With the goal of automating bloom intensity estimation, a method a novel method for apple flower detection is presented in which a pre-trained convolutional neural network (CNN) is fine-tuned to become specially sensitive to flowers.
Person detection from vehicles has made rapid progress recently with the advent of multiple high-quality datasets of urban and highway driving, yet no large-scale benchmark is available for the same problem in off-road or agricultural environments. Here we present the National Robotics Engineering Center (NREC) Agricultural Person-Detection Dataset to spur research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from Real Time Kinetic (RTK) GPS. We define a benchmark on part of the dataset that combines a total of 76k labeled person images and 19k sampled person-free images. The dataset highlights several key challenges of the domain, including varying environment, substantial occlusion by vegetation, people in motion and in nonstandard poses, and people seen from a variety of distances; metadata are included to allow targeted evaluation of each of these effects.
This dataset consists of four sets of flower images, from three different fruit tree species: apple, peach, and pear, and accompanying ground truth images. This data is provided to support a paper as well as to provide labeled data to the community for the development of new algorithms and models for object detection.
Data from: Data on morphological features of mycosis induced by Colletotrichum nymphaeae and Lecanicillium longisporum on citrus orthezia scale
Symptoms of mycosis induced by two native fungal entomopathogens of the citrus orthezia scale, Praelongorthezia praelonga (Hemiptera: Ortheziidae), an important pest of citrus orchards, are described.
Video data of flowers, fruitlets, and fruit in apple trees during the 2017 growing season at USDA-ARS-AFRS
This record contains videos of apple trees acquired from a ground vehicle throughout the growing season. Research in precision management methods in orchard crops revolve around locating objects of interest, namely flowers, fruitlets, and fruit, autonomously. This dataset is provided so that researchers without access to research plots or mature trees can experiment with the data acquired during the course of an ongoing project on apple flower estimation in images. The trees shown in these videos have a mixture of colors and growth habits. In particular, the four varieties represent one of each of the Lespinasse ideotypes.