Review of the cutting edge technologies for weed control in field crops
Abstract
Keywords: artificial intelligence, food security, integrated weed management, machine learning, nano-herbicide
DOI: 10.25165/j.ijabe.20241705.9019
Citation: Priyadarshini A, Dash S, Jena J, Kusumavathi K, Pattnaik P, Holderbaum W. Review of the cutting edge technologies for weed control in field crops. Int J Agric & Biol Eng, 2024; 17(4): 30-43.
Keywords
Full Text:
PDFReferences
Korav S, Dhaka A K, Singh R, Premaradhya N, Reddy G C. A study on crop weed competition in field crops. Journal of Pharmacognosy and Phytochemistry, 2018; 7（4）: 3235–3240.
Riemens M, Sønderskov M, Moonen A-C, Storkey J, Kudsk P. An integrated weed management framework: A pan-European perspective. European Journal of Agronomy, 2022; 133: 126443.
Yu J L, Schumann A W, Cao Z, Sharpe S M, Boyd N S. Weed detection in perennial ryegrass with deep learning convolutional neural network. Frontiers in Plant Science, 2019; 10: 1422.
Ahmad Loti N N, Mohd Noor M R, Chang S-W. Integrated analysis of machine learning and deep learning in chilli pest and disease identification. Journal of the Science of Food and Agriculture, 2021; 101（9）: 3582–3594.
Gharde Y, Singh P K, Dubey R P, Gupta P K. Assessment of yield and economic losses in agriculture due to weeds in India. Crop Protection, 2018; 107: 12–18.
Molinari F A, Blanco A M, Vigna M R, Chantre G R. Towards an integrated weed management decision support system: A simulation model for weed-crop competition and control. Computers and Electronics in Agriculture, 2020; 175: 105597.
MacLaren C, Storkey J, Menegat A, Metcalfe H, Dehnen-Schmutz K. An ecological future for weed science to sustain crop production and the environment. A review. Agronomy for Sustainable Development, 2020; 40: 24.
Chu L, Liu H F, Zhang Z Y, Zhan Y, Wang K, Yang D Y, et al. Evaluation of wood vinegar as an herbicide for weed control. Agronomy, 2022; 12（12）: 3120.
Sondhia S. Herbicides residues in soil, water, plants and non-targeted organisms and human health implications: An Indian perspective. Indian Journal of Weed Science, 2014; 46（1）: 66–85.
Hakme E, Herrmann S S, Poulsen M E. Data processing approach for the screening and quantification of pesticide residues in food matrices for early-generation GC-TOFMS. Brazilian Journal of Analytical Chemistry, 2020; 7（26）: 51–77.
Wang P, Tang Y, Luo F, Wang L H, Li C S, Niu Q, et al. Weed25: A deep learning dataset for weed identification. Frontiers in Plant Science, 2022; 13: 1053329.
Nasiri A, Omid M, Taheri-Garavand A, Jafari A. Deep learning-based precision agriculture through weed recognition in sugar beet fields. Sustainable Computing: Informatics and Systems, 2022; 35: 100759.
Jha K, Doshi A, Patel P, Shah M. A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2019; 2: 1–12.
Xie D B, Chen L, Liu L C, Chen L Q, Wang H. Actuators and sensors for application in agricultural robots: A review. Machines, 2022; 10（10）: 913.
Nath C P, Singh R G, Choudhary V K, Datta D, Nandan R, Singh SS. Challenges and alternatives of herbicide-based weed management. Agronomy, 2024; 14（1）: 126.
Wu Z N, Chen Y J, Zhao B, Kang X B, Ding Y Y. Review of weed detection methods based on computer vision. Sensors, 2021; 21（11）: 3647.
Garibaldi-Márquez F, Flores G, Mercado-Ravell D A, Ramírez-Pedraza A, Valentín-Coronado L M. Weed classification from natural corn field-multi-plant images based on shallow and deep learning. Sensors, 2022; 22（8）: 3021.
Ali M A, Rehman I, Iqbal A, Din S u, Rao A Q, Latif A, et al. Nanotechnology, a new frontier in Agriculture. Advances in Life Sciences, 2014; 1（3）: 129–138.
Paul S K, Mazumder S, Naidu R. Herbicidal weed management practices: history and future prospects of nanotechnology in an eco-friendly crop production system. Heliyon, 2024; 10（5）: e26527.
Dhanapal G N, Ganapathi S, Bai S K, Nagarjun P, Sindhu K K. Nanotechnology in weed management-a review. Mysore Journal of Agricultural Sciences, 2020; 54（3）: 19–25.
