Apple Flower Detection Using Deep Convolutional Networks

To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate bloom intensity, but their overall performance is still far from satisfactory even in relatively controlled environments. With the goal of devising a technique for flower identification which is robust to clutter and to changes in illumination, this paper presents a method in which a pre-trained convolutional neural network is fine-tuned to become specially sensitive to flowers. Experimental results on a challenging dataset demonstrate that our method significantly outperforms three approaches that represent the state of the art in flower detection, with recall and precision rates higher than 90%. Moreover, a performance assessment on three additional datasets previously unseen by the network, which consist of different flower species and were acquired under different conditions, reveals that the proposed method highly surpasses baseline approaches in terms of generalization capability

Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network

In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance its sensitivity to flowers, we fine-tune this network using a single dataset of apple flower images. Since CNNs tend to produce coarse segmentations, we employ a refinement method to better distinguish between individual flower instances. Without any preprocessing or dataset-specific training, experimental results on images of apple, peach, and pear flowers, acquired under different conditions demonstrate the robustness and broad applicability of our method

Machine Vision System for Early-stage Apple Flowers and Flower Clusters Detection for Precision Thinning and Pollination

Early-stage identification of fruit flowers that are in both opened and unopened condition in an orchard environment is significant information to perform crop load management operations such as flower thinning and pollination using automated and robotic platforms. These operations are important in tree-fruit agriculture to enhance fruit quality, manage crop load, and enhance the overall profit. The recent development in agricultural automation suggests that this can be done using robotics which includes machine vision technology. In this article, we proposed a vision system that detects early-stage flowers in an unstructured orchard environment using YOLOv5 object detection algorithm. For the robotics implementation, the position of a cluster of the flower blossom is important to navigate the robot and the end effector. The centroid of individual flowers (both open and unopen) was identified and associated with flower clusters via K-means clustering. The accuracy of the opened and unopened flower detection is achieved up to mAP of 81.9% in commercial orchard images

Detection and localization of cotton based on deep neural networks

Cotton detection is the localization and identification of the cotton in an image. It has a wide application in robot harvesting.  Various modern algorithms use deep learning techniques for detection of fruits/flowers. As per the survey, the topics travelled include numerous algorithms used, and accuracy obtained on using those algorithms on their data set. The limitations and the advantages in each paper, are also discussed. This paper focuses on various fruit detection algorithms- the Faster RCNN, the RCNN, YOLO. Ultimately, a rigorous survey of many papers related to the detection of objects like fruits/flowers, analysis of the assets and faintness of each paper leads us to understanding the techniques and purpose of algorithms. &nbsp

Blooming charge assessment in apple orchards for automatic thinning activities

Summary This work aims to develop an automatic system capable of providing objective information about the bloom charge in an apple orchard in order to manage flower-thinning activities. The article presents and discusses the use of a mobile lab (ByeLab) equipped with several optical sensors to carry out a site-specific bloom charge assessment in apple trees. The data collected by the sensors were processed by a specific algorithm implemented in MatLab®. Investigations of the flower reflectance signature indicated that the Normalized Difference Vegetation Index (NDVI) is the most suitable parameter to distinguish leaves from flowers. Pure flowers produce NDVI values slightly negative or at least very near to 0. Despite the homogeneous behavior of the NDVI flower response, OptRx™ sensors, which provide an average assessment of an area, were not able to highlight a significant correlation between the number of flowers and the NDVI values. In the future, further studies will be conducted to assess if other techniques based on image analyses can provide better and more sensitive results regarding the bloom charge assessment. Such results could then be used as a reference in automating machines for thinning operations according to a site-specific approach

Automatic Segmentation of Trees in Dynamic Outdoor Environments

Segmentation in dynamic outdoor environments can be difficult when the illumination levels and other aspects of the scene cannot be controlled. Specifically in orchard and vineyard automation contexts, a background material is often used to shield a camera\u27s field of view from other rows of crops. In this paper, we describe a method that uses superpixels to determine low texture regions of the image that correspond to the background material, and then show how this information can be integrated with the color distribution of the image to compute optimal segmentation parameters to segment objects of interest. Quantitative and qualitative experiments demonstrate the suitability of this approach for dynamic outdoor environments, specifically for tree reconstruction and apple flower detection application

The Use of Agricultural Robots in Orchard Management

Book chapter that summarizes recent research on agricultural robotics in orchard management, including Robotic pruning, Robotic thinning, Robotic spraying, Robotic harvesting, Robotic fruit transportation, and future trends.Comment: 22 page

Spoilage Detection in Raspberry Fruit Based on Spectral Imaging Using Convolutional Neural Networks

Effective spoilage detection of perishable food items like fruits and vegetables is essential for retailers who stock and sell large quantities of these items. This research is aimed at developing a non-destructive, rapid and accurate method which is based on Spectral Imaging (SI) used in tandem with Convolutional Neural Network (CNN) to predict whether the fruit is fresh or rotten. The study also aims to determine the number of days before which the fruit rots. This research employs a primary, quantitative and inductive methods to investigate the Deep Learning based approach to detect fruit spoilage. Raspberry fruit in particular has been chosen for the experiment. Baskets of raspberries from three different stores were bought and stored in the refrigerator at four-degree Celsius. Images of these baskets was captured on a daily basis using an RGB digital camera until all the baskets of fruits were rotten. The study employs a Supervised learning-based classification approach where-by the data is labelled based on the physical appearance of fruits in the basket. The results show that a Spectral imaging technique used along with a CNN yields a good accuracy of 86% with the F1 score of 0.82 to classify the fruits as Good or Bad but does not fare well in estimating the number of days before the fruit actually rots. The ability of CNN to process and identify patterns in a SI to detect spoilage in fruits would help fruit retail operators to optimize their business chain

Monitoring costs of result-based payments for biodiversity conservation: Will UAV-based remote sensing be the game-changer?

Paying landowners for conservation results rather than paying for the measures intended to provide such results is a promising approach for biodiversity conservation. However, a key roadblock for the widespread implementation of such result-based payment schemes are the frequent difficulties to monitor target species for whose presence a landowner is supposed to receive a remuneration. Until recently, the only conceivable monitoring approach would be conventional monitoring techniques, by which qualified experts investigate the presence of target species on-site. With the rise of remote sensing technologies, in particular increased capabilities and decreased costs of unmanned aerial vehicles (UAVs), technological monitoring opportunities enter the scene. We analyse the costs of monitoring an ecological target of a hypothetical result-based payments scheme and compare the monitoring cost between conventional monitoring and UAV-assisted monitoring. We identify the underlying cost structure and cost components of both monitoring approaches and use a scenario analysis to identify the influence of factors like UAV and analysis costs, area size, and monitoring frequency. We find that although conventional monitoring is the least-cost monitoring approach today, future cost developments are likely to render UAV-assisted monitoring more cost-effective

Ag-IoT for crop and environment monitoring: Past, present, and future

CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task. OBJECTIVE: Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, transmission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring. METHODS: It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms