AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 11; p. 3830
• Main Authors: Almadhor, Ahmad, Rauf, Hafiz Tayyab, Lali, Muhammad Ikram Ullah, Damaševičius, Robertas, Alouffi, Bader, Alharbi, Abdullah
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-06-2021; MDPI
• Subjects: agricultural informatics; Agriculture; Economic impact; feature extraction; Feature recognition; Fruits; Guava; guava fruit diseases; High resolution; Histograms; machine learning; Medical imaging; Nematodes; Neural networks; Pesticides; Plant diseases; Signs and symptoms; Tropical environments; Pakistan
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21113830

Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics’ apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers’ improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants’ leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions.