Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging

Stajnko, D.; Lakota, M.; Hočevar, Marko

doi:10.1016/s0168-1699(03)00086-3

Cited by 188 publications

(95 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, recently, thermal imaging has found applications in other biological systems including agriculture and food processing including process monitoring, product development and storage analysis (Alchanatis et al 2006;Chaerle and Van der Straeten 2000;Gowen et al 2010;Lamprecht et al 2002;Oerke et al 2006;Stajnko et al 2004;Sugiura et al 2007;Vadivambal and Jayas 2010). Infrared thermal imaging was first developed for military purposes but later gained a wider application in various fields such as aerospace, agriculture, civil engineering, medicine, and veterinary.…”

Section: Applications Of Thermal Imaging In Foodmentioning

confidence: 99%

Measurement techniques and application of electrical properties for nondestructive quality evaluation of foods—a review

et al. 2011

View full text Add to dashboard Cite

Non-destructive systems are recent trends for quality evaluation of fruits and vegetables. Information on post-harvest variations in electrical properties is needed to develop new instruments for this purpose. Electrical properties are finding increasing application in agriculture and food processing industries. Knowledge of dielectric properties of foods as a function of moisture content and temperature is essential in the design and control of drying systems. As simple, rapid and non-destructive measuring techniques, dielectric spectroscopy provides information about the dielectric response of materials to electromagnetic field. Electrical properties of agricultural materials have been of interest for many years. The interest in dielectric properties of materials has historically been associated with the design of electrical equipment. This review paper covers theoretical aspects of different electrical properties, their measurement techniques, applications of dielectric properties in agriculture/food processing sector and potential applications of thermal imaging (TI) for quality and safety assessment in food processing. The values of dielectric properties of a number of products including food grains, fruits and vegetables, and meat and meat products are presented in table form. This comprehensive coverage will be useful for academic, scientific and industrial community in treating and applying the facts in developing/testing new processes and products based on electromagnetic energy application.Keywords Electrical properties . Non-destructive . Measuring techniques . Dielectric constant . Loss factor . Thermal imaging technique . Food quality evaluationThe material investigation and moisture measurement using electromagnetic waves in wide spectrum serve for quality control and improvement in many branches like industry, forest and wood-working industry, civil engineering, agriculture, commerce and also foods e.g. for quality evaluation of meat, fruits, coffee etc (Hlavacova 2003; Venkatesh and Raghavan 2004). Non-destructive evaluation of quality based on constituents of fruits and vegetables has been successfully used (Chen et al. 1999;Jha et al. 2001;Kawano 1998). Electrical characteristics of agricultural materials have been of interest for many years (Nelson 2006) and are utilized in many areas of human activities. Techniques for non-destructive determination of quality and related characteristics of agricultural products are helpful to producers, handlers and processors . Permittivity and moisture can be closely correlated when water content is high. Knowledge of dielectric properties in partially frozen material is critical in determining the rates and uniformity of heating in microwave thawing. Electrical properties are important in processing foods with pulsed electric fields, ohmic heating, induction heating, radio frequency, and microwave heating. These properties are also useful in the detection of processing conditions or the quality of foods (Barbosa-Canovas et al. 2006). Considering ...

show abstract

Section: Applications Of Thermal Imaging In Foodmentioning

confidence: 99%

Measurement techniques and application of electrical properties for nondestructive quality evaluation of foods—a review

et al. 2011

View full text Add to dashboard Cite

show abstract

“…However, occlusion of fruit by branches and leaves, variations in illumination conditions, and thresholding errors were identified to be sources of error [12][13][14][15]. For avocado, fruit occlusion is likely to cause similar issues due to the leaves being the same shape and colour as the fruit, the fruit themselves growing inside the canopy, and the size of trees.…”

