Monitoring Root and Shoot Characteristics for the Sustainable Growth of Barley Using an IoT-Enabled Hydroponic System and AquaCrop Simulator

Dutta, Monica; Gupta, Deepali; Javed, Yasir; Mohiuddin, Khalid; Juneja, Sapna; Khan, Zafar Iqbal; Nauman, Ali

doi:10.3390/su15054396

Cited by 14 publications

(1 citation statement)

References 48 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Diseases can also be classified based on the part of the crop affected. Symptoms of the disease can appear on the stem, panicle, sheath, or leaf ( Dutta et al., 2023 ). All these diseases cause a loss in the yield of the crop.…”

Section: Introductionmentioning

confidence: 99%

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

Lamba,

Kukreja,

Rashid

et al. 2023

Front. Plant Sci.

Self Cite

View full text Add to dashboard Cite

IntroductionPaddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production.MethodsIn this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered.ResultsThree infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%.DiscussionThe findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models.

show abstract

Section: Introductionmentioning

confidence: 99%

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

Lamba,

Kukreja,

Rashid

et al. 2023

Front. Plant Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

Strigolactone insensitivity affects differential shoot and root transcriptome in barley

Korek,

Uhrig,

Marzec

2024

J Appl Genetics

View full text Add to dashboard Cite

Strigolactones (SLs) are plant hormones that play a crucial role in regulating various aspects of plant architecture, such as shoot and root branching. However, the knowledge of SL-responsive genes and transcription factors (TFs) that control the shaping of plant architecture remains elusive. Here, transcriptomic analysis was conducted using the SL-insensitive barley mutant hvd14.d (carried mutation in SL receptor DWARF14, HvD14) and its wild-type (WT) to unravel the differences in gene expression separately in root and shoot tissues. This approach enabled us to select more than six thousand SL-dependent genes that were exclusive to each studied organ or not tissue-specific. The data obtained, along with in silico analyses, found several TFs that exhibited changed expression between the analyzed genotypes and that recognized binding sites in promoters of other identified differentially expressed genes (DEGs). In total, 28 TFs that recognize motifs over-represented in DEG promoters were identified. Moreover, nearly half of the identified TFs were connected in a single network of known and predicted interactions, highlighting the complexity and multidimensionality of SL-related signalling in barley. Finally, the SL control on the expression of one of the identified TFs in HvD14- and dose-dependent manners was proved. Obtained results bring us closer to understanding the signalling pathways regulating SL-dependent plant development.

show abstract

Boosting of fruit choices using machine learning-based pomological recommendation system

Dutta,

Gupta,

Juneja

et al. 2023

SN Appl. Sci.

View full text Add to dashboard Cite

Pomology, also known as fruticulture, is a significant contributor to the economies of many nations worldwide. While vertical farming methods are not well-suited for fruit cultivation, substrate-based cultivation is commonly practiced. Vertical farming methods use no soil for cultivation of the plants, and the cultivation is done in vertically stacked layers. Therefore, smaller herbs are best suited for such cultivation, whereas, the majority of the fruit trees are big and woody. Therefore, vertical farming methods are not well suited for fruit trees. However, to maximize fruit production, smarter substrate cultivation methods are needed. Utilizing remote sensing techniques, such as Internet of Things (IoT) devices, agriculture sensors, and cloud computing, allows for precision agriculture and smart farming in autonomous systems. Nevertheless, a lack of understanding of fruit nutrient requirements, growing conditions, and soil health conditions can result in reduced fruit production. To address these challenges, this paper proposes an intelligent model based on machine learning that recommends the best fruit to grow based on prevailing soil and climatic conditions. The system is trained on a dataset that includes details on eleven different fruits, such as Nitrogen (N), Phosphorous (P), Potassium (K), temperature, humidity, pH, and rainfall. The model takes into account the soil type and nutrient contents to recommend the most suitable fruit to grow in the prevailing climate. To enhance the model's efficiency, two novel techniques, Gradient-based Side Sampling (GOSS) and Exclusive Feature Bundling (EFB), have been incorporated. The results show that the proposed system has achieved 99% accuracy in recommending the right fruit based on the given environmental conditions. As a result, this system has the potential to significantly improve the profitability of the pomology industry and boost national economies.

show abstract

Monitoring Root and Shoot Characteristics for the Sustainable Growth of Barley Using an IoT-Enabled Hydroponic System and AquaCrop Simulator

Cited by 14 publications

References 48 publications

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

Strigolactone insensitivity affects differential shoot and root transcriptome in barley

Boosting of fruit choices using machine learning-based pomological recommendation system

Contact Info

Product

Resources

About