text: Phenotyping is the process of measuring an organism's observable traits. Manual phenotyping of crops is a labor-intensive, time-consuming, costly, and error prone process. Accurate, automated, high-throughput phenotyping can relieve a huge burden in the crop breeding pipeline. In this paper, we propose a scalable, high-throughput approach to automatically count and segment panicles (heads), a key phenotype, from aerial sorghum crop imagery. Our counting approach uses the image density map obtained from dot or region annotation as the target with a novel deep convolutional neural network architecture. We also propose a novel instance segmentation algorithm using the estimated density map, to identify the individual panicles in the presence of occlusion. With real Sorghum aerial images, we obtain a mean absolute error (MAE) of 1.06 for counting which is better than using well-known crowd counting approaches such as CCNN, MCNN and CSRNet models. The instance segmentation model also produces respectable results which will be ultimately useful in reducing the manual annotation workload for future data.
  @cite_48: Recently, a body of computer vision research has studied the task of high-throughput plant phenotyping (measurement of plant attributes). The goal is to more rapidly and more accurately estimate plant properties as compared to conventional manual methods. In this work, we develop a method to measure two primary yield attributes of interest; stalk count and stalk width that are important for many broad-acre annual crops (sorghum, sugarcane, corn, maize for example). Prior work of using convolutional deep neural networks for plant analysis has either focused on object detection or dense image segmentation. In our work, we develop a novel pipeline that accurately extracts both detected object regions and dense semantic segmentation for extracting both stalk counts and stalk width. A ground-robot called the Robotanist is used to deploy a high-resolution stereo imager to capture dense image data of experimental plots of Sorghum plants. We ground-truth validate data extracted using two humans who assess the traits independently and we compare both accuracy and efficiency of human versus robotic measurements. Our method yields R-squared correlation of 0.88 for stalk count and a mean absolute error of 2.77 mm where average stalk width is 14.354 mm. Our approach is 30 times faster for stalk count and 270 times faster for stalk width measurement.
  @cite_29: Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment. With 361 field images collected in four experimental fields across China between 2010 and 2015 and corresponding manually-labelled dotted annotations, a novel Maize Tassels Counting (MTC) dataset is created and will be released with this paper. To alleviate the in-field challenges, a deep convolutional neural network-based approach termed TasselNet is proposed. TasselNet can achieve good adaptability to in-field variations via modelling the local visual characteristics of field images and regressing the local counts of maize tassels. Extensive results on the MTC dataset demonstrate that TasselNet outperforms other state-of-the-art approaches by large margins and achieves the overall best counting performance, with a mean absolute error of 6.6 and a mean squared error of 9.6 averaged over 8 test sequences. TasselNet can achieve robust in-field counting of maize tassels with a relatively high degree of accuracy. Our experimental evaluations also suggest several good practices for practitioners working on maize-tassel-like counting problems. It is worth noting that, though the counting errors have been greatly reduced by TasselNet, in-field counting of maize tassels remains an open and unsolved problem.
  @cite_39: Oil palm trees are important economic crops in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is important information for predicting the yield of palm oil, monitoring the growing situation of palm trees and maximizing their productivity, etc. In this paper, we propose a deep learning based framework for oil palm tree detection and counting using high-resolution remote sensing images for Malaysia. Unlike previous palm tree detection studies, the trees in our study area are more crowded and their crowns often overlap. We use a number of manually interpreted samples to train and optimize the convolutional neural network (CNN), and predict labels for all the samples in an image dataset collected through the sliding window technique. Then, we merge the predicted palm coordinates corresponding to the same palm tree into one palm coordinate and obtain the final palm tree detection results. Based on our proposed method, more than 96 of the oil palm trees in our study area can be detected correctly when compared with the manually interpreted ground truth, and this is higher than the accuracies of the other three tree detection methods used in this study.
  @cite_43: In this paper we address the task of counting crop plants in a field using Convolutional Neural Networks (CNN). The number of plants in an Unmanned Aerial Vehicle (UAV) image of the field is estimated using regression instead of classification. This avoids to need to know (or guess) the maximum expected number of plants. We also describe a method to extract images of sections or “plots” from an orthorectified image of the entire crop field. These images will be used for training and evaluation of the CNN. Our experiments show that we can obtain a Mean Absolute Percentage Error (MAPE) as low as 6.7 with the Inception-v3 CNN architecture.
  @cite_49: This paper describes a fruit counting pipeline based on deep learning that accurately counts fruit in unstructured environments. Obtaining reliable fruit counts is challenging because of variations in appearance due to illumination changes and occlusions from foliage and neighboring fruits. We propose a novel approach that uses deep learning to map from input images to total fruit counts. The pipeline utilizes a custom crowdsourcing platform to quickly label large data sets. A blob detector based on a fully convolutional network extracts candidate regions in the images. A counting algorithm based on a second convolutional network then estimates the number of fruits in each region. Finally, a linear regression model maps that fruit count estimate to a final fruit count. We analyze the performance of the pipeline on two distinct data sets of oranges in daylight, and green apples at night, utilizing human generated labels as ground truth. We also show that the pipeline has a short training time and performs well with a limited data set size. Our method generalizes across both data sets and is able to perform well even on highly occluded fruits that are challenging for human labelers to annotate.
  @cite_15: 
summary: Agronomy : For general plant counting @cite_43 , counting palm trees @cite_39 , plant stalks @cite_48 , fruits @cite_49 @cite_15 and maize tassels @cite_29 . This is a less mainstream application for counting, but a rich domain, that has real impact in people's lives, and this will be the focus of our paper.