Search

Machine learning has been used in various areas, but there are few studies on price prediction for agricultural products. Here, a machine learning technique for the price prediction of tomato and apple fruits was attempted based on environment and price data for 12 years. The goal of this study is to discover 1) how much can we accurately predict the product prices with the environmental factors and 2) how much each environmental factor affects to the product prices. This study assumes that the environmental factors directly affect crop growth and thus indirectly determine fruit production and accompanying price. In addition, it is assumed that there are two kinds of time lags, between the change in the environmental factors and their effects on the crop growth, and between the change in the crop growth and its effect on the price. In the process, machine learning techniques were used instead of econometric models commonly used in agricultural economics. The relationship between the environmental factors and fruit price with varying time lags in data-driven manner using long short-term memory (LSTM) was modeled in this study. The study empirically revealed that there are suitable time lags between the environmental factors and fruit price in the price prediction, and taking these time lags into the prediction improves the accuracy. Moreover, the importance of each of the environmental factors on the price using shapely additive explanations (SHAP) was demonstrated though this study, which assists the decision-making process in agriculture against the climate change.

ABSTRACT: β-Lactam antibiotics are one of the most clinical importance in human and veterinary medicine because they are used for both preventive and therapeutic purposes against several gram-positive, gram-negative, and anaerobic organisms. In this study, it was confirmed that β-lactams (81.1%) were found to be significantly prescribed the most among 74 farms in 5 integrated broiler operations, and single prescription (84.6%), 2-day (41.5%) or 3-day (40.0%) administration, and 15 to 22 d of age (67.7%) administration was significantly higher in the farms (P < 0.05). Among the E. coli isolated from 74 farms in 5 integrated broiler operations, β-lactam-resistant E. coli isolates were detected more frequently in fecal sample (94.6%) than in dust sample (60.8%) (P < 0.05). The prevalence of MDR in β-lactam-resistant isolates, ranging from 88.1 to 96.5%, was significantly higher than that in non–β-lactam-resistant isolates (P < 0.05), without significant differences among operations. Of 466 β-lactam-resistant isolates, 432 (92.7%) isolates harbored β-lactamase genes. The non–extended-spectrum β-lactamase (ESBL) gene blaTEM-1 (81.8%) showed the highest prevalence among isolates, followed by the non-ESBL gene blaTEM-135 (6.4%) (P < 0.05). Five ESBL genes, SHV-12, OXA-1, CTX-M-27, CTX-M-55, and CTX-M-65, were found in 0.9 to 6.0% of the isolates. The pAmpC gene blaCMY-2 was detected in 17 isolates (3.6%). These results suggest that feces and dust are important reservoirs of antimicrobial-resistant bacteria, highlighting the need to strengthen farm management regulations, such as cleaning, disinfection, and litter disposal and to reduce the use of antibiotics in broiler operations.

The receptacle of strawberry is a more direct part than the flower for predicting yield as they eventually become fruits. Thus, we tried to predict the yield by combining an AI technique for receptacle detection in images and statistical analysis on the relationship between the number of receptacles detected and the strawberry yield over a period of time. Five major cultivars were cultivated to consider the cultivar characteristics and environmental factors for two years were collected to consider the climate difference. Faster R–CNN based object detector was used to estimate the number of receptacles per strawberry plant in given two-dimensional images, which achieved a mAP of 0.6587 for our dataset. However, not all receptacles appear on the two-dimensional images, and Bayesian analysis was used to model the uncertainty associated with the number of receptacles missed by the AI. After estimating the probability of fruiting per receptacle, prediction models for the total strawberry yield at the end of harvest season were evaluated. Even though the detection accuracy was not perfect, the results indicated that counting the receptacles by object detection and estimating the probability of fruiting per receptacle by Bayesian modeling are more useful for predicting the total yield per plant than knowing its cumulative yield during the first month.

The purpose of this study was to identify the genetic environment of optrA gene in linezolid (LZD)-resistant Enterococcus faecalis from chicken meat and to describe the probable mechanism of dissemination of the optrA gene through plasmid or chromosomal integration. Whole genome sequencing and analysis revealed that all 3 E. faecalis isolates confirmed as LZD- and chloramphenicol-resistant carried fexA adjacent to the optrA gene as well as a variety of resistance genes for macrolides, tetracyclines, and aminoglycosides, simultaneously. But, the other genes conferring LZD resistance, cfr and poxtA, were not detected in those strains. Two isolates harboring the optrA gene in their chromosomal DNA showed >99% similarity in arrangement to the transposon Tn6674 and the transposase genes, tnpA, tnpB, and tnpC and were located in the first open reading frame for transposase. One isolate harboring an optrA-carrying plasmid also showed >99% similarity with the previously reported pE439 plasmid but had 2 amino acid changes (Thr96Lys and Tyr160Asp) and a higher minimum inhibitory concentration against LZD of 16 mg/L than that of pE439 (8 mg/L). Mobile genetic elements such as transposons or plasmids flanking the optrA gene conduct a crucial role in the dissemination of antimicrobial resistance genes. Further investigations are required to identify the way by which optrA is integrated into chromosomal DNA and plasmids.

Linezolid is an oxazolidinone class antibiotic used for treatment infections caused by various multidrug-resistant gram-positive pathogens including enterococci. However, recently, linezolid-resistant isolates in animals are considered as a human health hazard. In a broiler operation system, antimicrobial resistance can be transferred to the environment and commercial broiler via the fecal–oral route. Therefore, this study was conducted to investigate the prevalence and characteristics of linezolid-resistant Enterococcus faecalis (E. faecalis) from broiler parent stock in a broiler operation system. Among 297 E. faecalis isolates from 85 flocks in 8 broiler breeder farms, the prevalence of chloramphenicol- and linezolid-resistant isolates was 0 to 12.1% and 0 to 8.0%, respectively; however, there were no significant differences between farms. Therefore, a total of 14 (4.7%) chloramphenicol- and/or linezolid-resistant E. faecalis showed resistance to 7 or more antimicrobial classes. The drug-resistance gene optrA, which can confer resistance to linezolid, tedizolid, and phenicols, was found in 8 (2.69%) isolates, and 7 (2.36%) of the 8 optrA-positive isolates co-carried the phenicol exporter gene fexA. However, E. faecalis isolates from 3 of 8 broiler breeder farms only carried the optrA and/or fexA genes. As linezolid is one of the last antimicrobial treatments of choice for multidrug-resistant gram-positive pathogens including E. faecalis, the presence of antibiotic-resistant E. faecalis in broiler breeder farms should be monitored to prevent the introduction of linezolid-resistant strains to the food chain.