Agricultura de precisão na produtividade sustentável: Uma revisão sistemática
DOI:
https://doi.org/10.35588/5rmk9326Palavras-chave:
agricultura de precisão, produtividade, sustentabilidade, fertilização, recursosResumo
A agricultura de precisão tem gerado novas formas de produção agrícola, especialmente devido à escassez de recursos essenciais para a fertilização e monitoramento dos campos. Nesse sentido, o objetivo foi determinar as contribuições da agricultura de precisão para a produtividade sustentável do setor agrícola. Para isso, foi utilizado o método de revisão sistemática PRISMA, com as bases de dados Web of Science e Scopus. O período de busca foi de 2019 a 2024, resultando em um total de 31 documentos relacionados ao tema de interesse. Esses documentos foram analisados no R–Studio, obtendo uma inércia de 1,44 para a agricultura de precisão e 0,99 para a produtividade sustentável. A conclusão é que a agricultura de precisão proporciona importantes contribuições para a produtividade sustentável das culturas, incluindo fertilização ótima, detecção de pragas, previsão de colheitas, manejo de terras e monitoramento em tempo real das culturas.
Downloads
Referências
Afzaal, H., Farooque, A.A., Abbas, F., Acharya, B. y Esau, T. (2020). Precision Irrigation Strategies for Sustainable Water Budgeting of Potato Crop in Prince Edward Island. Sustainability (Switzerland), 12(6), 2419. DOI https://doi.org/10.4067/10.3390/su12062419
Aliabad, F.A., Shojaei, S., Mortaz, M., Ferreira, C.S.S. y Kalantari, Z. (2022). Use of Landsat 8 and UAV Images to Assess Changes in Temperature and Evapotranspiration by Economic Trees following Foliar Spraying with Light-Reflecting Compounds. Remote Sensing, 14(23), 6153. DOI https://doi.org/10.4067/10.3390/rs14236153
Bristow, N., Rengaraj, S., Chadwick, D.R., Kettle, J. y Jones, D.L. (2022). Development of a LoRaWAN IoT Node with Ion-Selective Electrode Soil Nitrate Sensors for Precision Agriculture. Sensors, 22(23), 9100. DOI https://doi.org/10.4067/10.3390/s22239100
Cota, D., Martins, J., Mamede, H. y Branco, F. (2023). BHiveSense: An Integrated Information System Architecture for Sustainable Remote Monitoring and Management of Apiaries Based on IoT and Microservices. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), 100110. DOI https://doi.org/10.4067/10.1016/j.joitmc.2023.100110
De Oliveira, H.F.E., de Moura Campos, H., Mesquita, M., Machado, R.L., Vale, L.S.R., Siqueira, A.P.S. y Ferrarezi, R.S. (2021). Horticultural Performance of Greenhouse Cherry Tomatoes Irrigated Automatically Based on Soil Moisture Sensor Readings. Water, 13(19), 2662. DOI https://doi.org/10.4067/10.3390/w13192662
Djaman, K., Mohammed, A.T. y Koudahe, K. (2023). Accuracy of Estimated Crop Evapotranspiration Using Locally Developed Crop Coefficients against Satellite-Derived Crop Evapotranspiration in a Semiarid Climate. Agronomy-Basel, 13(7), 1937. DOI https://doi.org/10.4067/10.3390/agronomy13071937
Dutta, M., Gupta, D., Sahu, S., Limkar, S., Singh, P., Mishra, A., Kumar, M. y Mutlu, R. (2023). Evaluation of Growth Responses of Lettuce and Energy Efficiency of the Substrate and Smart Hydroponics Cropping System. Sensors, 23(4), 1875. DOI https://doi.org/10.4067/10.3390/s23041875
