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Detection and Classification of Diseases and Maturity of Dragon Fruits
Published in Springer Science and Business Media Deutschland GmbH
Volume: 321
Pages: 365 - 374
A huge population of India is dependent on agriculture as their primary source of livelihood. The financial growth of India depends on the GDP (gross domestic product) rate of the agricultural commodities. Since dragon fruit is in high demand both domestically and globally, dragon fruit plantations are economical for small scale to large scale growers and businesses. There is a heavy focus on each stage of the fruit’s development because prolonged or premature harvesting of the fruit can result in food decay and can also impact the India’s economic growth. Farmers are still making use of manual methods of grading fruit maturity and identifying disease ridden crops, which can result in poor grading due to weariness and misjudgment. The article proposes the use of region-based convolutional neural network, a deep learning algorithm for detection and classification of common dragon fruit diseases and detection of dragon fruit maturity. There are three key steps to this process of detection and classification. The first step includes identification of several regions of interest, which are the bounding box candidates using selective search. A broad convolutional neural network extracts features from each region in the second step. The last step performs grouping of support vector machine (SVM) that tries to classify the regions for the classification of different dragon fruit diseases and detecting its maturity. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About the journal
JournalLecture Notes in Networks and Systems
PublisherSpringer Science and Business Media Deutschland GmbH
Open AccessNo