Nacéra TADJINE

Author: Nacéra TADJINE

Laboratory of Biotechnology of Medicinal and Aromatic Plants, Blida-1 University, Algeria

naceratadjine@yahoo.fr

Abstract

The study aims to determine the chemical composition of the ethanolic extract of Z. lotus L. leaves, as well as to evaluate its effectiveness in combating Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae), a pest of stored products. Rutin, robin, kaempferol, and apigenin have been identified as the main constituents of Z. lotus L. The tested ethanolic extract showed pronounced insecticidal activity against this harmful species, proportionally to the applied doses. The ethanolic extract of Z. lotus L. demonstrated high efficacy in the various treatments tested on T. castaneum. Regarding contact and fumigation assessments, the ethanolic extract of Z. lotus L. induced corrected mortality rates ranging from 37.5% to 100% in T. castaneum, with corresponding lethal concentrations (LC50) of 13.9 µl/ml and 16.9 µl/l of air, respectively, during contact and fumigation evaluations. Our results indicate that the ethanolic extract of Z. lotus L. exhibits very promising insecticidal activity against T. castaneum. The topical toxicity recorded ID10 and ID50 values of 1.45 and 2.77 µg per adult. These results clearly show that the ethanolic extract of Z. lotus L. has great potential for the development of new botanical insecticides as safe alternatives for controlling harmful insects.

Durum wheat is a strategic crop for food security in semi-arid regions such as North Africa, where climate variability increasingly threatens agricultural productivity. Multi-trait selection represents a valuable approach to developing superior genotypes capable of maintaining high performance under such challenging conditions. This study aimed to compare the efficiency of traditional and modern multi-trait selection indices in identifying elite durum wheat genotypes. A total of 59 genotypes were evaluated under field conditions in Sétif, Algeria. Ten traits related to plant growth and agronomic performance were assessed, and a 15% selection intensity was applied. The classical Smith–Hazel (SH) selection index was implemented under two scenarios—retaining multicollinearity (SH_1) and removing multicollinearity (SH_2)—and compared with two modern ideotype-based indices: the Factor Analysis and Ideotype Design-Based BLUP (FAI-BLUP) and the Multi-Trait Genotype Ideotype Distance Index (MGIDI). Among the four indices, the MGIDI and FAI-BLUP achieved the highest total predicted selection gains (36.28%), substantially outperforming SH_2 (30.76%) and especially SH_1 (–35.98%), which was negatively affected by multicollinearity. Modern indices promoted substantial gains in key productivity-related traits, including biological yield (5.64%), grain yield (5.49%), and straw yield (15.1%). Notably, genotypes G42, G10, G26, and G4 were consistently selected across the MGIDI, FAI-BLUP, and SH_2, confirming their superior multi-trait performance and breeding potential. In contrast, SH_1 yielded inconsistent and negative results, highlighting the limitations of applying traditional indices without accounting for multicollinearity. These findings confirm the robustness, efficiency, and practical value of ideotype-based indices—particularly the MGIDI and FAI-BLUP—for multi-trait selection in durum wheat breeding programs, especially under semi-arid environmental conditions.

The integration of Internet of Things (IoT) technologies in agriculture has advanced precision farming by enabling real-time monitoring and data-driven decision-making. However, the growing reliance on interconnected sensors introduces challenges such as cybersecurity risks, sensor failures, and data irregularities that can threaten operational reliability. This study presents an IoT-based anomaly detection framework designed to enhance the security and efficiency of smart agriculture systems. The approach employs unsupervised machine learning techniques, specifically a hybrid of Principal Component Analysis (PCA) and Isolation Forest for detecting anomalies in environmental sensor data. A publicly available smart agriculture dataset containing diverse parameters like soil moisture, temperature, humidity, and light intensity was used. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the PCA + Isolation Forest model achieved a high accuracy of 98.2% and a recall of 99.4%, indicating its effectiveness in detecting true anomalies while minimizing false negatives. This performance surpasses that of standalone models such as PCA, Isolation Forest, and One-Class SVM. The proposed framework is computationally efficient and well-suited for resource-constrained IoT environments commonly found in agricultural settings. By effectively identifying data irregularities, this approach enhances the security, reliability, and operational integrity of smart farming systems, making it a practical solution for supporting sustainable and secure precision agriculture.

