Feature extraction for plant growth estimation
Researchers have developed two novel feature extraction methods for estimating plant growth stages, crucial for optimizing resource use in precision agriculture. One method employs Gabor filters and morphological operations, while the other leverages pre-trained convolutional neural networks (CNNs) via transfer learning. Tests on canola and radish datasets showed that CNN features achieved higher accuracy and speed, with the best system reaching 98.4% accuracy in 0.08 seconds. AI
IMPACT Improves efficiency in precision agriculture by enabling more accurate real-time monitoring of crop development.