Deep Learning-Based Method for Irrigation Status Detection in Tomato Using Plant Leaves

被引：0

作者：

Shahi, Tej Bahadur ^{[1
,2
]}

Sitaula, Chiranjibi ^{[3
]}

Bhandari, Krishna Prasad ^{[4
]}

Poudel, Shobha ^{[5
]}

Bhandari, Rupesh ^{[4
]}

Mishra, Ravindra ^{[4
]}

Sharma, Bharat Kumar ^{[5
]}

Mishra, Bhogendra ^{[4
]}

机构：

[1] CQUniversity, School of Engineering and Technology, Norman Gardens, 4701, QLD

[2] Queensland University of Technology, School of Computer Science, Brisbane, 4000, QLD

[3] The University of Melbourne, Kent Institute Australia, Earth Observation and AI Research Group, Department of Infrastructure Engineering, Parkville, 3010, VIC

[4] Tribhuvan University, Centre for Space Science and Geometrics Studies, Pashchimanchal Campus, Pokhara

[5] Science Hub, Raniban, Kathmandu

来源：

IEEE Transactions on Artificial Intelligence | 2025年 / 6卷 / 07期

关键词：

Artificial intelligence; convolution neural network; deep learning; irrigation status; precision agriculture; water management;

D O I：

10.1109/TAI.2025.3528926

中图分类号：

学科分类号：

摘要：

The impact of climate change, arguably global warming and resulting drought, is one of the most escalating agricultural challenges affecting crop productivity. Therefore, effective water management is critical in agricultural practices. The analysis of plant leaves presents an opportunity to gauge irrigation status through automated solutions to encourage broader adoption among farmers. Currently, there is a notable absence of AI methods in the literature for detecting tomato plant irrigation status through leaf analysis. Addressing this gap, we propose a novel end-to-end deep learning (DL)-based method, inspired by the ResNet-50 model. Our model trims unnecessary blocks and reduces larger kernels, significantly streamlining the model to better fit with the leaf image dataset related to the tomato irrigation status. We evaluate our method using a newly developed dataset and find outstanding performance (Precision: 99.05%, Recall: 99.01%, F1-score: 99.01%, mean-average F1: 98.98%, weighted-average F1: 98.95%, Kappa: 98.61%, accuracy: 98.90%) while comparing with the pretrained DL models. Additionally, our model has fewer parameters and lower floating-point operations (FLOPs), enhancing its efficiency and suggesting its potential for more cost-effective and productive irrigation management practices. © 2020 IEEE.

引用

页码：1849 / 1858

页数：9

共 43 条

[1]

Ricciardi V., Ramankutty N., Mehrabi Z., Jarvis L., Chookolingo B., How much of the world’s food do smallholders produce?, Global Food Security, 17, 1, pp. 64-72, (2018)

[2]

Poulton C., Dorward A., Kydd J., The future of small farms: New directions for services, institutions, and intermediation, World Develop, 38, 10, pp. 1413-1428, (2010)

[3]

Mishra B., Et al., Methods in the spatial deep learning: current status and future direction, Spatial Inf. Res., 30, 2, pp. 215-232, (2022)

[4]

Azimi S., Wadhawan R., Gandhi T.K., Intelligent monitoring of stress induced by water deficiency in plants using deep learning, IEEE Trans. Instrum. Meas., 70, pp. 1-13, (2021)

[5]

Shahi T.B., Xu C.-Y., Neupane A., Guo W., Recent advances in crop disease detection using uav and deep learning techniques, Remote Sensing, 15, 9, (2023)

[6]

Silveira F.D., Lermen F.H., Amaral F.G., An overview of agriculture 4.0 development: Systematic review of descriptions, technologies, barriers, advantages, and disadvantages, Comput. Electron. Agric., 189, (2021)

[7]

Shahi T.B., Et al., A cooperative scheme for late leaf spot estimation in peanut using UAV multispectral images, PLoS One, 18, 3, (2023)

[8]

Sun Y., Wang C., Chen H.Y., Ruan H., Response of plants to water stress: A meta-analysis, Front. Plant Sci., 11, (2020)

[9]

Nilay G., Atul P., Deep learning methods and applications for precision agriculture, Machine Learning for Predictive Analysis, pp. 515-527, (2021)

[10]

Costa L., McBreen J., Ampatzidis Y., Guo J., Gahrooei M.R., Babar M.A., Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes, Precis. Agriculture, 23, 2, pp. 622-642, (2022)

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