Section: Introductionmentioning

confidence: 99%

Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia

2017

View full text Add to dashboard Cite

Accurate pre-harvest estimation of avocado (Persea americana cv. Haas) yield offers a range of benefits to industry and growers. Currently there is no commercial yield monitor available for avocado tree crops and the manual count method used for yield forecasting can be highly inaccurate. Remote sensing using satellite imagery offers a potential means to achieve accurate pre-harvest yield forecasting. This study evaluated the accuracies of high resolution WorldView (WV) 2 and 3 satellite imagery and targeted field sampling for the pre-harvest prediction of total fruit weight (kg·tree −1 ) and average fruit size (g) and for mapping the spatial distribution of these yield parameters across the orchard block. WV 2 satellite imagery was acquired over two avocado orchards during 2014, and WV3 imagery was acquired in 2016 and 2017 over these same two orchards plus an additional three orchards. Sample trees representing high, medium and low vigour zones were selected from normalised difference vegetation index (NDVI) derived from the WV images and sampled for total fruit weight (kg·tree −1 ) and average fruit size (g) per tree. For each sample tree, spectral reflectance data was extracted from the eight band multispectral WV imagery and 18 vegetation indices (VIs) derived. Principal component analysis (PCA) and non-linear regression analysis was applied to each of the derived VIs to determine the index with the strongest relationship to the measured total fruit weight and average fruit size. For all trees measured over the three year period (2014, 2016, and 2017) a consistent positive relationship was identified between the VI using near infrared band one and the red edge band (RENDVI1) to both total fruit weight (kg·tree −1 ) (R 2 = 0.45, 0.28, and 0.29 respectively) and average fruit size (g) (R 2 = 0.56, 0.37, and 0.29 respectively) across all orchard blocks. Separate analysis of each orchard block produced higher R 2 values as well as identifying different optimal VIs for each orchard block and year. This suggests orchard location and growing season are influencing the relationship of spectral reflectance to total fruit weight and average fruit size. Classified maps of avocado yield (kg·tree −1 ) and average fruit size per tree (g) were produced using the relationships developed for each orchard block. Using the relationships derived between the measured yield parameters and the optimal VIs, total fruit yield (kg) was calculated for each of the five sampled blocks for the 2016 and 2017 seasons and compared to actual yield at time of harvest and pre-season grower estimates. Prediction accuracies achieved for each block far exceeded those provided by the grower estimates.

show abstract

“…However, inexpensive flying robots can currently carry only a modest payload, and many useful sensors are simply too heavy to be mounted (e.g., high-resolution hyperspectral cameras [14], [15] or thermal cameras [15], [16]). In this paper we develop an approach for using a low resolution (i.e., single pixel) thermal sensor to approximate the field measured by a high-quality thermal camera.…”

Section: B Sensing Modelmentioning

confidence: 99%

Environmental field estimation with hybrid-mobility sensor networks

Evans

Dias

Roelofsen

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

Abstract-The remarkable accessibility of modern flying robots makes them an attractive platform for environmental sensing. However, low cost and ease of use are currently incompatible with large payloads, severely limiting the choice of sensor and ultimately modality. This paper describes the design of a system for using a small infrared thermometer to estimate the surface temperature over an area that is large compared to the area measured by the sensor, by mounting it on a flying robot. We leverage a priori knowledge about the spatial statistics of the phenomena under measure in order to plan an informative sampling path, fusing observations by Gaussian process regression. Our approach is designed to be evaluated in an indoor testbed, in which a quadrotor, in cooperation with simulated static sensing nodes, estimates the spatial distribution of surface temperature over a controlled thermal gradient. We perform extensive systematic experimentation both in simulation and our real-world testbed environment, with our algorithm estimating surface temperature to an accuracy of up to 2.1 C over a 16 m 2 area ranging in value from 25-65 C.

show abstract

Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging

Cited by 188 publications

References 6 publications

Measurement techniques and application of electrical properties for nondestructive quality evaluation of foods—a review

Measurement techniques and application of electrical properties for nondestructive quality evaluation of foods—a review

Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia

Environmental field estimation with hybrid-mobility sensor networks

Contact Info

Product

Resources

About