2,367 research outputs found

    Boosting minimalist classifiers for blemish detection in potatoes

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    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 nonblemishes. 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, minimalist blemish detectors were trained for both white and red potato varieties, achieving 89.6% and 89.5% accuracy respectively

    Potato Classification Using Deep Learning

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    Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties of potatoes, which can be classified into a number of categories based on the cooked texture and ingredient functionality. Using a public dataset of 2400 images of potatoes, we trained a deep convolutional neural network to identify 4 types (Red, Red Washed, Sweet, and White).The trained model achieved an accuracy of 99.5% of test set, demonstrating the feasibility of this approach

    Visual detection of blemishes in potatoes using minimalist boosted classifiers

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    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

    Experimental analysis and evaluation of wide residual networks based agricultural disease identification in smart agriculture system

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    Specialised pest and disease control in the agricultural crops industry have been a high-priority issue. Due to great cost-effectiveness and efficient automation, computer vision (CV)–based automatic pest or disease identification techniques are widely utilised in the smart agricultural systems. As rapid development of artificial intelligence, in the field of computer vision–based agricultural pest identification, an increasing number of scholars have begun to move their attentions from traditional machine learning models to deep learning techniques. However, so far, deep learning techniques still have been suffering from many problems such as limited data samples, cost-effectiveness of network structure, and high image quality requirements. These issues greatly limit the potential utilisation of deep-learning techniques into smart agricultural systems. This paper aims at investigating utilization of one new deep-learning model WRN (wide residual networks) into CV-based automatic disease identification problem. We first built up a large-scale agricultural disease images dataset containing over 36,000 pieces of diseases, which includes typical types of disease in tomato, potato, grape, corn and apple. Then, we analysed and evaluated wide residual networks algorithm using the Tesla K80 graphics processor (GPU) in the TensorFlow deep-learning framework. A set of comprehensive experimental protocols have been designed in comparing with GoogLeNet Inception V4 regarding several benchmarks. The experimental results indicate that (1) under WRN architecture, Softmax loss function gives a faster convergence and improved accuracy than GoogLeNet inception V4 network. (2) While WRN shows a good effect for identification of agricultural diseases, its effectiveness has a strong need on the number of training samples of dataset like at least 36 k images in our experiment. (3) The overall performance is better than 800 sheets. The disease identification results show that the WRN model can be applied to the identification of agricultural diseases

    AlexNet-Based Feature Extraction for Cassava Classification: A Machine Learning Approach

