LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming.

Wheat lodging is a serious problem affecting grain yield, plant health, and grain quality. Addressing the lodging issue in wheat is a desirable task in breeding programs. Precise detection of lodging levels during wheat screening can aid in selecting lines with resistance to lodging. Traditional approaches to phenotype lodging rely on manual data collection from field plots, which are slow and laborious, and can introduce errors and bias. This paper presents a framework called 'LodgeNet,' that facilitates wheat lodging detection. Using Unmanned Aerial Vehicles (UAVs) and Deep Learning (DL), LodgeNet improves traditional methods of detecting lodging with more precision and efficiency. Using a dataset of 2000 multi-spectral images of wheat plots, we have developed a novel image registration technique that aligns the different bands of multi-spectral images. This approach allows the creation of comprehensive RGB images, enhancing the detection and classification of wheat lodging. We have employed advanced image enhancement techniques to improve image quality, highlighting the important features of wheat lodging detection. We combined three color enhancement transformations into two presets for image refinement. The first preset, 'Haze & Gamma Adjustment,' minimize atmospheric haze and adjusts the gamma, while the second, 'Stretching Contrast Limits,' extends the contrast of the RGB image by calculating and applying the upper and lower limits of each band. LodgeNet, which relies on the state-of-the-art YOLOv8 deep learning algorithm, could detect and classify wheat lodging severity levels ranging from no lodging (Class 1) to severe lodging (Class 9). The results show the mean Average Precision (mAP) of 0.952% @0.5 and 0.641% @0.50-0.95 in classifying wheat lodging severity levels. LodgeNet promises an efficient and automated high-throughput solution for real-time crop monitoring of wheat lodging severity levels in the field.

Effect of laser wavelength on soil carbon measurements using laser-induced breakdown spectroscopy.

We investigate the effect of laser wavelength on laser-induced breakdown spectroscopy (LIBS) on the measurement of carbon in agricultural soils. Two laser wavelengths, 1064 nm and 532 nm, were used to determine soil carbon concentration. No chemical pretreatment, grinding, or pelletization was performed on soil samples to simulate in-field conditions. A multivariate calibration model with outlier filtering and optimized parameters in partial least squared regression (PLSR) was established and validated. The calibration model estimated carbon content in soils with an average prediction error of 4.7% at a laser wavelength of 1064 nm and 2.7% at 532 nm. The limit of detection (LOD) range for 532 nm was 0.34-0.5 w/w%, approximately half of the LOD range for 1064 nm laser wavelength. The improvement in prediction error and LOD of LIBS measurements is attributed to the increase in plasma density achieved at 532 nm.

Charge transfer and opto-electronic properties of some newly designed polycatenar discotic liquid crystal derivatives: a DFT study.

Herein, we demonstrate effect of substituents on optoelectronic properties of discotic liquid crystals (DLCs) by using density functional theory (DFT) calculations at B3LYP/Lanl2Z level of theory. Three parent DLCs, namely, (1) benzene-1,3,5-triyl tris(3,5-dialkoxybenzoate), (2) N1, N3, N5-tris(3-alkoxyphenyl)benzene-1,3,5-tricarboxamide, and (3) trialkyl 4, 4', 4″-(benzenetricarbonyltris (azanediyl)) tribenzoate benzoate and their -N and -S group derivatives of 1, 2, and 3, were investigated to observe the change in optoelectronic response of these systems. The frontier molecular orbital studies and electron affinity values indicate that the studied compounds are stable against the oxygen and moisture present in air. The calculated charge transfer integrals, electron, and hole mobility values revealed that parent DLCs and their derivatives can be employed as an effective n-type material for OLEDs; however, derivatives have enhanced charge transfer values compared with their parents. For better understanding of the thermochemistry and effect of substituents, frequency calculations were carried out. P1-D4 derivative having R = -NH-CO-CH3 terminal group came out to be theoretically the most favored having the lowest ΔG value. Computed UV/visible spectroscopic analysis showed minimum absorbance and maximum transmittance for derivative P2-D1 having -S-NH2 substituent. Molecular electrostatic potential surfaces mapped at potential range, i.e., - 8.531e-3esu to + 8.531e-3esu, describe electrophilic and nucleophilic characteristics. Introduction of electron donor groups enhanced electrical conductivity, excitation energy, and charge transfer integral, thus increasing optoelectronic properties of DLCs. However, these claims require further experimental verification.

Dynamical system based compact deep hybrid network for classification of Parkinson disease related EEG signals.

Electroencephalogram (EEG) signals accumulate the brain's spiking activities using standardized electrodes placed at the scalp. These cumulative brain signals are chaotic in nature and vary depending upon current physical and/or mental activities. The anatomy of the brain is altered when dopamine releasing neurons die because of Parkinson Disease (PD), a neurodegenerative disorder. The resulting alterations force synchronized neuronal activity in ß frequency components deep within motor region of the brain. This synchronization in the motor region affects the dynamical behavior of the brain activities, which induce motor related impairments in patient's limbs. Identification of reliable bio-markers for PD is active research area since there are no tests or scans to diagnose PD. We use embedding reconstruction, a tool from chaos theory, to highlight PD-related alterations in dynamical properties of EEG and present it as a potentially reliable bio-marker for PD related classification. We use Individual Component Analysis (ICA) to demonstrate that the strengthened synchronizations can be cumulatively collected from EEG channels over the motor region of the brain. We use this information to select the 12 EEG channels for classification of On and Off medication PD patients. Additionally, there is the strong synchronization between amplitude of higher frequency components and phase of ß components for PD patients. This information is used to improve the performance of this classification. We apply embedding reconstruction to design a new architecture of a deep neural network called Dynamical system Generated Hybrid Network. We report that this network outperforms the state of the art in terms of classification accuracy of 99.2%(+0.52%) with approximately 24% of the computational resources. Apart from classification accuracy, we use well known statistical measures like specificity, sensitivity, Matthews Correlation Coefficient (MCC), F1 score, and Cohen Kappa score for the analysis and comparison of classification performances.

Novel Penicillin Analogues as Potential Antimicrobial Agents; Design, Synthesis and Docking Studies.

A number of penicillin derivatives (4a-h) were synthesized by the condensation of 6-amino penicillinic acid (6-APA) with non-steroidal anti-inflammatory drugs as antimicrobial agents. In silico docking study of these analogues was performed against Penicillin Binding Protein (PDBID 1CEF) using AutoDock Tools 1.5.6 in order to investigate the antimicrobial data on structural basis. Penicillin binding proteins function as either transpeptidases or carboxypeptidases and in few cases demonstrate transglycosylase activity in bacteria. The excellent antibacterial potential was depicted by compounds 4c and 4e against Escherichia coli, Staphylococcus epidermidus and Staphylococcus aureus compared to the standard amoxicillin. The most potent penicillin derivative 4e exhibited same activity as standard amoxicillin against S. aureus. In the enzyme inhibitory assay the compound 4e inhibited E. coli MurC with an IC50 value of 12.5 µM. The docking scores of these compounds 4c and 4e also verified their greater antibacterial potential. The results verified the importance of side chain functionalities along with the presence of central penam nucleus. The binding affinities calculated from docking results expressed in the form of binding energies ranges from -7.8 to -9.2kcal/mol. The carboxylic group of penam nucleus in all these compounds is responsible for strong binding with receptor protein with the bond length ranges from 3.4 to 4.4 Ǻ. The results of present work ratify that derivatives 4c and 4e may serve as a structural template for the design and development of potent antimicrobial agents.