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    Application of Melaleuca cajuputi Powell crude extract on controlling postharvest anthracnose disease in Gross Michel banana (Musa (AAA group) ‘Kluai Hom Thong’)
    (2026-03-01)
    Montri, N.
    ;
    Leujantuak, P.
    ;
    Tongsri, V.
    ;
    Deewatthanawong, R.
    ;
    Bunya-Atichart, K.
    Exporting Gross Michel bananas, known as ‘Kluai Hom Thong’ in Thailand, to Japan involves several considerations to ensure compliance with Japan’s stringent food safety and quality standards, especially regarding pesticide residues and other contaminants. The research aimed to evaluate the impact of various concentrations of Melaleuca cajuputi Powell crude extract on controlling postharvest anthracnose disease in Gross Michel bananas, focusing on developing a non-toxic production method. The 70% ripening stage bananas were dipped with 0, 800, 1,000, and 1,200 mg L<sup>-1</sup> crude extract for 2 min before spraying with 10<sup>-6</sup> spore mL<sup>-1</sup> of Colletotrichum musae spore suspension, followed by a 2-day incubation, subsequent spraying with 500 mg L<sup>-1</sup> of ethephon solution, and a further 1-day incubation at ambient temperature (29±2°C). The shelf-life of banana, weight loss, total soluble solids, and surface morphology of banana peel were analyzed. Results showed that applying M. cajuputi Powell crude extract could extend shelf-life and maintain the quality of banana fruits. The 800 mg L<sup>-1</sup> crude extract resulted in 2.13% weight loss, with peel density at 14.80 N cm<sup>-3</sup> and pulp density at 6.23 N cm<sup>-3</sup>. Additionally, it resulted in the lowest percentage of anthracnose disease at 7.5% and the longest shelf life of 8 days.
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    Banana Plant Nutrient Deficiencies Identification using Deep Learning
    (2023-01-01)
    Han, Kadipa Aung Myo
    ;
    Maneerat, Noppadol
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    Sepsirisuk, Kasemsuk
    ;
    Hamamoto, Kazuhiko
    This paper presents nutrient deficiency multi-class classification in banana plant data sets using a deep convolutional neural network. In this paper, healthy and eight nutrient deficiency classes were studied. The performance was evaluated in different situations of two public data sets. The proposed method can provide sensitivity and specificity in Raw Images, Raw Images with combination, Augmented Images, and Augmented Images with the combination. Furthermore, nearly 88% of the F1-score was outperformed.