CountShoots: Automatic Detection and Counting of Slash Pine New Shoots Using UAV Imagery

One to All: Toward a Unified Model for Counting Cereal Crop Heads Based on Few-Shot Learning

Accurate counting of cereals crops, e.g., maize, rice, sorghum, and wheat, is crucial for estimating grain production and ensuring food security. However, existing methods for counting cereal crops focus predominantly on building models for specific crop head; thus, they lack generalizability to different crop varieties. This paper presents Counting Heads of Cereal Crops Net (CHCNet), which is a unified model designed for counting multiple cereal crop heads by few-shot learning, which effectively reduces labeling costs. Specifically, a refined vision encoder is developed to enhance feature embedding, where a foundation model, namely, the segment anything model (SAM), is employed to emphasize the marked crop heads while mitigating complex background effects. Furthermore, a multiscale feature interaction module is proposed for integrating a similarity metric to facilitate automatic learning of crop-specific features across varying scales, which enhances the ability to describe crop heads of various sizes and shapes. The CHCNet model adopts a 2-stage training procedure. The initial stage focuses on latent feature mining to capture common feature representations of cereal crops. In the subsequent stage, inference is performed without additional training, by extracting domain-specific features of the target crop from selected exemplars to accomplish the counting task. In extensive experiments on 6 diverse crop datasets captured from ground cameras and drones, CHCNet substantially outperformed state-of-the-art counting methods in terms of cross-crop generalization ability, achieving mean absolute errors (MAEs) of 9.96 and 9.38 for maize, 13.94 for sorghum, 7.94 for rice, and 15.62 for mixed crops. A user-friendly interactive demo is available at http://cerealcropnet.com/, where researchers are invited to personally evaluate the proposed CHCNet. The source code for implementing CHCNet is available at https://github.com/Small-flyguy/CHCNet.

MTSC-Net: A Semi-Supervised Counting Network for Estimating the Number of Slash pine New Shoots

The new shoot density of slash pine serves as a vital indicator for assessing its growth and photosynthetic capacity, while the number of new shoots offers an intuitive reflection of this density. With deep learning methods becoming increasingly popular, automated counting of new shoots has greatly improved in recent years but is still limited by tedious and expensive data collection and labeling. To resolve these issues, this paper proposes a semi-supervised counting network (MTSC-Net) for estimating the number of slash pine new shoots. First, based on the mean-teacher framework, we introduce the improved VGG19 to extract multiscale new shoot features. Second, to connect local new shoot feature information with global channel features, attention feature fusion module is introduced to achieve effective feature fusion. Finally, the new shoot density map and density probability distribution are processed in a fine-grained manner through multiscale dilated convolution of the regression head and classification head. In addition, a masked image modeling strategy is introduced to encourage the contextual understanding of global new shoot features and improve the counting performance. The experimental results show that MTSC-Net outperforms other semi-supervised counting models with labeled percentages ranging from 5% to 50%. When the labeled percentage is 5%, the mean absolute error and root mean square error are 17.71 and 25.49, respectively. These findings demonstrate that our work can be used as an efficient semi-supervised counting method to provide automated support for tree breeding and genetic utilization.

AgCNER, the First Large-Scale Chinese Named Entity Recognition Dataset for Agricultural Diseases and Pests

Named entity recognition is a fundamental subtask for knowledge graph construction and question-answering in the agricultural diseases and pests field. Although several works have been done, the scarcity of the Chinese annotated dataset has restricted the development of agricultural diseases and pests named entity recognition(ADP-NER). To address the issues, a large-scale corpus for the Chinese ADP-NER task named AgCNER was first annotated. It mainly contains 13 categories, 206,992 entities, and 66,553 samples with 3,909,293 characters. Compared with other datasets, AgCNER maintains the best performance in terms of the number of categories, entities, samples, and characters. Moreover, this is the first publicly available corpus for the agricultural field. In addition, the agricultural language model AgBERT is also fine-tuned and released. Finally, the comprehensive experimental results showed that BiLSTM-CRF achieved F1-score of 93.58%, which would be further improved to 94.14% using BERT. The analysis from multiple aspects has verified the rationality of AgCNER and the effectiveness of AgBERT. The annotated corpus and fine-tuned language model are publicly available at https://doi.org/XXX and https://github.com/guojson/AgCNER.git .

Semi-supervised Counting of Grape Berries in the Field Based on Density Mutual Exclusion

Automated counting of grape berries has become one of the most important tasks in grape yield prediction. However, dense distribution of berries and the severe occlusion between berries bring great challenges to counting algorithm based on deep learning. The collection of data required for model training is also a tedious and expensive work. To address these issues and cost-effectively count grape berries, a semi-supervised counting of grape berries in the field based on density mutual exclusion (CDMENet) is proposed. The algorithm uses VGG16 as the backbone to extract image features. Auxiliary tasks based on density mutual exclusion are introduced. The tasks exploit the spatial distribution pattern of grape berries in density levels to make full use of unlabeled data. In addition, a density difference loss is designed. The feature representation is enhanced by amplifying the difference of features between different density levels. The experimental results on the field grape berry dataset show that CDMENet achieves less counting errors. Compared with the state of the arts, coefficient of determination (R2) is improved by 6.10%, and mean absolute error and root mean square error are reduced by 49.36% and 54.08%, respectively. The code is available at https://github.com/youth-tang/CDMENet-main.