Scientia Agropecuaria

Nematophagous fungi for integrated management of Meloidogyne (Tylenchida): a review of taxonomic diversity, mechanisms of action and potential as biological control agents

Carlos J. Villarreal-Pérez — 2025-08-08

Root-knot nematodes (RKNs) are classified under the genus Meloidogyne and are among the most devastating pests affecting strategical agricultural crops. They attack a wide variety of plant species, including vegetables, fruit trees and ornamental plants, causing root deformities and even lead to plant death in severe cases of infestation. These nematodes contribute to substantial crop yield loss and affect the quality of harvested products. Although synthetic nematicides are available for the control of these pest organisms, there is a growing emphasis on exploring sustainable and eco-friendly alternatives, such as nematophagous fungi like the genera Purpureocillium, Arthrobotrys, Dactylellina, Orbilia, and Trichoderma, among others. Here a review of literature on the matter is given, with a focus on the taxonomic classification of the most relevant fungal orders and genera, their diagnostic features, mechanisms of action, and potential as biological control agents (BCAs) against Meloidogyne species. Other relevant aspects addressed in this review include a brief description of the nematode genus Meloidogyne, along with the symptoms it causes in host plants, such as root gall formation, stunted growth, and yellowing of foliage, among others. It also describes integrated pest management (IPM) strategies such as crop rotation, resistant crops, soil solarization, trap crops, as well as currently used chemical control techniques. Biological control alternatives are also presented with particular emphasis on nematophagous fungi. Future research should focus on improving the formulae of biological agents based on nematophagous fungi under field conditions and understanding their ecological roles and interactions in soil microbiomes.

Balancing accuracy, interpretability, and stability in machine-learning models: Live-weight prediction of Andean sheep from morphometric traits

Jordan Ninahuanca — 2025-08-08

The objective of this research was to predict the live weight of Corriedale lambs using morphological measurements and machine learning algorithms. A total of 291 five-month-old lambs from the Corpacancha Production Unit of SAIS PACHACÚTEC SAC were used. These animals represented a homogeneous group in terms of age, sex, and genetics, as they belonged to the Corriedale breed and were offspring of "Category A" ewes. Morphological measurements recorded included Body Length (BL), Withers Height (WH), Thoracic Girth (TG), Rump Width (RW), Abdominal Girth (AG), Cannon Bone Length (CBL), Chest Depth (CD), and Live Weight (LW). The models evaluated were Multiple Linear Regression, Ridge Regression, Decision Trees, Random Forest, and XGBoost. The comparative analysis of the machine learning models identified ModG and Ridge as the most accurate and stable options, standing out for their low Mean Squared Error (MSE = 0.083) and Root Mean Squared Error (RMSE ≈ 0.287 – 0.288). Additionally, they exhibited the highest coefficients of determination (R²= 0.89, R_Adj²= 0.88), indicating excellent predictive capability and data fit. Their low coefficient of variation (CV%) confirms their stability, establishing them as the best choices for applications where precision is paramount, such as predicting critical values in production processes and high-demand scientific studies. While XGBoost proved to be a robust alternative with an MSE of 0.119, an RMSE of 0.345, and a relative error of 2.22%. These findings confirm that prioritizing models that balance accuracy, interpretability, and stability enable faster, data-driven decision-making in Corriedale sheep production. Such an approach optimizes feed allocation, classifies lambs by market weight, and promptly detects growth deviations, thereby improving overall flock profitability.

