Background The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time-frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). Results The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves. Conclusion Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.

中文翻译：

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机器学习驱动的使用太赫兹波估算活植物叶子水分含量的非侵入性方法。


背景 由于农业科学领域持续的全球气候变化，对有效利用水资源的需求有所增加。具有成本效益且及时分配适量的水不仅对于维持植物叶子的健康状态至关重要，而且对于提高作物的生产力并实现经济效益至关重要。在这方面，采用太赫兹（THz）技术由于其独特的特点可以成为更可靠和先进的技术。本文提出了一种新颖的非侵入式机器学习 (ML) 驱动方法，在现实生活中的数字农业干预中使用太赫兹波和 swissto12 材料表征套件 (MCK)，频率范围为 0.75 至 1.1 THz，旨在开发一种可行且可行的技术，用于精确估算 4 天植物叶子中的水分含量 (WC)。为此，使用测量观测数据，从频率、时间、时频域中提取多域特征，以合并三种不同的机器学习算法，例如支持向量机（SVM）、K近邻（KNN）和决策树（D 树）。结果结果表明，SVM 在不同天的分类中使用十倍和留一观察交叉验证优于其他分类器，对咖啡、豌豆苗和菠菜叶的总体准确率分别为 98.8%、97.15% 和 96.82% 。此外，使用 SFS 技术，咖啡叶在 SVM、KNN 和 D-tree 的计算时间上显着提高了 15%、11.9%、6.5%。对于豌豆芽，SVM、KNN 和 D-Tree 分类器的运行时间分别提高了 21.28%、10.01% 和 8.53%。最后，小菠菜叶进一步提高了 21。在分类器的总运行时间中，SVM 为 28%，KNN 为 10.01%，D 树为 8.53%。分类器的这些改进显着提高了分类精度，表明叶子中 WC 的量化更加精确。结论 因此，所提出的使用太赫兹波结合机器学习的方法有利于精确估计叶子中的 WC，并可以为种植者在植物健康监测方面采取积极行动提供丰富的建议和见解。