Reza Ehsani

A Laser-Scanning System for Quantification of Tree-Geometric Characteristics

Tree-geometric characteristics such as tree canopy height, width, and volume are required for an accurate estimation of citrus yield early in the season and variable rate application of fertilizer and pesticides on each tree. A laser-based measurement system consisted of a laser scanner, a GPS, an inertial sensor, a serial-to-USB adapter, and a computer, with corresponding algorithms for measurement of the tree characteristics were developed and mounted on a utility vehicle for tree scanning. The performance of the system was tested on an orange tree in a citrus orchard.

Detection of multi-tomato leaf diseases ( late blight, target and bacterial spots ) in different stages by using a spectral-based sensor

Several diseases have threatened tomato production in Florida, resulting in large losses, especially in fresh markets. In this study, a high-resolution portable spectral sensor was used to investigate the feasibility of detecting multi-diseased tomato leaves in different stages, including early or asymptomatic stages. One healthy leaf and three diseased tomato leaves (late blight, target and bacterial spots) were defined into four stages (healthy, asymptomatic, early stage and late stage) and collected from a field. Fifty-seven spectral vegetation indices (SVIs) were calculated in accordance with methods published in previous studies and established in this study. Principal component analysis was conducted to evaluate SVIs. Results revealed six principal components (PCs) whose eigenvalues were greater than 1. SVIs with weight coefficients ranking from 1 to 30 in each selected PC were applied to a K-nearest neighbour for classification. Amongst the examined leaves, the healthy ones had the highest accuracy (100%) and the lowest error rate (0) because of their uniform tissues. Late stage leaves could be distinguished more easily than the two other disease categories caused by similar symptoms on the multi-diseased leaves. Further work may incorporate the proposed technique into an image system that can be operated to monitor multi-diseased tomato plants in fields.

Experimental Study of Vibrational Acceleration Spread and Comparison Using Three Citrus Canopy Shaker Shaking Tines

The goal of this article is to experimentally study how the vibrational acceleration spreads along the branch shaken by PVC tine, steel tine, and nylon tine for citrus canopy shaking harvesting and to compare the difference. PVC tine and steel tine have potential to be used as shaking rod for citrus canopy shaking harvesting. Nylon tine is a commonly used shaking rod. A tractor-mounted canopy shaker was developed to do the trial. The shaking frequency was set at 2.5 and 5u2009Hz. Experimental results showed that the vibrational acceleration at the shaking spot is not the highest. Spreading from shaking spot to the stem, it increases evidently. When spreading from stems of the outside subbranch to stems of the nearest inside subbranch, its average decrease percentage is 42%. The overall vibrational acceleration of shaking at 5u2009Hz is 1.85 times as high as shaking at 2.5u2009Hz. The overall vibrational acceleration exerted by straight PVC tine and steel tine is 1.77 and 1.97 times as high as that exerted by straight nylon tine, respectively. It is indicated that replacing nylon tine with steel tine or PVC tine helps remove the fruits inside the canopy. Replacing with steel tine is more effective than with PVC tine.

Real-Time Synchronization of Two Vehicles in Parallel

For harvesting citrus in Florida, two self-propelled citrus canopy shake and catch harvesting machines work in pair (mater and slave), one on each side of the row of citrus trees, necessitating synchronization of their forward movement. Unreliable synchronization causes inefficiency in the catching system which degrades the capability of the citrus harvesting system.

Identifying immature and mature pomelo fruits in trees by elliptical model fitting in the Cr–Cb color space

In precision agriculture, identification of fruit in trees is important. Furthermore, it is also essential for estimating the yield, targeting the exact location for a harvesting robot and selectively harvesting the fruit. An elliptical boundary model-based machine vision algorithm was developed to identify immature and mature pomelo fruit in trees. In the proposed solution, the images were converted from RGB space to Y′CbCr space. Then, ordinary least-squares (OLS) was introduced in fitting implicit second order polynomials of elliptical boundary models in the Cr–Cb color space for segmenting immature green fruits, mature green, green partial white, green partial yellow and green partial red fruits. Those elliptical boundary models along with area opening mathematical morphology and diameter thresholding were applied in the identification procedure. The algorithm was tested on a set of 200 validation images acquired under natural illumination conditions. The results of the validation test showed that the total correct identification rate was 93.5%. The total false positive, missed rate, repeated rate and merged rate were equal to 8.2, 6.5, 10.2 and 10.6%, respectively. The proposed method performed better in detecting mature fruit, the color of which is different from green, than in detecting immature green fruit. On average, the segmenting time for 640u2009×u2009480 and 1280u2009×u2009960 images were 0.134 and 0.200xa0s, respectively and the total identification time for 640u2009×u2009480 and 1280u2009×u2009960 images were 0.240 and 0.362xa0s, respectively.

Detection of citrus fruit and tree trunks in natural environments using a multi-elliptical boundary model

Abstract Intelligent detection is a key technology in precision agriculture. As items of different color cluster in different non-overlapping elliptical regions, this study proposed a method for constructing a multi-elliptical boundary model in Cr-Cb co-ordinates to detect citrus fruit and tree trunks in natural light environments. Here, the detected citrus variety was spring sweet tangerine, and the parameters of the elliptical boundary models for detecting these fruit and tree trunks solved by color-space transformation and ellipse fitting. A series of image detection experiments were performed to evaluate the method’s performance. The experimental results showed that the correct and false positive percentages in fruit identification from images were 90.8 and 11.2%, respectively. The number of correctly detected images in distinguishing tree trunks from background was 44 of 50 images.

Simulation study on the effects of tine-shaking frequency and penetrating depth on fruit detachment for citrus canopy-shaker harvesting

Abstract The overall low detachment percentage in citrus mechanical harvesting is a concerning problem. Studies of the effects of tine-shaking frequency and penetrating depth on fruit detachment for citrus canopy-shaker harvesting have not been reported to date. The objective of this study was to examine how tine-shaking frequency and penetrating depth affect fruit detachment based on simulation and pertinent field experiments for a citrus canopy shaker that inserts a row of shaking tines into the tree canopy. According to evaluation of the branch/stem elasticity, density, and fruit detachment force, a cantilevered limb model, including a periodic shaking force, was constructed to simulate the shaking process in citrus canopy shaking. Simulation results demonstrated a positive correlation between the shaking frequency and maximum stress at the fruit end of the stem, and a 5u202fHz shaking frequency found to be sufficient for fruit removal. It was also observed that the penetrating depth ensured that, when shaking spot was close to the junction of the limb and stem, the maximum stress increased at the fruit end of the stem. Field trial results agreed with the simulation results, with both simulation and experiments indicating highly significant effects ( p

Immature green citrus fruit detection using color and thermal images

Abstract 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.