Ed-Daoudi, R., Alaoui, A., Ettaki, B. y Zerouaoui, J. (2023). A Predictive Approach to Improving Agricultural Productivity in Morocco through Crop Recommendations. International Journal of Advanced Computer Science and Applications, 14(3), 199-205. DOI https://doi.org/10.4067/10.14569/IJACSA.2023.0140322
Hundal, G.S., Laux, C.M., Buckmaster, D., Sutton, M.J. y Langemeier, M. (2023). Exploring Barriers to the Adoption of Internet of Things-Based Precision Agriculture Practices. Agriculture, 13(1), 163. DOI https://doi.org/10.4067/10.3390/agriculture13010163
Kawamura, K., Asai, H., Yasuda, T., Khanthavong, P., Soisouvanh, P. y Phongchanmixay, S. (2020). Field Phenotyping of Plant height in an Upland Rice Field in Laos Using Low-Cost Small Unmanned Aerial Vehicles (UAVs). Plant production science, 23(4), 452-465. DOI https://doi.org/10.4067/10.1080/1343943X.2020.1766362
Kazlauskas, M., Bruciene, I., Savickas, D., Naujokiene, V., Buragiene, S., Steponavicius, D., Romaneckas, K. y Sarauskis, E. (2023). Life Cycle Assessment of Winter Wheat Production Using Precision and Conventional Seeding Technologies. Sustainability, 15(19), 14376. DOI https://doi.org/10.4067/10.3390/su151914376
Kho, E.P., Chua, S.N.D., Lim, S.F., Lau, L.C. y Gani, M.T.N. (2022). Development of Young Sago Palm Environmental Monitoring System with Wireless Sensor Networks. Computers and Electronics in Agriculture, 193, 106723. DOI https://doi.org/10.4067/10.1016/j.compag.2022.106723
Krevh, V., Groh, J., Filipovic, L., Gerke, H.H.H., Defterdarovic, J., Thompson, S., Sraka, M., Bogunovic, I., Kovac, Z., Robinson, N., Baumgartl, T. y Filipovic, V. (2023). Soil-Water Dynamics Investigation at Agricultural Hillslope with High-Precision Weighing Lysimeters and Soil-Water Collection Systems. Water, 15(13), 2398. DOI https://doi.org/10.4067/10.3390/w15132398
Li, C., Hunt, D., Koenig, K., Smukler, S. y Bittman, S. (2021). Integrated Farm Management Systems to Improve Nutrient Management Using Semi-Virtual Farmlets: Agronomic Responses. Environmental Research Communications, 3, 075009. DOI https://doi.org/10.4067/10.1088/2515-7620/ac13c6
Li, Z., Chen, Z., Cheng, Q., Duan, F., Sui, R., Huang, X. y Xu, H. (2022). UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat. Agronomy, 12(1), 202. DOI https://doi.org/10.4067/10.3390/agronomy12010202
Liang, X., Jin, X., Liu, J., Yin, Y., Gu, Z., Zhang, J. y Zhou, Y. (2023). Formation Mechanism and Sustainable Productivity Impacts of Non-Grain Croplands: Evidence from Sichuan Province, China. Land Degradation and Development, 34(4), 1120-1132. DOI https://doi.org/10.4067/10.1002/ldr.4520
Liu, Z., Bashir, R.N., Iqbal, S., Shahid, M.M.A., Tausif, M. y Umer, Q. (2022). Internet of Things (IoT) and Machine Learning Model of Plant Disease Prediction-Blister Blight for Tea Plant. IEEE Access, 10, 44934–44944. DOI https://doi.org/10.4067/10.1109/Access.2022.3169147
Lu, J., Wang, H., Miao, Y., Zhao, L., Zhao, G., Cao, Q. y Kusnierek, K. (2022). Developing an Active Canopy Sensor-Based Integrated Precision Rice Management System for Improving Grain Yield and Quality, Nitrogen Use Efficiency, and Lodging Resistance. Remote Sensing, 14(10), 2440. DOI https://doi.org/10.4067/10.3390/rs14102440