Molecular breeding and gene function studies in plants require high transformation efficiency. Agrobacterium-mediated transformation has contributed significantly to molecular research in many plants, but is inefficient and inconsistent in rice that do not host Agrobacterium. Transformation efficiency in rice remains low. Therefore, this study aimed to establish a simple and efficient transformation method for rice using Agrobacterium. Two foreign genes (CISP1-GFP and CISP2-GFP) and a Agrobacterium strain (EHA105) were used in the experiments. Then, Agrobacterium infection of rice seeds that had absorbed water and germinated under reduced pressure infiltration conditions showed that an average of 14% of the seeds formed after growth (12% with CISP1-GFP and 16% with CISP2-GFP) carried the foreign gene, and it was also confirmed by PCR, Western blot, GFP fluorescence and TAIL-PCR. Since this method does not involve callus formation or re-differentiation of rice plants, no special equipment or complicated operations are required, and transformants can be obtained in only three months. Therefore, this method is expected to simplify rice genetic manipulation and promote molecular breeding of rice.

In large-scale poultry farming, respiratory diseases affect the health of chickens, leading to a decline in the quality and yield of both meat and eggs. Effective monitoring of these diseases is crucial to reducing their impact and enhancing the quality and yield. Currently, most monitoring methods still rely on manually monitoring chicken vocalizations, which are time-consuming, labor-intensive, and require specialized personnel. Existing smart methods are often limited to laboratory environments where individual chickens are monitored separately. These approaches do not meet the industrial and commercial requirements of poultry farms, where a diverse set of complex auditory signals may be captured. These signals include not only chicken vocalizations but also complex noises from cages, human activities, mechanical ventilation systems, and other background noises.

In this study, we design a deep learning-based intelligent recognition method capable of accurately distinguishing abnormal chicken vocalizations among complex sound signals. Our proposed framework is based on wavelet scattering transform (WST) and Long Short-Term Memory (LSTM) network, and the use of preprocessed chicken vocalizations through a deep denoising scheme, adopting an audio image generation model (AIGM). We have used a public chicken language dataset consisting of a total of segments for each of the three categories (Healthy, Sick, None - no chicken sound), totaling 6,000 five-second audio clips from actual farming environments, which were labeled by veterinary experts. Promising robust performances are achieved by the proposed method outperforming the state-of-the-art methods for detecting poultry respiratory diseases, and enabling poultry personnel to accurately determine the health and well-being of the chickens.

Wetlands are vital ecosystems as they play a key role in agriculture, providing essential resources such as water for crops, livestock, and aquaculture, while also serving as habitats for a diverse range of species, particularly wild birds. Our study focuses on the Sidi Moussa–Oualidia wetland complex, integrating Sentinel-1 and Sentinel-2 imagery with spectral indices, radar backscatter (VV/VH ratio), and topographic features to classify land cover and monitor surface water quality. A Random Forest classifier optimized via Recursive Feature Elimination (RFE) achieved 91% accuracy and a macro F1-score of 0.90 across six classes, including permanent water, salt marshes, artificial marshes, hypersaline zones, oyster farming areas, and others. Surface water quality was also evaluated using the turbidity index as a proxy, revealing notable spatial degradation near aquaculture and agricultural activity hotspots. Our first-of-its-kind study in the Moroccan context proposes a scalable, reproducible methodology for simultaneous wetland land cover mapping and water quality monitoring, reinforcing the importance of remote sensing for integrated wetland-agriculture management in data-limited regions.

The ability of proximal hyperspectral sensors to capture precise spectral measurements, which signify the inherent properties of the target material, presents a strong potential for accurately estimating key crop health indicators in precision agriculture. This study employs a hybrid methodology that integrates a physical process-based radiative transfer (RT) model and machine learning regression to assess three key wheat crop traits: leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC). The non-imaging hyperspectral data collected proximally using the ASD FieldSpec Spectroradiometer were spectrally resampled to 269 spectral bands ranging from 400 to 1000 nm for the retrieval of these crop traits. A hybrid retrieval workflow was developed using a Gaussian process regression algorithm, an active learning method for reducing sample size, principal component analysis for spectral dimensionality reduction, and training with spectral simulations from the PROSAIL RT model. Upon validating against in-situ measurements, good accuracies in terms of NRMSE values, 10.65%, 11.63%, and 13.85%, were achieved for LAI, LCC, and CCC, respectively. Plot-wise maps showing the spatial variability of LAI, LCC, and CCC, along with their uncertainties, were also generated to visualize the prediction results. These optimised retrieval models facilitate operational delivery of critical variables for monitoring crop dynamics by facilitating efficient nutrient management practices.

