Minimalist AdaBoost for blemish identification in potatoes

We present a multi-class solution based on minimalist Ad- aBoost for identifying blemishes present in visual images of potatoes. Using training examples we use Real AdaBoost to rst reduce the fea- ture set by selecting ve features for each class, then train binary clas- siers for each class, classifying each testing example according to the binary classier with the highest certainty. Against hand-drawn ground truth data we achieve a pixel match of 83% accuracy in white potatoes and 82% in red potatoes. For the task of identifying which blemishes are present in each potato within typical industry dened criteria (10% coverage) we achieve accuracy rates of 93% and 94%, respectively

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

Visual detection of blemishes in potatoes using minimalist boosted classifiers

This paper introduces novel methods for detecting blemishes in potatoes using machine vision. After segmentation of the potato from the background, a pixel-wise classifier is trained to detect blemishes using features extracted from the image. A very large set of candidate features, based on statistical information relating to the colour and texture of the region surrounding a given pixel, is first extracted. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and non-blemishes. With this approach, different features can be selected for different potato varieties, while also handling the natural variation in fresh produce due to different seasons, lighting conditions, etc. The results show that the method is able to build ``minimalist'' classifiers that optimise detection performance at low computational cost. In experiments, blemish detectors were trained for both white and red potato varieties, achieving 89.6\% and 89.5\% accuracy, respectively

Applications of Image Processing for Grading Agriculture products

Image processing in the context of Computer vision, is one of the renowned topic of computer science and engineering, which has played a vital role in automation. It has eased in revealing unknown fact in medical science, remote sensing, and many other domains. Digital image processing along with classification and neural network algorithms has enabled grading of various things. One of prominent area of its application is classification of agriculture products and especially grading of seed or cereals and its cultivars. Grading and sorting system allows maintaining the consistency, uniformity and depletion of time. This paper highlights various methods used for grading various agriculture products. DOI: 10.17762/ijritcc2321-8169.15036

Importance of Machine Vision Framework with Nondestructive Approach for Fruit Classification and Grading: A Review

Machine vision technology has gained significant importance in the agricultural industry, particularly in the non-destructive classification and grading of fruits. This paper presents a comprehensive review of the existing literature, highlighting the crucial role of machine vision in automating the fruit quality assessment process. The study encompasses various aspects, including image acquisition techniques, feature extraction methods, and classification algorithms. The analysis reveals the substantial progress made in the field, such as developing sophisticated hardware and software solutions, which have improved accuracy and efficiency in fruit grading. Furthermore, it discusses the challenges and limitations, such as dealing with variability in fruit appearance, handling different fruit types, and real-time processing. The identification of future research needs emphasizes the potential for enhancing machine vision frameworks through the integration of advanced technologies like deep learning and artificial intelligence.Additionally, it underscores the importance of addressing the specific needs of different fruit varieties and exploring the applicability of machine vision in real-world scenarios, such as fruit packaging and logistics. This review underscores the critical role of machine vision in non-destructive fruit classification and grading, with numerous opportunities for further research and innovation. As the agricultural industry continues to evolve, integrating machine vision technologies will be instrumental in improving fruit quality assessment, reducing food waste, and enhancing the overall efficiency of fruit processing and distribution

Application progress of machine vision technology in the field of modern agricultural equipment

With the rapid progress of image processing algorithms and computer equipment, the development of machine vision technology in the field of modern agricultural equipment is in the ascendant, and major application results have been obtained in many production links to improve the efficiency and automation of agricultural production. In the face of China, the world's largest agricultural market, agricultural machine vision equipment undoubtedly has tremendous development potential and market prospects. This paper introduces the research and application of machine vision technology in agricultural equipment in the fields of agricultural product sorting, production automation, pest control, picking machinery and navigation and positioning, analyzes and summarizes the current problems, and looks forward to the future development trend