Developing a Dual Vision Harvesting Bot Using ROS

With increasing demand for labor in the orchard harvesting field and limited supply to meet that demand, creating a tool for farmers to meet this need is important. Northwest Nazarene University is developing a robot to help farmers by automating harvesting. This robot uses a dual vision system that allows the robot to detect apples on a tree and pick them using the attached arm. This system utilizes the Robotic Operating System, ROS, to allow each piece of the system to communicate to create a seamless package

A Machine Vision Algorithm Combining Adaptive Segmentation and Shape Analysis for Orange Fruit Detection

 Over the last several years there has been a renewed interest in the automation of harvesting of fruits and vegetables. The two major challenges in the automation of harvesting are the recognition of the fruit and its detachment from the tree. This paper deals with fruit recognition and it presents the development of a machine vision algorithm for the recognition of orange fruits. The algorithm consists of segmentation, region labeling, size filtering, perimeter extraction and perimeter-based detection. In the segmentation of the fruit, the orange was enhanced by using the red chromaticity coefficient which enabled adaptive segmentation under variable outdoor illumination. The algorithm also included detection of fruits which are in clusters by using shape analysis techniques. Evaluation of the algorithm included images taken inside the canopy (varying lighting condition) and on the canopy surface. Results showed that more than 90% of the fruits visually recognized in the images were detected in the 110 images tested with a false detection rate of 4%. The proposed segmentation was able to deal with varying lighting condition and the perimeter-based detection method proved to be effective in detecting fruits in clusters. The development of this algorithm with its capability of detecting fruits in varying lighting condition and occlusion would enhance the overall performance of robotic fruit harvesting

Citrus black spot detection using hyperspectral image analysis

A recently discovered fungal disease called citrus black spot, is threatening the Florida citrus industry.  The fungal disease, which causes cosmetic lesions on the rind of the fruit and can cause a tree to drop its fruit prematurely, could possibly lead to a ban on sales of fresh Florida citrus in other citrus-producing states.  The objective of this research is to develop a multispectral imaging algorithm to detect citrus black spots based on hyperspectral image data.  Hyperspectral images of citrus fruits (Valencias) were collected in the wavelength range of 480 nm to 950 nm.  Five surface conditions were examined, citrus black spot, greasy spot, melanose, wind scar, and normal one.  The first part of the image analysis determined the optimal wavelengths using correlation analysis based on the wavelength ratio (l1/l2) and wavelength difference (l1 - l2).  Four wavelengths were identified, 493 nm, 629 nm, 713 nm, and 781 nm.  In the second part, pattern recognition approaches namely linear discriminant classifier and artificial neural networks were developed using the four selected wavelengths as the input.  Both pattern recognition approaches had an overall accuracy of 92%.  The detection accuracy was improved to 96% by using the NDVI band ratio method of 713 nm and 781 nm.  The multispectral image algorithm developed in this study haspotential to be adopted by a real-time multispectral imaging system for citrus black spot detection.     Keywords: activation energy, effective diffusivity, foam-mat drying, foam characteristics, modeling, Shrim

Evaluation of Different Irrigation Methods for an Apple Orchard Using an Aerial Imaging System

Regular monitoring and assessment of crops is one of the keys to optimal crop production. This research presents the development of a monitoring system called the Crop Monitoring and Assessment Platform (C-MAP). The C-MAP is composed of an image acquisition unit which is an off-the-shelf unmanned aerial vehicle (UAV) equipped with a multispectral camera (near-infrared, green, blue), and an image processing and analysis component. The experimental apple orchard at the Parma Research and Extension Center of the University of Idaho was used as the target for monitoring and evaluation. Five experimental rows of the orchard were randomly treated with five different irrigation methods. An image processing algorithm to detect individual trees was developed to facilitate the analysis of the rows and it was able to detect over 90% of the trees. The image analysis of the experimental rows was based on vegetation indices and results showed that there was a significant difference in the Enhanced Normalized Difference Vegetation Index (ENDVI) among the five different irrigation methods. This demonstrates that the C-MAP has very good potential as a monitoring tool for orchard management

