Fruit detection system and an end effector for robotic harvesting of Fuji apples

 The challenges in developing a fruit harvesting robot are recognizing the fruit in the foliage and detaching the fruit from the tree without damaging either the fruit or the tree.  The objectives of this study were to develop a real-time fruit detection system using machine vision and a laser ranging sensor and to develop an end effector capable of detaching the fruit in a way similar to manual pick.  The Fuji apple variety was used in this study. In the detection of the fruit, machine vision was combined with a laser ranging sensor.  The machine vision recognized the fruit and the laser ranging sensor determined the distance.  The system detected a single fruit with 100% accuracy in both front and back lighted scenes with ±3 mm accuracy in distance measurement.  To detach the fruit from the tree, an end effector was developed with a peduncle holder and a wrist; the peduncle holder pinches the peduncle of the fruit and the wrist rotates the peduncle holder to detach the fruit.  Field test results of the end effector showed more than 90% success rate in detaching the fruit with average time use of 7.1 seconds.Keywords: apple, end effector, image processing, machine vision, robotic harvesting, Japan Citation: Bulanon D. M., and T. Kataoka.  Fruit detection system and an end effector for robotic harvesting of Fuji apples.  Agric Eng Int: CIGR Journal, 2010, 12(1): 203-210.&nbsp

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

Measuring the dynamic photosynthome

Background: Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes inenvironmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The ‘dynamic’ changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield. Scope: It is known that photosynthesis does not respond in a timely manner, and even a small temporal “mismatch” between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered. Conclusions: There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of ‘phenomics’ which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the ‘photosynthome’. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved

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

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

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

Video Processing for Fruit Yield Prediction

Blossom counting in fruit orchards has been identified as having the potential for estimating final crop yield. This is invaluable information for fruit farmers as they can market their crop earlier as well as prepare for the harvest more effectively. However, capturing and analyzing photos of each tree in the orchard is a time-consuming process. This study uses live video acquisition system attached to an autonomous vehicle to count blossoms as it goes. Both a simple color filtration segmentation and shallow neural network segmentation were used for detection. The results show potential for fruit yield estimation however there is a need to improve the blossom detection for variable lighting condition in the orchard