Neményi, M., Kovács, A.J., Oláh, J., Popp, J., Erdei, E., Harsányi, E., Ambrus, B., Teschner, G. y Nyéki, A. (2022). Challenges of Sustainable Agricultural Development with Special Regard to Internet of Things: Survey. Progress in Agricultural Engineering Sciences, 18(1), 95-114. DOI https://doi.org/10.4067/10.1556/446.2022.00053
Postolache, S., Sebastião, P., Viegas, V., Postolache, O. y Cercas, F. (2023). IoT-Based Systems for Soil Nutrients Assessment in Horticulture. Sensors, 23(1), 403. DOI https://doi.org/10.4067/10.3390/s23010403
Sánchez Millán, F., Ortiz, F.J., Mestre Ortuño, T.C., Frutos, A. y Martínez, V. (2023). Development of Smart Irrigation Equipment for Soilless Crops Based on the Current Most Representative Water-Demand Sensors. Sensors, 23(6), 3177. DOI https://doi.org/10.4067/10.3390/s23063177
Sarker, K.K., Hossain, A., Ibn Murad K.F., Biswas, S.K., Akter, F., Rannu, R.P., Moniruzzaman, M., Karim, N.N. y Timsina, J. (2019). Development and Evaluation of an Emitter with a Low-Pressure Drip-Irrigation System for Sustainable Eggplant Production. Agriengineering, 1(3), 376-390. DOI https://doi.org/10.4067/10.3390/agriengineering1030028
Schillaci, C., Tadiello, T., Acutis, M. y Perego, A. (2021). Reducing Topdressing N Fertilization with Variable Rates Does Not Reduce Maize Yield. Sustainability, 13(14), 8059. DOI https://doi.org/10.4067/10.3390/su13148059
Shah, T.M., Nasika, D.P.B. y Otterpohl, R. (2021). Plant and Weed Identifier Robot as an Agroecological Tool Using Artificial Neural Networks for Image Identification. Agriculture-Basel, 11(3), 222. DOI https://doi.org/10.4067/10.3390/agriculture11030222
Shukla, B.K., Maurya, N. y Sharma, M. (2023). Advancements in Sensor-Based Technologies for Precision Agriculture: An Exploration of Interoperability, Analytics and Deployment Strategies. Engineering Proceedings, 58(1), 22. DOI https://doi.org/10.4067/10.3390/ecsa-10-16051
Singh, N., Ajaykumar, K., Dhruw, L.K. y Choudhury, B.U. (2023). Optimization of Irrigation Timing for Sprinkler Irrigation System Using Convolutional Neural Network-Based Mobile Application for Sustainable Agriculture. Smart Agricultural Technology, 5, 100305. DOI https://doi.org/10.4067/10.1016/j.atech.2023.100305
Terán-Chaves, C.A., Montejo-Nuñez, L., Cordero-Cordero, C. y Polo-Murcia, S.M. (2023). Water Productivity Indices of Onion (Allium cepa) under Drip Irrigation and Mulching in a Semi-Arid Tropical Region of Colombia. Horticulturae, 9(6), 632. DOI https://doi.org/10.4067/10.3390/horticulturae9060632
Thilakarathne, N.N., Bakar, M.S.A., Abas, P.E. y Yassin, H. (2023). Towards Making the Fields Talks: A Real-Time Cloud Enabled IoT Crop Management Platform for Smart Agriculture. Frontiers in Plant Science, 13. DOI https://doi.org/10.4067/10.3389/fpls.2022.1030168
Torres-Sánchez, J., Escola, A., De Castro A.I., López-Granados, F., Rosell-Polo, J.R., Sebe, F., Jiménez-Brenes, F.M., Sanz, R., Gregorio, E. y Pena, J.M. (2023). Mobile Terrestrial Laser Scanner vs. UAV Photogrammetry to Estimate Woody Crop Canopy Parameters-Part 2: Comparison for Different Crops and Training Systems. Computers and Electronics in Agriculture, 212, 108083. DOI https://doi.org/10.4067/10.1016/j.compag.2023.108083