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    تعتبر الكسافا محصولًا مهمًا في أجزاء كثيرة من العالم، لا سيما في إفريقيا وآسيا وأمريكا الجنوبية، حيث تعمل كغذاء أساسي لملايين الأشخاص. يعتبر استخدام ميزات اللون والملمس والشكل أقل كفاءة في تصنيف أنواع الكسافا. وذلك لأن أوراق الكسافا لها نفس لون مورفولوجيا بين نوع وآخر. بالإضافة إلى ذلك، فإن أوراق الكسافا لها شكل مشابه نسبيًا لنوع واحد من الكسافا، وبالمثل، مع قوام أوراق الكسافا. إلى جانب ذلك، هناك أيضًا المنيهوت السامة. الكسافا السامة وغير السامة لها لون وشكل وملمس أوراق متطابق نسبيًا. يهدف هذا البحث إلى تصنيف أنواع الكسافا باستخدام طريقة التعلم العميق مع AlexNet المدربة مسبقًا كمستخرج للميزات. تم استخدام ثلاث طبقات مختلفة متصلة بالكامل لاستخراج السمات، وهي fc6 و fc7 و fc8. كانت المصنفات المستخدمة هي Support Vector Machine (SVM) و K-Nearest Neighbours (KNN) و Naive Bayes. تتكون مجموعة البيانات من 1400 صورة لأوراق الكسافا تتكون من أربعة أنواع من الكسافا: Gajah و Manggu و Kapok و Beracun. أوضحت النتائج أن أفضل طبقة استخلاص كانت fc6 وبدقة 90.7٪ للطبقة المتناهية الصغر (SVM). كان أداء SVM أيضًا أفضل مقارنةً بـ KNN و Naive Bayes، بدقة 90.7٪، وحساسية 83.5٪، ونوعية 93.7٪، ودرجة F1 83.5٪. ستساهم نتائج هذا البحث في تطوير تقنيات تصنيف النباتات، وتوفير رؤى حول الاستخدام الأمثل للتعلم العميق وطرق التعلم الآلي لتحديد الأنواع النباتية. في النهاية، يمكن للنهج المقترح أن يساعد الباحثين والمزارعين وعلماء البيئة في تحديد الأنواع النباتية ومراقبة النظام البيئي والإدارة الزراعية.Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has 350 images. Three fully connected (FC) layers were utilized for feature extraction, namely fc6, fc7, and fc8. The classifiers employed were support vector machine (SVM), k-nearest neighbors (KNN), and Naive Bayes. The study demonstrated that the most effective feature extraction layer was fc6, achieving an accuracy of 90.7% with SVM. SVM outperformed KNN and Naive Bayes, exhibiting an accuracy of 90.7%, sensitivity of 83.5%, specificity of 93.7%, and F1-score of 83.5%. This research successfully addressed the challenges in classifying cassava species by leveraging deep learning and machine learning methods, specifically with SVM and the fc6 layer of AlexNet. The proposed approach holds promise for enhancing plant classification techniques, benefiting researchers, farmers, and environmentalists in plant species identification, ecosystem monitoring, and agricultural management

    A Survey on the State of Art Approaches for Disease Detection in Plants

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    Agriculture is the main factor for economy and contributes to GDP. The growth of the economy of many countries is based on agriculture. As a result, the yield factor, quality and volume of agricultural products, play a critical role in economic development. Plant diseases and pests have become a major determinant of crop yields throughout the years, as such illnesses in plants offer a serious threat and impediment to higher yields or production in the agriculture industry. As a result, From the outset, it becomes the major duty to correctly monitor the plants, to detect diseases thoroughly, and to determine methods of controlling or monitoring these plant diseases pests in order to achieve a higher rate of production growth and minimal crop damage. Using machine vision, deep learning methods and tools for extracting and classifying features, It could be possible to build a reliable disease detection system. Numerous researchers have created and deployed various ways for detecting plant diseases and pests. The potential of these methods has been examined in this work

    Review: computer vision applied to the inspection and quality control of fruits and vegetables

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    This is a review of the current existing literature concerning the inspection of fruits and vegetables with the application of computer vision, where the techniques most used to estimate various properties related to quality are analyzed. The objectives of the typical applications of such systems include the classification, quality estimation according to the internal and external characteristics, supervision of fruit processes during storage or the evaluation of experimental treatments. In general, computer vision systems do not only replace manual inspection, but can also improve their skills. In conclusion, computer vision systems are powerful tools for the automatic inspection of fruits and vegetables. In addition, the development of such systems adapted to the food industry is fundamental to achieve competitive advantages

    Applications of Image Processing for Grading Agriculture products

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    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

    Analysis on Leaf Disease Identification using Classification Models

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    The Researchers have all been aware of the rising food demand brought on by the population's rapid growth and the high mortality rates caused by medical developments. One of the many farming practises where computerization in agriculture has made significant progress is the identification of numerous plant diseases. The focus of almost every nation has shifted towards mechanising agriculture in order to achieve accuracy and precision and to meet the continually increasing demand for food. Identification of plant diseases is one of the most difficult tasks in agriculture and has a significant effect on crop yield. Artificial intelligence has recently begun to concentrate on smart agriculture science.Ground-breaking methods in plant science through deep learning and hyperspectral imaging to locate and recognise plant diseases has been addressed in this study
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