Abstract
This work describes the process of design, construction, and installation of a remote monitoring system based on the ESP8266 microcontroller, to record environmental and production information during the development of the smooth green chayote (Sechium edule var. virens levis) plant and fruit. This process involved integration of hardware and software technologies; it began with the construction and installation of probes for recording the weight of the biomass of the fruit in the production of chayote on aerial structure used for that purpose, which has a configuration of 8 m x 8 m (4 square sections of 4 meters on each side, formed by 9 support poles). The environmental data collected included: dry bulb temperature, relative humidity, and light exposure; while, for biomass weight, it was determined to generate a schedule to track the variation of its development, which allows observing growth and pruning of the plant (it is intended that this information will help to accurately estimate the optimal time to harvest fruits). This constant monitoring system will provide relevant information to predict the yield of this crop, in addition to identify causes that limit it so they can be corrected in time. The information obtained can help growers to make decisions (irrigation, agricultural inputs, labor, harvesting time); in addition, the technology used can be extrapolated to other vegetables.
References
Arduino SA. (2024). Arduino Cloud (Version 1.0) [Software]. https://cloud.arduino.cc/
Avendaño-Arrazate, C., Cadena-Iñiguez, J., Arévalo-Galarza, M., Campos-Rojas, E., Cisneros-Solano, V., & Aguirre-Medina, J. (2010). Las variedades del chayote mexicano, recurso ancestral con potencial de comercialización. México: Grupo Interdisciplinario de Investigación en Sechium edule en México, AC. https://www.gob.mx/cms/uploads/attachment/file/231856/Las_variedades_del_chayote_mexicano.pdf
Cadena-Iñiguez, J., & Arévalo-Galarza, M. (2010). GISeM: Rescatando y Aprovechando los Recursos Fitogenéticos de Mesoamérica Volumen 1: Chayote. Grupo Interdisciplinario de Investigación en Sechium edule en México, A.C.; Colegio de Postgraduados. ISBN 978-607-7533-80-1.https://www.gob.mx/cms/uploads/attachment/file/231857/El_chayote_volumen_1.pdf
Cadena-Iñiguez, J. (2010). El chayote (Sechium edule (Jacq.) Sw., importante recurso fitogenético mesoamericano. Agro Productividad, 3(2). https://revista-agroproductividad.org/index.php/agroproductividad/article/view/589
Cadena-Iñiguez, J. (2018). Uso de chayotes mexicanos para tratamiento de enfermedades de interés público. Agro Productividad, 9(11-B). https://doi.org/10.32854/agrop.v9i11-B.893
Cadena-Iñiguez, J., Arévalo-Galarza, M., Avendaño-Arrazate, C., Ventura-Valerio, V., & Ruiz-Posadas, L. (2021). Diseño y transferencia de la variedad vegetal de chayote [Sechium edule (Jacq) Sw.] var. virens levis "VENTLALI". Agro-Divulgación, 1(1). https://doi.org/10.54767/ad.v1i2.32
Chen, J., & Yang, A. (2019). Intelligent agriculture and its key technologies based on Internet of Things architecture. IEEE Access, 7, 77134-77141. https://doi.org/10.1109/ACCESS.2019.2921391
Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture, 12(10), 1745. https://doi.org/10.3390/agriculture12101745
Easy Eda (2013). (Versión 6.5.41) [Software]. Disponible en https://u.easyeda.com/
Effah, E., Thiare, O., & Wyglinski, A. M. (2024). Hardware evaluation of cluster-based agricultural IoT network. IEEE Access, 12, 33628-33651. https://doi.org/10.1109/ACCESS.2024.3370230
ElBeheiry, N., & Balog, R. (2023). Technologies driving the shift to smart farming: A review. IEEE Sensors Journal, 23(3), 1752-1769. https://doi.org/10.1109/JSEN.2022.3225183
FAO [Food and Agriculture Organization of the United Nations]. (2024). The state of food security and nutrition in the world 2024: Financing to end hunger, food insecurity and malnutrition in all its forms. https://doi.org/10.4060/cd1254en
Fritzing (2009). Fritzing (Versión 0.9.10) [Software]. Disponible en https://fritzing.org
Huang, K., Shu, L., Li, K., Yang, F., Han, G., Wang, X., & Pearson, S. (2020). Photovoltaic agricultural Internet of Things towards realizing the next generation of smart farming. IEEE Access, 8, 76300-76312. https://doi.org/10.1109/ACCESS.2020.2988663
Hu, W., Fan, J., Du, Y., Li, B., Xiong, N., & Bekkering, E. (2020). MDFC–ResNet: An agricultural IoT system to accurately recognize crop diseases. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3001237
Núñez, J., Fonthal, F., & Quezada, Y. (2017). Design and implementation of WSN for precision agriculture in white cabbage crops. In proc. 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON) (pp. 1–4). https://doi.org/10.1109/INTERCON.2017.8079671
JetBrains s.r.o. (2024). PyCharm Community Edition (Version 2024.1) [Software]. https://www.jetbrains.com/pycharm
Khan, N., Ray, R. L., Sargani, G. R., Ihtisham, M., Khayyam, M., & Ismail, S. (2021). Current progress and future prospects of agriculture technology: Gateway to sustainable agriculture. Sustainability, 13(9), 4883. https://doi.org/10.3390/su13094883
Malik, P., Singh, R., Gehlot, A., Akram, S., & Das, P. (2022). Village 4.0: Digitalization of village with smart Internet of Things technologies. Computers & Industrial Engineering, 165, 107938. https://doi.org/10.1016/j.cie.2022.107938
Ma, Y., Chen, J., & Shih, C. (2022). An automatic and intelligent Internet of Things for future agriculture. IT Professional, 24(6), 74-80. https://doi.org/10.1109/MITP.2022.3205707
Ortiz, H., Rosas-Calleja, D., & Debernardi-de-la-Vequia, H. (2019). Descripción de la estructura de soporte para el cultivo de chayote (Sechium edule (Jacq). Swartz), y propuesta de un nuevo modelo. Agro Productividad, 12(9). https://doi.org/10.32854/agrop.v12i9.1193
Sachithra, V. & Subhashini, L. (2023). How artificial intelligence uses to achieve the agriculture sustainability: Systematic review. Artificial Intelligence in Agriculture, 8, 46-59. https://doi.org/10.1016/j.aiia.2023.04.002
Servicio de Información Agroalimentaria y Pesquera [SIAP]. (2023). Valor de la producción nacional, módulo agrícola producción estatal producción anual de chayote sin clasificar. SIACON. [Software]. https://www.gob.mx/agricultura/dgsiap/documentos/siacon-ng-161430
Trongtorkid, C., & Pramokchon, P. (2018). Expert system for diagnosis mango diseases using leaf symptoms analysis. In Proc. of the International Conference on Digital Arts, Media and Technology (ICDAMT 2018) (pp. 59–64). https://doi.org/10.1109/ICDAMT.2018.8376496
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