Immature Green Citrus Research Articles

Object Detection under Natural Illumination Conditions using Superpixels and Local Binary Pattern Feature

This paper proposes a novel object detection method under natural illumination conditions in which a set of features, texture features and Local Binary Pattern (LBP) features, are extracted from the images acquired by a colour camera. The goal of this study is to develop a robust and fast algorithm to detect immature green citrus fruit in individual trees from colour images acquired under natural outdoor conditions. Colour and shape features are used to remove the complex background as much as possible. Since leaves share much more similarities with green citrus fruit in colour and to some extent in the shape, texture features are used for citrus detection. Statistical features, Tamura features and LBP features are used to build the KNN classifier. Experimental results show that the proposed approach provides fairly good object detection performance and confirms an efficient way for outdoor green citrus detection.

Rh6 gene modulates the visual mechanism of host utilization in fruit fly Bactrocera minax.

Vision plays a critical role in host location and oviposition behavior for herbivorous insects. However, the molecular mechanisms underlying visual regulation in host recognition and oviposition site selection in insects remains unknown. The aim of this study was to explore the key visual genes that are linked to the host plant location of the fruit fly, Bactrocera minax. Using a host specialist fruit fly, B. minax, which lays eggs only into immature green citrus fruit, we undertook behavioral, transcriptomic, and RNAi research to identify the molecular basis for host fruit color recognition. In laboratory and field assays we found that adults prefer green over other colors, and this preference is significantly increased in sexually mature over immature flies. Furthermore, we identified that the Rh6 gene, responsible for green spectral sensitivity, has elevated expression in mature flies over immature flies. RNAi suppression of Rh6 eliminated the preference for green, resulting in a significant decrease in the number of eggs laid by B. minax in green unripe citrus. These results show that the Rh6 gene modulates the visual mechanism of host utilization in B. minax, providing a genetic basis for visual host location in a non-model insect herbivore. © 2018 Society of Chemical Industry.

Immature green citrus fruit detection using color and thermal images

Citrus fruit detection is one of the most important and challenging steps in citrus yield mapping. The distinct color differences between the ripe fruit and leaves allowed previously-described imaging-based methods to achieve good results. However, immature green citrus fruit detection, which aims to provide valuable information for citrus yield mapping at earlier stages is much more difficult because the fruit and leaf colors are very similar. This study combines color and thermal images for immature green fruit detections. Experiments identified optimal conditions for thermal imaging. A multimodal imaging platform was built to integrate color and thermal cameras. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color-Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. An increase in recall rate from 78.1% when using only color images to 90.4% after fusing the color and thermal images was obtained at similar precision rates, and an increase in precision rate from 86.6% to 95.5% was obtained at similar recall rates. The fusion of the color and thermal images effectively improved immature green citrus fruit detection.

Correction to: Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images

Correction to: Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images

Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images

The original version of this article unfortunately contained a mistake. The affiliation “China West Normal University” should be removed from the author Dr. Chenglin Wang. The correct affiliation details are given below.

Feature extraction of hyperspectral images for detecting immature green citrus fruit

Feature extraction of hyperspectral images for detecting immature green citrus fruit

Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove

Recognition and detection of green immature citrus fruit more accurately and efficiently in groves under natural illumination conditions provides a promising benefit for growers to plan application of nutrients during the fruit maturing stages and estimate their yield and profit prior to the harvesting period. The goal of this study was to develop a robust and fast algorithm to detect and count immature green citrus fruit in individual trees from colour images acquired with different fruit sizes and under various illumination conditions. Adaptive Red and Blue chromatic map (ARB) was created and combined with the Hue image extracted after histogram equalization (HEH). The sum of absolute transformed difference (SATD), a block-matching method, was applied to detect potential fruit pixels. After OR operation of the results obtained from colour and SATD analysis which kept as many fruit pixels as possible, a kernel support vector machine (SVM) classifier was built with same learning sets used for different classification stages to remove false positives based on five selected texture features. The algorithm was evaluated with a set of testing images, and achieved more than 83% recognition accuracy. The proposed method can provide a more efficient way for green citrus identification in a grove using colour images.

Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images

A fast normalized cross correlation (FNCC) based machine vision algorithm was proposed in this study to develop a method for detecting and counting immature green citrus fruit using outdoor colour images toward the development of an early yield mapping system. As a template matching method, FNCC was used to detect potential fruit areas in the image, which was the very basis for subsequent false positive removal. Multiple features, including colour, shape and texture features, were combined in this algorithm to remove false positives. Circular Hough transform (CHT) was used to detect circles from images after background removal based on colour components. After building disks centred in centroids resulted from both FNCC and CHT, the detection results were merged based on the size and Euclidian distance of the intersection areas of the disks from these two methods. Finally, the number of fruit was determined after false positive removal using texture features. For a validation dataset of 59 images, 84.4 % of the fruits were successfully detected, which indicated the potential of the proposed method toward the development of an early yield mapping system.

Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions

Yield mapping for tree crops by mechanical harvesting requires automatic detection and counting of fruits in tree canopy. However, partial occlusion, shape irregularity, varying illumination, multiple sizes and similarity with the background make fruit identification a very difficult task to achieve. Therefore, immature green citrus-fruit detection within a green canopy is a challenging task due to all the above-mentioned problems. A novel algorithmic technique was used to detect immature green citrus fruit in tree canopy under natural outdoor conditions. Shape analysis and texture classification were two integral parts of the algorithm. Shape analysis was conducted to detect as many fruits as possible. Texture classification by a support vector machine (SVM), Canny edge detection combined with a graph-based connected component algorithm and Hough line detection, were used to remove false positives. Next, keypoints were detected using a scale invariant feature transform (SIFT) algorithm and to further remove false positives. A majority voting scheme was implemented to make the algorithm more robust. The algorithm was able to accurately detect and count 80.4% of citrus fruit in a validation set of images acquired from a citrus grove under natural outdoor conditions. The algorithm could be further improved to provide growers early yield estimation so that growers can manage grove more efficiently on a site-specific basis to increase yield and profit.

Green citrus detection using fast Fourier transform (FFT) leakage

Detection of immature green citrus fruit is important during the early life cycle of citrus fruit. It allows growers to manage citrus groves more efficiently and maximize yields by identifying expected fruit yields well in advance before harvesting. It also helps the growers prepare harvesting equipment and pickers for the harvesting operation. A novel technique was developed for detecting immature green citrus fruit from an outdoor color image and counting number of fruits. This technique is unique in that it is the first known attempt towards exploring it on green citrus fruits. A set of 71 images containing immature green citrus fruit was acquired in an experimental citrus grove at the University of Florida, Gainesville, Florida, USA. An algorithm was developed using a set of 11 training images by calculating the fast Fourier transform leakage values for fruit and leaves. A threshold value was obtained by comparing the percent leakage of fruit and other objects. The algorithm was tested on a set of 60 validation images. The correct total fruit count for a validation set came out to be 120, whereas the actual number of fruit was 146. The overall correct detection rate was 82.2 %. The proposed algorithm can be further improved to help growers manage their grove more efficiently.

Green citrus detection using ‘eigenfruit’, color and circular Gabor texture features under natural outdoor conditions

A machine vision algorithm was developed to detect and count immature green citrus fruits in natural canopies using color images. A total of 96 images were acquired in October 2010 from an experimental citrus grove in the University of Florida, Gainesville, Florida. Thirty-two of the total 96 images were selected randomly and used for training the algorithm, and 64 images were used for validation. Color, circular Gabor texture analysis and a novel ‘eigenfruit’ approach (inspired by the ‘eigenface’ face detection and recognition method) were used for green citrus detection. A shifting sub-window at three different scales was used to scan the entire image for finding the green fruits. Each sub-window was classified three times by eigenfruit approach using intensity component, eigenfruit approach using saturation component, and circular Gabor texture. Majority voting was performed to determine the results of the sub-window classifiers. Blob analysis was performed to merge multiple detections for the same fruit. For the validation set, 75.3% of the actual fruits were successfully detected using the proposed algorithm.