Novel hybrid transfer neural network for wheat crop growth stages recognition using field images
Abstract Wheat is one of the world’s most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential for precision farming. Determining wheat g...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-96332-9 |
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| author | Aisha Naseer Madiha Amjad Ali Raza Kashif Munir Aseel Smerat Henry Fabian Gongora Carlos Eduardo Uc Rios Imran Ashraf |
| author_facet | Aisha Naseer Madiha Amjad Ali Raza Kashif Munir Aseel Smerat Henry Fabian Gongora Carlos Eduardo Uc Rios Imran Ashraf |
| author_sort | Aisha Naseer |
| collection | DOAJ |
| description | Abstract Wheat is one of the world’s most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential for precision farming. Determining wheat growth stages accurately is crucial for increasing the efficiency of agricultural yield in wheat farming. Preliminary research identified obstacles in distinguishing between these stages, negatively impacting crop yields. To address this, this study introduces an innovative approach, MobDenNet, based on data collection and real-time wheat crop stage recognition. The data collection utilized a diverse image dataset covering seven growth phases ‘Crown Root’, ‘Tillering’, ‘Mid Vegetative’, ‘Booting’, ‘Heading’, ‘Anthesis’, and ‘Milking’, comprising 4496 images. The collected image dataset underwent rigorous preprocessing and advanced data augmentation to refine and minimize biases. This study employed deep and transfer learning models, including MobileNetV2, DenseNet-121, NASNet-Large, InceptionV3, and a convolutional neural network (CNN) for performance comparison. Experimental evaluations demonstrated that the transfer model MobileNetV2 achieved 95% accuracy, DenseNet-121 achieved 94% accuracy, NASNet-Large achieved 76% accuracy, InceptionV3 achieved 74% accuracy, and the CNN achieved 68% accuracy. The proposed novel hybrid approach, MobDenNet, that synergistically merges the architectures of MobileNetV2 and DenseNet-121 neural networks, yields highly accurate results with precision, recall, and an F1 score of 99%. We validated the robustness of the proposed approach using the k-fold cross-validation. The proposed research ensures the detection of growth stages with great promise for boosting agricultural productivity and management practices, empowering farmers to optimize resource distribution and make informed decisions. |
| format | Article |
| id | doaj-art-faecf4649eea452196b6d7d22f733f23 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-faecf4649eea452196b6d7d22f733f232025-08-20T02:11:46ZengNature PortfolioScientific Reports2045-23222025-04-0115111610.1038/s41598-025-96332-9Novel hybrid transfer neural network for wheat crop growth stages recognition using field imagesAisha Naseer0Madiha Amjad1Ali Raza2Kashif Munir3Aseel Smerat4Henry Fabian Gongora5Carlos Eduardo Uc Rios6Imran Ashraf7Institute of Information Technology, Khwaja Fareed University of Engineering and Information TechnologyInstitute of Information Technology, Khwaja Fareed University of Engineering and Information TechnologyDepartment of Software Engineering, University of LahoreInstitute of Information Technology, Khwaja Fareed University of Engineering and Information TechnologyCentre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara UniversityUniversidad Europea del AtlanticoUniversidad Europea del AtlanticoDepartment of Information and Communication Engineering, Yeungnam UniversityAbstract Wheat is one of the world’s most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential for precision farming. Determining wheat growth stages accurately is crucial for increasing the efficiency of agricultural yield in wheat farming. Preliminary research identified obstacles in distinguishing between these stages, negatively impacting crop yields. To address this, this study introduces an innovative approach, MobDenNet, based on data collection and real-time wheat crop stage recognition. The data collection utilized a diverse image dataset covering seven growth phases ‘Crown Root’, ‘Tillering’, ‘Mid Vegetative’, ‘Booting’, ‘Heading’, ‘Anthesis’, and ‘Milking’, comprising 4496 images. The collected image dataset underwent rigorous preprocessing and advanced data augmentation to refine and minimize biases. This study employed deep and transfer learning models, including MobileNetV2, DenseNet-121, NASNet-Large, InceptionV3, and a convolutional neural network (CNN) for performance comparison. Experimental evaluations demonstrated that the transfer model MobileNetV2 achieved 95% accuracy, DenseNet-121 achieved 94% accuracy, NASNet-Large achieved 76% accuracy, InceptionV3 achieved 74% accuracy, and the CNN achieved 68% accuracy. The proposed novel hybrid approach, MobDenNet, that synergistically merges the architectures of MobileNetV2 and DenseNet-121 neural networks, yields highly accurate results with precision, recall, and an F1 score of 99%. We validated the robustness of the proposed approach using the k-fold cross-validation. The proposed research ensures the detection of growth stages with great promise for boosting agricultural productivity and management practices, empowering farmers to optimize resource distribution and make informed decisions.https://doi.org/10.1038/s41598-025-96332-9Precision agricultureAgricultural systemWheat growth predictionHybrid neural networkImage processingDeep learning |
| spellingShingle | Aisha Naseer Madiha Amjad Ali Raza Kashif Munir Aseel Smerat Henry Fabian Gongora Carlos Eduardo Uc Rios Imran Ashraf Novel hybrid transfer neural network for wheat crop growth stages recognition using field images Scientific Reports Precision agriculture Agricultural system Wheat growth prediction Hybrid neural network Image processing Deep learning |
| title | Novel hybrid transfer neural network for wheat crop growth stages recognition using field images |
| title_full | Novel hybrid transfer neural network for wheat crop growth stages recognition using field images |
| title_fullStr | Novel hybrid transfer neural network for wheat crop growth stages recognition using field images |
| title_full_unstemmed | Novel hybrid transfer neural network for wheat crop growth stages recognition using field images |
| title_short | Novel hybrid transfer neural network for wheat crop growth stages recognition using field images |
| title_sort | novel hybrid transfer neural network for wheat crop growth stages recognition using field images |
| topic | Precision agriculture Agricultural system Wheat growth prediction Hybrid neural network Image processing Deep learning |
| url | https://doi.org/10.1038/s41598-025-96332-9 |
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