Machine learning driven non-invasive approach of water content estimation in living plant leaves using terahertz waves

Abstract

BackgroundThe 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).ResultsThe 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.ConclusionThus, 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.

Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

“…In this sense, A4.0 refers to the use of information and communication technologies such as Big Data and Analytics and ML to explore the variability of data and use it to deal with changes in the agricultural scenario [57]. A4.0 is directly related to emerging technologies, such as ML algorithms for water management [58] and automation of grain selection [59], complex systems for identifying and monitoring pests and diseases [57], and artificial intelligence for soil analysis [60].…”

“…Several works have implemented A4.0 technologies to improve productivity and sustainability in specific scenarios. Zahid et al [58] propose a non-invasive ML model for water management and generate an accurate estimate of the water content in plants, and Lasso et al [57] reviewed the use of alert systems based on ML to identify pests and diseases.…”

Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

“…UAV are being used to measure with high spatial and temporal resolution capable of generating useful information for plant breeding tasks [15][16][17]. In the era of digital revolution, aerial image phenotyping [18][19][20] and ML models could predict crop yield performance [21][22][23][24][25][26][27] in a non-invasive means with a greater accuracy [28][29][30][31]. Efficient selection of desired phenotypes through HTP across large field populations could be achieved through incorporating ML methodologies such as, automated identification, classification, quantification and prediction [20].…”

Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

“…UAV are being used to measure with high spatial and temporal resolution capable of generating useful information for plant breeding tasks [15][16][17]. In the era of digital revolution, aerial image phenotyping [18][19][20] and ML models could predict crop yield performance [21][22][23][24][25][26][27] in a non-invasive means with a greater accuracy [28][29][30][31][32]. Efficient selection of desired phenotypes through HTP across large field populations could be achieved through incorporating ML methodologies such as, automated identification, classification, quantification and prediction [20].…”