Each strawberry (Fragaria × ananassa) cultivar has its own growth and yield characteristics. However, the characteristics of many cultivars have not been determined at a consistent time and place, making direct comparative analysis difficult. The objective of this study was to identify characteristics and trends of five Korean strawberry cultivars in the same environment during an entire season. Therefore, environmental factors such as daily average air temperature, daily average relative humidity, daily average solar radiation, daily soil temperature, daily soil water content, daily soil electrical conductivity (EC), plant growth characteristics such as the number of leaves, plant height, leaf length, leaf width, and crown diameter, and productivity characteristics such as flowering and fruiting were measured to investigate the possible correlations of the data over one season. The vegetative growth of “Seolhyang” and “Keumsil” was greater than that of “Jukhyang” and “Maehyang”. The yield of “Arihyang” was greater than that of all other cultivars. “Arihyang” also presented the greatest weight per number of fruits. Among environmental factors, higher variability in air temperature and soil water content was correlated to lower total fresh weight in the following week at different degrees for each cultivar. Among the cultivars, the time to the first flowering was delayed by about seven days when the number of leaves increased by one and was reduced by one day per 1 cm increase in plant height. The total fresh weight was enhanced up to 271 g per experimental unit, while the average number of leaves increased by one. The results indicate that the data can be used by those who need information regarding the characteristics of the strawberry cultivars through direct comparative analysis.

Supramolecular polymers are a class of macromolecules stabilized by weak non‐covalent interactions. These self‐assembled aggregates typically undergo stimuli‐induced reversible assembly and disassembly. They thus hold great promise as so‐called functional materials. In this work, we present the design, synthesis, and responsive behavior of a short supramolecular oligomeric system based on two hetero‐complementary subunits. These “monomers” consist of a tetrathiafulvalene‐functionalized calix[4]pyrrole (TTF‐C[4]P) and a glycol diester‐linked bis‐2,5,7‐trinitrodicyanomethylenefluorene‐4‐carboxylate (TNDCF), respectively. We show that when mixed in organic solvents, such as CHCl₃, CH₂ClCH₂Cl, and methylcyclohexane, supramolecular aggregation takes place to produce short oligomers stabilized by hydrogen bonding and donor–acceptor charge‐transfer (CT) interactions. The self‐associated materials were characterized by ¹H NMR and UV/Vis/NIR absorption spectroscopy, as well as by concentration‐ and temperature‐dependent absorption spectroscopy and dynamic light scattering (DLS) analyses of both the monomeric and oligomerized species. The self‐associated system produced from TTF‐C[4]P and TNDCF exhibits a concentration‐dependent aggregation behavior typical of supramolecular polymers. Further support for the proposed self‐assembly came from theoretical calculations. The fluorescence emitting properties of TNDCF are quenched under conditions that promote the formation of supramolecular aggregates containing TTF‐C[4]P and TNDCF. This quenching effect has been utilized as a probe for the detection of substrates in the form of anions (i.e., chloride) and nitroaromatic explosives (i.e., 1,3,5‐trinitrobenzene). Specifically, the addition of these substrates to mixtures of TTF‐C[4]P and TNDCF produced a fluorescence “turn‐on” response.

Binding of a porphyrin carboxylate anion (1) to tetrathiafulvalene calix[4]pyrrole (TTF-C4P) results in electron transfer from TTF-C4P to Li⁺@C₆₀ to produce the charge-separated state (1/TTF-C4P˙⁺/Li⁺@C₆₀˙⁻) in benzonitrile. Upon photoexcitation of 1, photoinduced electron transfer from the triplet excited state of 1 to TTF-C4P˙⁺ occurs to produce the higher energy charge-separated state (1˙⁺/TTF-C4P/Li⁺@C₆₀˙⁻), which decays to the ground state with a lifetime of 4.8 μs.

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.

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.

Image analysis is a useful technique to evaluate the efficacy of a treatment for weed control. In this study, we address two practical challenges in the image analysis. First, it is challenging to accurately quantify the efficacy of a treatment when an entire experimental unit is not affected by the treatment. Second, RGB codes, which can be used to identify weed growth in the image analysis, may not be stable due to various surrounding factors, human errors, and unknown reasons. To address the former challenge, the technique of image segmentation is considered. To address the latter challenge, the proportion of weed area is adjusted under a beta regression model. The beta regression is a useful statistical method when the outcome variable (proportion) ranges between zero and one. In this study, we attempt to accurately evaluate the efficacy of a 35% hydrogen peroxide (HP). The image segmentation was applied to separate two zones, where the HP was directly applied (gray zone) and its surroundings (nongray zone). The weed growth was monitored for five days after the treatment, and the beta regression was implemented to compare the weed growth between the gray zone and the control group and between the nongray zone and the control group. The estimated treatment effect was substantially different after the implementation of image segmentation and the adjustment of green area.