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Proposed study overcomes the major limitations and weaknesses of thexisting contemporary methods. This study introduces a novel three-phase approach: (1) segmentation of disease regions using a Min\u2013Max Hue Histogram-based technique to isolate the disease region with the most important data, (2) a lightweight, sequential deep learning model (PDCNet) trained from scratch to classify the diseases and (3) a disease recovery module to provide recommendation to farmer. Unlikexisting works, we avoid data augmentation and transfer learning toliminate overfitting risks and domain mismatches. Additionally, we created the Plant Disease Small Dataset (PDSD)\u2014a new, realistic subset of the PlantVillage dataset featuring images with variednvironmental conditions and backgrounds.experimental results show that our method achieves\u2002an average classification accuracy of 91%, which is better than popular CNN architectures (VGG19 and ResNet50). This complete system provides an accurate,ffective, and convenient way for farmers to diagnose plant diseases and to receive recovery\u2002strategy, which can help promote the precision agriculture practices.<\/jats:p>","DOI":"10.1186\/s40537-025-01261-z","type":"journal-article","created":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T19:35:13Z","timestamp":1764444913000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An\u00a0effective approach for classification of plant disease of small size dataset by using sequential deep learning model with Min\u2013Max Hue histogram based segmentation"],"prefix":"10.1186","volume":"13","author":[{"given":"Vijay Kumar","family":"Trivedi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piyush Kumar","family":"Shukla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anjana","family":"Pandey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3423-0993","authenticated-orcid":false,"given":"Noha","family":"Alduaiji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.","family":"Shreyas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,29]]},"reference":[{"key":"1261_CR1","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1257\/jep.28.1.121","volume":"28","author":"JM Alston","year":"2014","unstructured":"Alston JM, Pardey PG. 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