CNN exhibited an area recognition accuracy of 81%. A pest and disease identification system was developed in this study. The developed system can be further improved by adopting the proposed reinforcement model construction flow.
Plant pest control is very important, especially in the early stage. If the plant pests and diseases can be identified earlier, farmers can prevent them in ...
Faster R-CNN is adopted to develop a knowledge base system that can automatically identify plant pests and diseases and it is shown that this system can ...
... The authors introduced a system in [28] that focused on identifying diseases in sweet pepper leaves and employed a Faster R-CNN (Region- ...
Therefore, in this study, Faster R-CNN is adopted to develop a knowledge base system that can automatically identify plant pests and diseases. I. INTRODUCTION.
The Pest and Disease Identification in the Growth of Sweet Peppers Using Faster R-CNN and Mask R-CNN.
Early-stage control of plant pests is a crucial topic in modern agriculture. If plant pests and diseases can be identified as early as possible, ...
Faster region-convoluted neural networks and Mask R-CNNs were used to develop a knowledge-based system that can automatically identify plant pests and ...
Sep 20, 2023 · In this study, we propose a concatenated neural network of the extracted features of VGG16 and AlexNet networks to develop a pepper disease classification ...
Pest and Disease Identification in the Growth of Sweet Peppers using Faster R-CNN. Tu-Liang Lin, Hong-Yi Chang, Kai-Hong Chen. Pest and Disease ...