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.

A Multiscale Point-Supervised Network for Counting Maize Tassels in the Wild.

Accurate counting of maize tassels is essential for monitoring crop growth and estimating crop yield. Recently, deep-learning-based object detection methods have been used for this purpose, where plant counts are estimated from the number of bounding boxes detected. However, these methods suffer from 2 issues: (a) The scales of maize tassels vary because of image capture from varying distances and crop growth stage; and (b) tassel areas tend to be affected by occlusions or complex backgrounds, making the detection inefficient.

From Laboratory to Field: Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild.

Plant disease recognition is of vital importance to monitor plant development and predicting crop production. However, due to data degradation caused by different conditions of image acquisition, e.g.

Semi-supervised Learning for Weed and Crop Segmentation Using UAV Imagery.

Weed control has received great attention due to its significant influence on crop yield and food production. Accurate mapping of crop and weed is a prerequisite for the development of an automatic weed management system. In this paper, we propose a weed and crop segmentation method, SemiWeedNet, to accurately identify the weed with varying size in complex environment, where semi-supervised learning is employed to reduce the requirement of a large amount of labelled data.

A Segmentation-Guided Deep Learning Framework for Leaf Counting.

Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e.

Learning Robust Discriminant Subspace Based on Joint L₂,ₚ- and L₂,ₛ-Norm Distance Metrics.

Recently, there are many works on discriminant analysis, which promote the robustness of models against outliers by using L- or L-norm as the distance metric. However, both of their robustness and discriminant power are limited. In this article, we present a new robust discriminant subspace (RDS) learning method for feature extraction, with an objective function formulated in a different form.