Evaluating the potential of artificial neural network and neuro-fuzzy techniques for estimating antioxidant activity and anthocyanin content of sweet cherry during ripening by using image processing

A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology

Fruit phenotypic information reflects all the physical, physiological, biochemical characteristics and traits of fruit. Accurate access to phenotypic information is very necessary and meaningful for post-harvest storage, sales and deep processing. The methods of obtaining phenotypic information include traditional manual measurement and damage detection, which are inefficient and destructive. In the field of fruit phenotype research, image technology is increasingly mature, which greatly improves the efficiency of fruit phenotype information acquisition. This review paper mainly reviews the research on phenotypic information of Prunoideae fruit based on three imaging techniques (RGB imaging, hyperspectral imaging, multispectral imaging). Firstly, the classification was carried out according to the image type. On this basis, the review and summary of previous studies were completed from the perspectives of fruit maturity detection, fruit quality classification and fruit disease damage identification. Analysis of the advantages and disadvantages of various types of images in the study, and try to give the next research direction for improvement.

Predictive Modeling of a Leaf Conceptual Midpoint Quasi-Color (CMQ) Using an Artificial Neural Network

The color of plant leaves is moderated by the content of pigments, which can show considerable dorsiventral distribution. Two typical examples are leafy vegetables and ornamentals, wherein red and green color surfaces can be seen on the same leaf. The proof of concept is provided for predictive modeling of a leaf conceptual mid-point quasi-color (CMQ) from the content of pigments. The CMQ idea is based on the hypothesis that the content of pigments in leaves is associated with the combined color from both surfaces. The CMQ, which is calculated from CIELab color coordinates at adaxial and abaxial antipodes, is thus not an actual color, but a notion that can be used in modeling. The CMQ coordinates, predicted from the content of chlorophylls and anthocyanins by means of an artificial neural network (ANN), matched well with the CMQ coordinates empirically found on photosynthetically active leaves of lettuce (Lactuca sativa L.), but also with other plant species with comparable leaf attributes. Modeled values of lightness (qL*) decreased with the increasing content of both pigments, while the redness or greenness (qa*) and yellowness or blueness (qb*) of the CMQ were affected more by a relative content of chlorophylls and anthocyanins in leaves. The highest vividness of quasi-colors (qC*) was modeled for leaves with a high content of either pigment alone. The model predicted a substantially duller quasi-color for leaves with chlorophylls and anthocyanins present together, particularly when both pigments were present at very high levels.

Massive phenotyping of multiple cranberry populations reveals novel QTLs for fruit anthocyanin content and other important chemical traits

Because of its known phytochemical activity and benefits for human health, American cranberry (Vaccinium macrocarpon L.) production and commercialization around the world has gained importance in recent years. Flavonoid compounds as well as the balance of sugars and acids are key quality characteristics of fresh and processed cranberry products. In this study, we identified novel QTL that influence total anthocyanin content (TAcy), titratable acidity (TA), proanthocyanidin content (PAC), Brix, and mean fruit weight (MFW) in cranberry fruits. Using repeated measurements over the fruit ripening period, different QTLs were identified at specific time points that coincide with known chemical changes during fruit development and maturation. Some genetic regions appear to be regulating more than one trait. In addition, we demonstrate the utility of digital imaging as a reliable, inexpensive and high-throughput strategy for the quantification of anthocyanin content in cranberry fruits. Using this imaging approach, we identified a set of QTLs across three different breeding populations which collocated with anthocyanin QTL identified using wet-lab approaches. We demonstrate the use of a high-throughput, reliable and highly accessible imaging strategy for predicting anthocyanin content based on cranberry fruit color, which could have a large impact for both industry and cranberry research.

Sorting Olive Batches for the Milling Process Using Image Processing

The quality of virgin olive oil obtained in the milling process is directly bound to the characteristics of the olives. Hence, the correct classification of the different incoming olive batches is crucial to reach the maximum quality of the oil. The aim of this work is to provide an automatic inspection system, based on computer vision, and to classify automatically different batches of olives entering the milling process. The classification is based on the differentiation between ground and tree olives. For this purpose, three different species have been studied (Picudo, Picual and Hojiblanco). The samples have been obtained by picking the olives directly from the tree or from the ground. The feature vector of the samples has been obtained on the basis of the olive image histograms. Moreover, different image preprocessing has been employed, and two classification techniques have been used: these are discriminant analysis and neural networks. The proposed methodology has been validated successfully, obtaining good classification results.

Estimating Some Physical Properties of Sour and Sweet Cherries Based on Combined Image Processing and AI Techniques

Physical properties of agricultural products are considered as important factors in optimization of storage conditions, packaging, transportation, water adsorption/desorption, heat, pesticides, and foodstuff moving out and also their breathing. This paper presents a time and cost economizing method to determine these important attributes of sour and sweet cherries by combining image processing and two common artificial intelligence techniques, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). The measuring technique consisted of a charge-coupled device camera for image acquisition, fluorescent illuminants, capture card, and MATLAB for image analysis. Several networks were designed, trained, and generalized with a back-propagation algorithm using “trainlm” as training function. Several ANFIS models were designed with different number and type of membership functions (MFs) for each input. Generally, “gaussian” and “pi-shaped” MFs showed better results for estimating output variables among others. Considering statistical analysis, ANFIS showed better results than ANN.