Toscano, P., Cutini, M., Filisetti, A., Premoli, E., Porcu, M., Catalano, N., Bisaglia, C. y Brambilla, M. (2022). Workability Assessment of Different Stony Soils by Soil–Planter Interface Noise and Acceleration Measurement. AgriEngineering, 4(4), 1139-1152. DOI https://doi.org/10.4067/10.3390/agriengineering4040070
Trenz, J., Memic, E., Batchelor, W.D. y Graeff-Hoenninger, S. (2023). Generic Optimization Approach of Soil Hydraulic Parameters for Site-Specific Model Applications. Precision Agriculture, 25, 654-680. DOI https://doi.org/10.4067/10.1007/s11119-023-10087-9
Tseng, H.H., Yang, M.D., Saminathan, R., Yu-Chun, H., Yang, C.Y. y Wu, D.H. (2022). Rice Seedling Detection in UAV Images Using Transfer Learning and Machine Learning. Remote Sensing, 14(12), 2837. DOI https://doi.org/10.4067/10.3390/rs14122837
Tsiropoulos, Z., Skoubris, E., Fountas, S., Gravalos, I. y Gemtos, T. (2022). Development of an Energy Efficient and Fully Autonomous Low-Cost IoT System for Irrigation Scheduling in Water-Scarce Areas Using Different Water Sources. Agriculture-Basel, 12(7), 1044. DOI https://doi.org/10.4067/10.3390/agriculture12071044
Turnip, A., Pebriansyah, F.R., Simarmata, T., Sihombing, P. y Joelianto, E. (2023). Design of Smart Farming Communication and Web Interface Using MQTT and Node.js. Open Agriculture, 8(1), 20220159. DOI https://doi.org/10.4067/10.1515/opag-2022-0159
Visentin, F., Cremasco, S., Sozzi, M., Signorini, L., Signorini, M., Marinello, F. y Muradore, R. (2023). A Mixed-Autonomous Robotic Platform for Intra-Row and Inter-Row Weed Removal for Precision Agriculture. Computers and Electronics in Agriculture, 214, 108270. DOI https://doi.org/10.4067/10.1016/j.compag.2023.108270
Vogel, S., Gebbers, R., Oertel, M. y Kramer, E. (2019). Evaluating Soil-Borne Causes of Biomass Variability in Grassland by Remote and Proximal Sensing. SENSORS, 19(20), 4593. DOI https://doi.org/10.4067/10.3390/s19204593
Waheed, H., Akram, W., Islam, S.U., Hadi, A., Boudjadar, J. y Zafar, N. (2023). A Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning. Future Internet, 15(3), 86. DOI https://doi.org/10.4067/10.3390/fi15030086
Wakjira, K., Negera, T., Zacepins, A., Kviesis, A., Komasilovs, V., Fiedler, S., Kirchner, S., Hensel, O., Purnomo, D., Nawawi, M., Gratzer, K. y Brodschneider, R. (2021). Smart Apiculture Management Services for Developing Countries—The Case of SAMS Project in Ethiopia and Indonesia. PeerJ Computer Science, 7, e484. DOI https://doi.org/10.4067/10.7717/Peerj-CS.484
Yepes-Nuñez, J.J., Urrútia, G., Romero-García, M. y Alonso-Fernández, S. (2021). Declaración PRISMA 2020: Una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790-799.
Zeraatpisheh, M., Bakhshandeh, E., Emadi, M., Li, T. y Xu, M. (2020). Integration of PCA and Fuzzy Clustering for Delineation of Soil Management Zones and Cost-Efficiency Analysis in a Citrus Plantation. Sustainability, 12(14), 1-17. DOI https://doi.org/10.4067/10.3390/su12145809
Downloads
Publicado
Edição
Secção
Licença
Direitos de Autor (c) 2025 RIVAR

Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição 4.0.