Development of a Visual Servo System for Robotic Fruit Harvesting

One of the challenges in the future of food production, amidst increasing population and decreasing resources, is developing a sustainable food production system. It is anticipated that robotics will play a significant role in maintaining the food production system, specifically in labor-intensive operations. Therefore, the main goal of this project is to develop a robotic fruit harvesting system, initially focused on the harvesting of apples. The robotic harvesting system is composed of a six-degrees-of-freedom (DOF) robotic manipulator, a two-fingered gripper, a color camera, a depth sensor, and a personal computer. This paper details the development and performance of a visual servo system that can be used for fruit harvesting. Initial test evaluations were conducted in an indoor laboratory using plastic fruit and artificial trees. Subsequently, the system was tested outdoors in a commercial fruit orchard. Evaluation parameters included fruit detection performance, response time of the visual servo, and physical time to harvest a fruit. Results of the evaluation showed that the developed visual servo system has the potential to guide the robot for fruit harvesting

Improving the Harvesting Speed of Fruit Harvesting Robot

Automated robotic fruit harvesting has become an exciting new topic in the last decade, with multiple new developments in the subject every year. With the lack of agricultural workers, robotic harvesting has become even more relevant. However, none of the robotic systems that have been created can compare with the speed of a human worker, and for a robotic system to become economically viable, it must be able to compete with them. OrBot, the orchard Picking robot created by the Robotics Vision Lab, has been successful at harvesting at 15s per apple. For this to be commercially applicable, the harvesting speed should be around 10s. Using MATLAB optimization techniques, the harvesting time was reduced to 8.2s

Development of a low-cost multispectral camera for aerial crop monitoring

This paper presents the development of a low-cost multispectral camera that can be mounted on small unmanned aerial vehicles for crop monitoring. The low-cost multispectral camera is a modified commercial grade point-and-shoot camera with a special dual-band filter that can detect narrow red band and near-infrared band. The modified camera was evaluated by flying it over a commercial apple orchard, and its performance was compared with a commercial multispectral camera. Results showed that the modified camera produced a similar Normalized Difference Vegetation Index images as compared with the commercial multispectral camera.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Analysis of Machine Learning Algorithm to Improve Fruit Yield Estimation

Accurately estimating the fruit yield is an extremely important part of precision agriculture. Northwest Nazarene University is developing a mobile application to help farmers estimate the fruit yield of apple trees more accurately and efficiently. Before the development of fruit yield apps, farmers would have to manually count the fruit and take the averages from multiple trees to estimate the fruit yield. The app currently uses an RGB color mask to count the fruit. A new color masking system was created to see if the RGB color model is the most accurate model to use for the purpose of apple counting. Machine learning algorithms were also investigated and prototyped for fruit yield estimation

Fruit Yield Estimation Using Deep Neural Network

One of the tools for precision agriculture is yield monitoring. In this paper, a yield monitoring system using machine vision is developed to estimate fruit yield early in the season. Predicting yield early in the season helps farmers in the marketing of their product and the production logistics. The machine vision system uses a color camera to acquire images of the trees during the blossom period. A deep neural network was developed to recognize and count the blossoms on the tree. There was a high correlation between the blossom count and the number of fruits on the tree which shows the potential of this method

Blossom Counting App

Having the ability to predict crop yield early into the season is crucial to farmers. The current method of predicting crop yield is to count the number of blossoms of one tree and multiplying that value of the number of trees in the orchard. With this method, the farmer is assuming that all trees will produce an equal amount of crop. The flaw of the current method is, each tree will not produce the same amount and therefore could affect the crop yield prediction. With the use of the Blossom Counting App, we can provide a tool for farmers to count blossoms in an efficient way. The Blossom Counting App consists of two folds, the front end, and the back end. The front end is responsible for the user interface and allowing the user to input the image they want to process. The requirement needed for the front end was using the language known as Swift. The back end is responsible for handling the image processing. The requirements needed to build the back end were using the programming language C++ and a library called OpenCV. Currently, the app is still in its development phase attempting to take into account multiple variables that decrease the performance of the app