{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T05:41:06Z","timestamp":1778046066261,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"R&amp;D Project BioDAgro\u2014Sistema operacional inteligente de informa\u00e7\u00e3o e suporte \u00e1 decis\u00e3o em AgroBiodiversidade","award":["PD20-00011"],"award-info":[{"award-number":["PD20-00011"]}]},{"name":"Funda\u00e7\u00e3o La Caixa and Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia, taking place at the C-MAST\u2014Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical Engineering of the University of Beira Interior, Covilh\u00e3, Portugal","award":["PD20-00011"],"award-info":[{"award-number":["PD20-00011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Processes"],"abstract":"<jats:p>The rapid growth of the world\u2019s population has put significant pressure on agriculture to meet the increasing demand for food. In this context, agriculture faces multiple challenges, one of which is weed management. While herbicides have traditionally been used to control weed growth, their excessive and random use can lead to environmental pollution and herbicide resistance. To address these challenges, in the agricultural industry, deep learning models have become a possible tool for decision-making by using massive amounts of information collected from smart farm sensors. However, agriculture\u2019s varied environments pose a challenge to testing and adopting new technology effectively. This study reviews recent advances in deep learning models and methods for detecting and classifying weeds to improve the sustainability of agricultural crops. The study compares performance metrics such as recall, accuracy, F1-Score, and precision, and highlights the adoption of novel techniques, such as attention mechanisms, single-stage detection models, and new lightweight models, which can enhance the model\u2019s performance. The use of deep learning methods in weed detection and classification has shown great potential in improving crop yields and reducing adverse environmental impacts of agriculture. The reduction in herbicide use can prevent pollution of water, food, land, and the ecosystem and avoid the resistance of weeds to chemicals. This can help mitigate and adapt to climate change by minimizing agriculture\u2019s environmental impact and improving the sustainability of the agricultural sector. In addition to discussing recent advances, this study also highlights the challenges faced in adopting new technology in agriculture and proposes novel techniques to enhance the performance of deep learning models. The study provides valuable insights into the latest advances and challenges in process systems engineering and technology for agricultural activities.<\/jats:p>","DOI":"10.3390\/pr11041263","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T01:42:39Z","timestamp":1681954959000},"page":"1263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Methods for Detecting and Classifying Weeds, Diseases and Fruits Using AI to Improve the Sustainability of Agricultural Crops: A Review"],"prefix":"10.3390","volume":"11","author":[{"given":"Ana","family":"Corceiro","sequence":"first","affiliation":[{"name":"Department of Electromechanical Engineering, University of Beira Interior, Rua Marqu\u00eas d\u2019\u00c1vila e Bolama, 6201-001 Covilh\u00e3, Portugal"}]},{"given":"Khadijeh","family":"Alibabaei","sequence":"additional","affiliation":[{"name":"Steinbuch Centre for Computing, Zirkel 2, D-76131 Karlsruhe, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6027-7763","authenticated-orcid":false,"given":"Eduardo","family":"Assun\u00e7\u00e3o","sequence":"additional","affiliation":[{"name":"Department of Electromechanical Engineering, University of Beira Interior, Rua Marqu\u00eas d\u2019\u00c1vila e Bolama, 6201-001 Covilh\u00e3, Portugal"},{"name":"C-MAST Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"},{"name":"Department of Computer Science, Instituto de Telecomunica\u00e7\u00f5es, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1691-1709","authenticated-orcid":false,"given":"Pedro D.","family":"Gaspar","sequence":"additional","affiliation":[{"name":"Department of Electromechanical Engineering, University of Beira Interior, Rua Marqu\u00eas d\u2019\u00c1vila e Bolama, 6201-001 Covilh\u00e3, Portugal"},{"name":"C-MAST Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"},{"name":"Department of Computer Science, Instituto de Telecomunica\u00e7\u00f5es, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"}]},{"given":"Nuno","family":"Pereira","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Instituto de Telecomunica\u00e7\u00f5es, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"ref_1","unstructured":"Tripathi, A.D., Mishra, R., Maurya, K.K., Singh, R.B., and Wilson, D.W. 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