{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T01:12:11Z","timestamp":1768785131520,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T00:00:00Z","timestamp":1637971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP21K05585"],"award-info":[{"award-number":["JP21K05585"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The biggest challenge in the classification of plant water stress conditions is the similar appearance of different stress conditions. We introduce HortNet417v1 with 417 layers for rapid recognition, classification, and visualization of plant stress conditions, such as no stress, low stress, middle stress, high stress, and very high stress, in real time with higher accuracy and a lower computing condition. We evaluated the classification performance by training more than 50,632 augmented images and found that HortNet417v1 has 90.77% training, 90.52% cross validation, and 93.00% test accuracy without any overfitting issue, while other networks like Xception, ShuffleNet, and MobileNetv2 have an overfitting issue, although they achieved 100% training accuracy. This research will motivate and encourage the further use of deep learning techniques to automatically detect and classify plant stress conditions and provide farmers with the necessary information to manage irrigation practices in a timely manner.<\/jats:p>","DOI":"10.3390\/s21237924","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7924","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["HortNet417v1\u2014A Deep-Learning Architecture for the Automatic Detection of Pot-Cultivated Peach Plant Water Stress"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5931-853X","authenticated-orcid":false,"given":"Md Parvez","family":"Islam","sequence":"first","affiliation":[{"name":"Research Center for Agricultural Robotics, NARO, Tsukuba 3050856, Japan"}]},{"given":"Takayoshi","family":"Yamane","sequence":"additional","affiliation":[{"name":"Research Center for Agricultural Information Technology and National Institute of Fruit Tree and Tea Science, NARO, Tsukuba 3050856, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,27]]},"reference":[{"key":"ref_1","first-page":"2118","article-title":"Influence of irrigation method and scheduling on patterns of soil and tree water status and its relation to yield and fruit quality in peach","volume":"40","author":"Bryla","year":"2005","journal-title":"J. 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