Unpredictable weather conditions, infestation of pests, and infection with diseases are common problems in bitter gourd fruits when still attached to the mother plants, which affect the seedling vigor. Thus, it is a big problem for farmers to face. Fruit storage under ambient conditions may alleviate this problem. A study was conducted on the fruit storage (0, 2, 4, and 6 days) of bitter gourd (half-ripe stage) under ambient conditions (temperature: 28 ± 2 °C and relative humidity: 65 ± 5%). It was conducted to evaluate the impact of fruit storage on the enhancement of bitter gourd's seedling vigor. A completely randomized design with four replicates was used. Based on the results, 4 days of fruit storage significantly improved the seedling vigor index (SVI), and time beyond this period did not contribute to a higher SVI. Likewise, this fruit-storage period produced better growth for shoot, root, and seedling lengths as compared to lower and higher periods as well as unstored fruits. Moreover, a higher root count per seedling and seedling fresh weight was observed in 4-day fruit storage. Conversely, 6-day fruit storage exhibited a higher chlorophyll content (SPAD index) as compared to shorter storage periods and unstored fruits. A 4-day fruit-storage period may be recommended to the farmers to obtain vigorous bitter gourd seedlings.

Plant height is one of the key agronomic traits used as a variable to model vegetative and developmental growth in crops. It is one of the most salient yet important yield-associated traits that could be utilized in breeding against lodging effects in the maize breeding program. A RIL subpopulation derived from the cross between parental lines, CML52 x B73, from a nested association mapping (NAM) population genotyped with SNP markers for approximately thirty-seven (37) traits was used to identify QTL for plant height. A genetic linkage covering all 10 chromosomes was constructed, and the QTLs affecting plant height were mapped and analyzed by single marker analysis (SMA), interval mapping (IM), and composite interval mapping (CIM) at a LOD score of 3.11, 3.14, and 6.11, respectively. Six (6) QTLs for plant height detected were located at different regions of five chromosomes (2, 6, 8, 9, and 10). The results showed that there were different effect values of QTL on plant height estimated by the different techniques. The percentage of phenotypic variation of single-QTL to plant height varied from 6.49% to 10.24% using IM and 4.14% to 9.16% using CIM. The multiple-QTL estimated an overall phenotypic variation of 17.25% using IM and 22.07% using CIM. Since the CIM detected the highest number of putative QTLs, it can be widely adopted for QTL mapping analysis. The two QTLs, c9.loc93 (chr.9 pos. 93), being consistently identified by most of the techniques, and PZA03728.1 (chr.10 pos. 65.1), having the highest phenotypic variation (10.24%), could be considered in a maize breeding program to lower the lodging effect on taller maize plants.

Educational Tools in Herbal Medicine: A Streamlit-Based AI Decision Tree Classifier for South Indian Medicinal Herb Identification ("PLANTIFY")

J.Jaydish , G.Chelladurai

Department of Botany, St. Joseph’s College (Autonomous), Tiruchirappalli, TamilNadu, India

Corresponding author Email:jaydishkennedy@gmail.com

Abstract:

The application of emerging artificial intelligence, particularly the decision tree classifier algorithm, enables the accurate identification and classification of plant species for herbal south Indian medicinal herbs that are vital to traditional systems like Ayurveda and Siddha. However, identifying these herbs is challenging due to their complex morphology and limited taxonomic resources. To address this, we developed PLANTIFY, a web-based app using streamlit and a decision tree classifier trained on eight key morphological traits. The model identifies 100 South Indian herbs with 92.5% accuracy using 5-fold cross-validation. The app provides species predictions with confidence scores, detailed taxonomy, ethnobotanical uses, and downloadable PDF reports. A usability study found 90% of users rated the app as user-friendly. PLANTIFY bridges traditional knowledge with AI, promoting herbal education and preserving ethnobotanical heritage. For research into and the identification of plant species for taxonomical purposes, this emerging technology is more convenient and innovative.

Keywords:

morphological trait; tensor flow; streamlit; Ayurveda; plant identification