Bibliographic Details
| Title: |
Potential of machine learning in leaf-based multi-source data driven tomato growth monitoring |
| Authors: |
Ke Zhang, Qi Chai, Xiaojin Qian, Ruocheng Gao, Xiaoying Liu, Lifei Yang, Guan Pang, Yu Wang, Jin Sun |
| Source: |
Smart Agricultural Technology, Vol 10, Iss , Pp 100854- (2025) |
| Publisher Information: |
Elsevier, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Agriculture (General) |
| Subject Terms: |
Machine learning, Growth parameter, Color index, Crop phenotyping, Agriculture (General), S1-972, Agricultural industries, HD9000-9495 |
| More Details: |
Tomatoes (Solanum lycopersicum L.) represent a crucial fruit and vegetable crop whose leaf is a significant phenotypic parameter regulating photosynthesis and growth in a greenhouse environment. Chlorophyll content and leaf area index (LAI) are essential leaf indicators for directly monitoring alterations in tomato growth status. Therefore, this study conducted two years of experiments for collecting tomato growth parameters [tomato yield, vitamin C (VC), above-ground biomass (AGB)], sensors indicators [Chlorophyll content (SPAD), LAI], and image-based color indexes (CIs). Four machine learning methods and multiple-step regression (MSR) were applied to explore the monitoring model and methodology of the tomato growth parameters. The results demonstrated that leaf-based indicators performed well in nitrogen related indicators [leaf nitrogen content and N nutrition index (NNI)] and AGB estimation (|r|>0.7). CIs were important indicators in tomato yield and VC prediction (0.20.31) or CIs (R2>0.31). The mixture of sensor values and CIs could predict the tomato indicators accurately (0.87>R2>0.42). MSR method measured a higher R2 and low error in tomato indicators (Linear: 0.81>R2>0.14, 19.56>RMSE>0.02; MSR: 0.85>R2>0.42, 10.73>RMSE>0.01). Machine learning obtained higher accuracy and lower error by combining sensors’ indexes and CIs for monitoring tomato growth indicators than the single indicator (sensor index or CI, 0.87>R2>0.45). Significantly, the least absolute shrinkage and selection operator measured the relatively higher R2 (>0.73) and lower error (RMSE |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2772-3755 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2772375525000875; https://doaj.org/toc/2772-3755 |
| DOI: |
10.1016/j.atech.2025.100854 |
| Access URL: |
https://doaj.org/article/9fdd21884fa9423ab5b6f3fa8f0dd659 |
| Accession Number: |
edsdoj.9fdd21884fa9423ab5b6f3fa8f0dd659 |
| Database: |
Directory of Open Access Journals |