Applying artificial intelligence in durian fertile lobe detection: Attention-Residual Unet and Test Time Augmentation algorithm

Thanh Tung Luu — 2025-08-08

The key factor in durian fruit trading is ripeness. Several studies have been conducted on non-destructive durian maturity classification using near-infrared (NIR) spectroscopy. However, most of these studies manually determined the most accurate measurement position, which was the durian's main fertile lobe center. This research aims to automate the stage of detecting this position of the durian by using UNet segmentation method, which leverages differences in rind texture between the center of the main fertile lobe and other areas (lobe grooves and stems), prior to conducting NIR measurements. The rough and non-uniform surface of the durian rind presents a significant challenge for segmentation. However, the large size of the durian spines in the main fertile lobe serves as an identification characteristic for the segmentation model. This study uses the Ri-6 durian in Vietnam as the samples for the experiment. The model was developed using three architectures: Unet, Attention-Unet and Attention-Residual Unet. According to the analysis results on test set, Unet, Attention-Unet and Attention-Residual Unet algorithms achieved %accuracy of 78.22%, 81.34%, 82.89% and %intersection over union of 79.49%, 80.47%, 80.72%, respectively. After that, the model was further enhanced using the test time augmentation algorithm, improving the %accuracy to 85.24%, 85.68%, 86.85% and %IoU to 81.65%, 82.03% and 83.12%. Among the three architectures, the Attention-Residual-Unet model demonstrated the highest efficiency in detecting the center of the durian’s main fertile lobe for non-destructive durian maturity classification. This method can be applied to the development of an automatic durian’s maturity classification machine, which would save time and improve economic efficiency.

Morphological characterization, molecular identification, and phylogenetic analysis of Lasiodiplodia theobromae associated with CCN-51 cacao plants in Ecuador

José Humberto Vera-Rodríguez — 2025-08-08

Necrotrophic fungi are pathogens that cause tissue death in plants, which negatively impacts their growth and productivity. This study focused on identifying the presence of Lasiodiplodia theobromae in CCN-51 cacao plants in the Simón Bolívar canton in Ecuador. We sampled cacao pods exhibiting necrotic lesions and obtained fungal isolates for morphological and molecular characterization. Techniques, such as culturing on selective media, microscopy, and DNA sequencing were used to confirm the fungal identity. We compared our results with international databases and assessed the genetic variability of the isolates. Morphological characterization placed the fungal isolates within the family Botryosphaeriaceae, and molecular analysis using ITS and EF1-α regions confirmed the species as Lasiodiplodia theobromae, with 100% DNA quality for amplicon analysis and 100% sequence similarity in GenBank. We constructed phylogenetic trees using maximum likelihood methods, which revealed high genetic similarity and recent divergence among sequences despite their varied geographic origins. The fungal isolates specifically confirmed the presence of L. theobromae as the causal agent of necrotic lesions in CCN-51 cacao pods from Simón Bolívar. These findings underscore the importance of studying necrotrophic fungi in cacao plants to inform control strategies, improve crop resistance, and support sustainable production, essential to the global cacao trade.

Antifungal activity of plant extracts against Botrytis cinerea, Lasiodiplodia theobromae, and Fusarium sp.: Effectiveness in controlling Erysiphe necator and phytotoxic effect on wheat seeds

Hanna Cáceres Iparraguirre — 2025-08-08

The agricultural sector faces risks from damage caused by phytopathogens, and many farmers rely on synthetic fungicides to combat them. However, excessive use of these products pollutes the environment and promotes pathogen resistance. This study evaluated the mycelial growth inhibition of 57 plant extracts against Botrytis cinerea, Lasiodiplodia theobromae, and Fusarium sp., and their efficacy in controlling Erysiphe necator in the field. It also evaluated their phytotoxic effect on wheat seeds and the identification of metabolites present in the extracts. The most effective extracts were those of Ambrosia artemisiifolia, Conyza sumatrensis, Dysphania ambrosioides, Minthostachis mollis, Salvia sp., Pimpinella anisum, and Syzygium aromaticum. The P. anisum extract exhibited the greatest inhibition of B. cinerea growth in tomatoes, while the most effective extracts against E. necator were P. anisum, C. sumatrensis, and S. aromaticum. Furthermore, the A. artemisiifolia extract exhibited phytotoxic effects on wheat seed growth. Flavonoids, tannins, steroids, triterpenoids, alkaloids, leucoanthocyanidins, coumarins, and saponins were identified as the main metabolites in the extracts. These results offer viable alternatives for controlling phytopathogenic fungi using plant extracts, contributing to a more sustainable agriculture that is less dependent on chemicals.