Computer vision developments for the automatic inspection of fresh and processed fruits

The quality of a fresh or processed fruit or vegetable is defined by a series of characteristics which make it more or less attractive to the consumer, such as ripeness, size, weight, shape, colour, presence of blemishes and diseases, presence or absence of fruit stems, seeds, etc. In summary, these characteristics may cover all of the factors that exert an influence on the product’s appearance, on its nutritional and organoleptic qualities or on its suitability for preservation. Most of these factors have traditionally been assessed by visual inspection performed by trained operators. However, the application of machine vision in agriculture has increased considerably in recent years since it provides substantial information about the nature and attributes of the produces, reduces costs, guarantees the maintenance of quality standards and provides useful information in real time. Moreover, machine vision opens the possibility of exploring agricultural products in invisible regions of the electromagnetic spectrum, as in the ultraviolet or infrared regions. Instituto Valenciano de Investigaciones Agrarias (IVIA) has developed during the past 15 years computer vision systems for the automatic, on-line inspection of fresh and processed fruits and vegetables. This paper shows the most important outcomes in this matter achieved by the department called Centro de Agroingeniería. One of such systems is a machine for the automatic inspection of pomegranate arils for fresh consumption. This machine individualizes, inspects, classifies and separates the arils in four categories, removing those that do not fulfil the minimal specifications. Multivariate analysis models are used to classify the arils with an average success about 90%. Another application is a machine to classify mandarin segments for canning. The system distinguishes among sound, broken or double segments, and is able to detect the presence of seeds in the segments. The system analyses the shape of the each individual segment to estimate morphological features that are used to classify it into different commercial categories. The machine classifies correctly more than 75% of the analyzed segments. Both systems are currently patent pending. In the field of computer vision systems for the inspection of fresh, whole fruit, most research has been focused on citrus fruits. While most commercial systems only detect the blemishes on the skin of fruit, a multispectral system has been developed to identify them. The system is capable of identifying the 11 most common defects of citrus skin using near infrared, colour and ultraviolet. It also uses induced ultraviolet fluorescence. The success rate achieved with such system reached 87% when identifying about 800 defects in five species of oranges and mandarins. The use of hyperspectral sensors makes it possible to conduct a more sophisticated analysis of the scene by acquiring sets of images corresponding to particular wavelengths. Using this technology, we have conducted different works aimed at detecting damages in citrus fruits, including fungal infestation. The acquired multi-dimensional spectral signature characterising a pixel has been used to analyse scenes and to detect different types of defects such as decay, more easily than using standard colour imaging systems

Vision applications in agriculture

From early beginnings in work on the visual guidance of tractors, the National Centre for Engineering in Agriculture has built up a portfolio of projects in which machine vision plays a prominent part. This presentation traces the history of this research, including some highly unusual topics

Automated soil hardness testing machine

This paper describes the design and performance of a mechatronic system for controlling a standard drop-hammer mechanism that is commonly used in performing outdoor soil or ground hardness tests. A low-cost microcontroller is used to control a hydraulic actuator to repeatedly lift and drop a standard free-falling weight that strikes a pipe (sampler) which is pushed deeper into the ground with each impact. The depth of the sampler pipe and position of the hydraulic cylinder are constantly monitored and the number of drops, soil penetration data and other variables are recorded in a database for future analysis. This device, known as the “EVH Trip Hammer”, allows the full automation and faster completion of what is typically a very labour-intensive and slow testing process that can involve human error and the risk of human injuries

The use of machine vision for assessment of fodder quality

At present fodder is assessed subjectively. The evaluation depends greatly on a personal opinion and there can be large variations in assessments. The project has investigated the use of machine vision in several ways, to provide measures of fodder quality that will be ojective and independent of the assessor. Growers will be able to quote a quality measure that buyers can trust. The research includes the possibility of discerning colour differences that are beyond the capability of the human eye, while still using equipment that is of relatively modest cost

Bovine intelligence for training horses

A rail-mounted model of a small cow is to be used in the training of horses for camp-drafting contests. The paper concerns the addition of sensors and a strategy to enable the machine to respond to the proximity of the horse in a manner that will represent the behaviour of a live calf

Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses

Image analysis for ornamental crops is discussed with examples from the bedding plant industry. Feed-forward artificial neural networks are used to segment top and side view images of three contrasting species of bedding plants. The segmented images provide objective measurements of leaf and flower cover, colour, uniformity and leaf canopy height. On each imaging occasion, each pack was scored for quality by an assessor panel and it is shown that image analysis can explain 88.5%, 81.7% and 70.4% of the panel quality scores for the three species, respectively. Stereoscopy for crop height and uniformity is outlined briefly. The methods discussed here could be used for crop grading at marketing or for monitoring and assessment of growing crops within a glasshouse during all stages of production