Abstract
Automatic recognition of ripening tomatoes is a main hurdle precluding the replacement of manual labour by robotic harvesting. In this paper, we present a novel automatic algorithm for recognition of ripening tomatoes using an improved method that combines multiple features, feature analysis and selection, a weighted relevance vector machine (RVM) classifier, and a bi-layer classification strategy. The algorithm operates using a two-layer strategy. The first-layer classification strategy aims to identify tomato-containing regions in images using the colour difference information. The second classification strategy is based on a classifier that is trained on multi-medium features. In our proposed algorithm, to simplify the calculation and to improve the recognition efficiency, the processed images are divided into 9 × 9 pixel blocks, and these blocks, rather than single pixels, are considered as the basic units in the classification task. Six colour-related features, namely the Red (R), Green (G), Blue (B), Hue (H), Saturation (S) and Intensity (I) components, respectively, colour components, and five textural features (entropy, energy, correlation, inertial moment and local smoothing) were extracted from pixel blocks. Relevant features and their weights were analysed using the iterative RELIEF (I-RELIEF) algorithm. The image blocks were classified into different categories using a weighted RVM classifier based on the selected relevant features. The final results of tomato recognition were determined by combining the block classification results and the bi-layer classification strategy. The algorithm demonstrated the detection accuracy of 94.90% on 120 images, this suggests that the proposed algorithm is effective and suitable for tomato detection
Highlights
Tomato is a popularly cultivated fruit/vegetable that is highly favoured by consumers worldwide owing to its unique flavour, rich nutritional content, and health-promoting properties
The image blocks were classified into different categories using a weighted relevance vector machine (RVM) classifier based on the selected relevant features
The algorithm demonstrated the detection accuracy of 94.90% on 120 images, this suggests that the proposed algorithm is effective and suitable for tomato detection Keywords: tomato recognition; harvesting robots; multi-feature fusion; feature analysis; weighted
Summary
Tomato is a popularly cultivated fruit/vegetable that is highly favoured by consumers worldwide owing to its unique flavour, rich nutritional content, and health-promoting properties. Automation technology has been widely used in various fields, such as machinery manufacture, industrial production, traffic control, and agriculture. The rationale for agricultural automatization is reduction in the manpower; robotized harvesting has become popular in agriculture automatization [1]. In China, harvesting of tomatoes is associated with high labour cost. Tardy manual work causes deterioration, and improper handling during plucking may influence the transportation and preservation of tomatoes. There has been a recent trend of replacing human workers with harvesting robots to avoid the above-described drawbacks [2]. Recognition and localization of fruits and vegetables is fundamentally important [3]
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