CMB_2026v16n2

Computational Molecular Biology 2026, Vol.16, No.2, 129-145 http://bioscipublisher.com/index.php/cmb 144 Lin Y., Fang S., Kang L., Chen C., Yao M., and Kuo B., 2024, Combining recurrent neural network and sigmoid growth models for short-term temperature forecasting and tomato growth prediction in a plastic greenhouse, Horticulturae, 10(3): 230. https://doi.org/10.3390/horticulturae10030230 Liu H., Shao M., and Yang L., 2023, Photosynthesis characteristics of tomato plants and its’ responses to microclimate in new solar greenhouse in North China, Horticulturae, 9(2): 197. https://doi.org/10.3390/horticulturae9020197 Liu H., Zhao H., Liu S., Tian Y., Li W., Wang B., Hu X., Sun D., Wang T., Wu S., Wang F., Zhu N., Tao Y., and Lei X., 2025, When tomatoes hit the winter: A counterattack to overwinter production in soft-shell solar greenhouses in North China, Horticulturae, 11(4): 436. https://doi.org/10.3390/horticulturae11040436 Locatelli S., Barrera W., Verdi L., Nicoletto C., Marta D., and Maucieri C., 2024, Modelling the response of tomato on deficit irrigation under greenhouse conditions, Scientia Horticulturae, 325: 112770. https://doi.org/10.1016/j.scienta.2023.112770 Miller G., Beery A., Singh P., Wang F., Zelingher R., Motenko E., and Lieberman-Lazarovich M., 2021, Contrasting processing tomato cultivars unlink yield and pollen viability under heat stress, AoB Plants, 13(4): plab046. https://doi.org/10.1093/aobpla/plab046 Nassar J., Khan S., Villalva D., Nour M., Almuslem A., and Hussain M., 2018, Compliant plant wearables for localized microclimate and plant growth monitoring, npj Flexible Electronics, 2(1): 1-12. https://doi.org/10.1038/s41528-018-0039-8 Nițu O., Ivan E., and Arshad A., 2025, Optimizing microclimatic conditions for lettuce, tomatoes, carrots, and beets: Impacts on growth, physiology, and biochemistry across greenhouse types and climatic zones, International Journal of Plant Biology, 16(3): 100. https://doi.org/10.3390/ijpb16030100 Odah K., Houetohossou S., Houndji V., and Kakaï R., 2025, Machine learning techniques for tomato yield prediction: A comprehensive analysis, Smart Agricultural Technology, 11: 101067. https://doi.org/10.1016/j.atech.2025.101067 Ogunlowo Q., Akpenpuun T., Na W., Rabiu A., Adesanya M., Addae K., Kim H., and Lee H., 2021, Analysis of heat and mass distribution in a single- and multi-span greenhouse microclimate, Agriculture, 11(9): 891. https://doi.org/10.37473/dac/10.3390/agriculture11090891 Park B., Jeong H., Yang E., Kim M., Kim J., Chae W., Lee O., Kim S., and Kim S., 2023, Differential responses of cherry tomatoes (Solanum lycopersicum) to long-term heat stress, Horticulturae, 9(3): 343. https://doi.org/10.3390/horticulturae9030343 Peng X., Yu X., Luo Y., Chang Y., Lu C., and Chen X., 2023, Prediction model of greenhouse tomato yield using data based on different soil fertility conditions, Agronomy, 13(7): 1892. https://doi.org/10.3390/agronomy13071892 Rajametov S., Yang E., Jeong H., Cho M., Chae S., and Paudel N., 2021, Heat treatment in two tomato cultivars: A study of the effect on physiological and growth recovery, Horticulturae, 7(5): 119. https://doi.org/10.3390/horticulturae7050119 Rezvani S., Abyane H., Shamshiri R., Balasundram S., Dworak V., Goodarzi M., Sultan M., and Mahns B., 2020, IoT-based sensor data fusion for determining optimality degrees of microclimate parameters in commercial greenhouse production of tomato, Sensors, 20(22): 6474. https://doi.org/10.3390/s20226474 Ro S., Chea L., Ngoun S., Stewart Z., Roeurn S., Theam P., Lim S., Sor R., Kosal M., Roeun M., Dy K., and Prasad P., 2021, Response of tomato genotypes under different high temperatures in field and greenhouse conditions, Plants, 10(3): 449. https://doi.org/10.3390/plants10030449 Šalagovič J., Vanhees D., Verboven P., Holsteens K., Verlinden B., Huysmans M., Van De Poel B., and Nicolaï B., 2024, Microclimate monitoring in commercial tomato (Solanum lycopersicumL.) greenhouse production and its effect on plant growth, yield and fruit quality, Frontiers in Horticulture, 3: 1425285. https://doi.org/10.3389/fhort.2024.1425285 Sato S., Kamiyama M., Iwata T., Makita N., Furukawa H., and Ikeda H., 2006, Moderate increase of mean daily temperature adversely affects fruit set of Lycopersicon esculentumby disrupting specific physiological processes in male reproductive development, Annals of Botany, 97(5): 731-738. https://doi.org/10.1093/aob/mcl037 Sellami D., and Kooli S., 2026, Physiological and growth responses of tomato plants to heat stress, Discover Plants, 3(1): 1-15. https://doi.org/10.1007/s44372-025-00462-3 Shan Z., Chen J., Zhang X., Si Z., Yi R., and Fan H., 2025, Optimizing irrigation and nitrogen application for greenhouse tomato using the DSSAT-CROPGRO-Tomato model, Water, 17(3): 426. https://doi.org/10.3390/w17030426 Sharaf-Eldin M., Yaseen Z., Elmetwalli A., Elsayed S., Scholz M., Al-Khafaji Z., and Omar G., 2023, Modifying walk-in tunnels through solar energy, fogging, and evaporative cooling to mitigate heat stress on tomato, Horticulturae, 9(1): 77. https://doi.org/10.3390/horticulturae9010077

RkJQdWJsaXNoZXIy MjQ4ODYzNA==