Attri I, Awasthi L K, Sharma T P. Machine learning in agriculture: a review of crop management applications. Multimedia Tools and Applications, 2024; 83（5）: 12875–12915.
Vasileiou M, Kyriakos L S, Kleisiari C, Kleftodimos G, Vlontzos G, Belhouchette H, et al. Transforming weed management in sustainable agriculture with artificial intelligence: A systematic literature review towards weed identification and deep learning. Crop Protection, 2024; 176: 106522.
Roslim M H M, Juraimi A S, Che’Ya N N, Sulaiman N, Manaf M N H A, Ramli Z, et al. Using remote sensing and an unmanned aerial system for weed management in agricultural crops: A review. Agronomy, 2021; 11（9）: 1809.
Ghatrehsamani S, Jha G, Dutta W, Molaei F, Nazrul F, Fortin M, et al. Artificial intelligence tools and techniques to combat herbicide resistant weeds—a review. Sustainability, 2023; 15（3）: 1843.
Sa I, Popović M, Khanna R, Chen Z, Lottes P, Liebisch F, et al. Weed Map: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming. Remote Sensing, 2018; 10（9）: 1423.
Duwadi A, Acharya A, Gautam S. A review on non-chemical weed management in maize （Zea Mays L.）. Food and Agri Economics Review, 2021; 1（1）: 46–51.
Gao W-T, Su W-H. Weed management methods for herbaceous field crops: A review. Agronomy, 2024; 14（3）: 486.
Gerhards R, Andujar Sanchez D, Hamouz P, Peteinatos G G, Christensen S, Fernandez-Quintanilla C. Advances in site-specific weed management in agriculture—A review. Weed Research, 2022; 62（2）: 123–133.
Balaska V, Adamidou Z, Vryzas Z, Gasteratos A. Sustainable crop protection via robotics and artificial intelligence solutions. Machines, 2023; 11（8）: 774.
Naeem M, Farooq M, Farooq S, Ul-Allah S, Alfarraj S, Hussain M. The impact of different crop sequences on weed infestation and productivity of barley （Hordeum vulgare L.） under different tillage systems. Crop Protection, 2021; 149: 105759.
Naeem M, Mehboob N, Farooq M, Farooq S, Hussain S, M Ali H, et al. Impact of different barley-based cropping systems on soil physicochemical properties and barley growth under conventional and conservation tillage systems. Agronomy, 2020; 11（1）: 8.
Hauvermale A L, Sanad M N M E. Phenological plasticity of wild and cultivated plants. Plant Communities and Their Environment, 2019. doi: 10.5772/intechopen.85070.
Shahzad M, Farooq M, Hussain M. Weed spectrum in different wheat-based cropping systems under conservation and conventional tillage practices in Punjab, Pakistan. Soil and Tillage Research, 2016; 163: 71–79.
Walia U S, Singh B. Performance of triasulfuron and carfentrazone-ethyl against broad leaf weeds in wheat. Indian Journal of Weed Science, 2007; 39（1,2）: 52–54.
Smith J D, Dubois T, Mallogo R, Njau E-F, Tua S, Srinivasan R. Host range of the invasive tomato pest Tuta absoluta Meyrick （Lepidoptera: Gelechiidae） on solanaceous crops and weeds in Tanzania. Florida Entomologist, 2018; 101（4）: 573–579.
Esposito M, Crimaldi M, Cirillo V, Sarghini F, Maggio A. Drone and sensor technology for sustainable weed management: A review. Chemical and Biological Technologies in Agriculture, 2021; 8: 18.
Tanveer A, Khaliq A, Ali H H, Mahajan G, Chauhan B S. Interference and management of parthenium: the world’s most important invasive weed. Crop Protection, 2015; 68: 49–59.
Khan M R, Somvanshi V S, Rao U. Emerging nematode pest of rice, wheat and onion. Rice Root-Knot Nematode. Popular Kheti, 2017; 3: 53.
Kaur G, Brar H S, Singh G. Effect of weed management on weeds, nutrient uptake, nodulation, growth and yield of summer mungbean （Vigna radiata）. Indian Journal of Weed Science, 2010; 42（1,2）: 114–119.
Kumar N, Hazra K K, Yadav S L, Singh S S. Weed dynamics and productivity of chickpea （Cicer arietinum） under pre-and post-emergence application of herbicides. Indian Journal of Agronomy, 2015; 60（4）: 570–575.
Kumar N, Nath C P, Hazra K K. Weed management in pulse crops: Challenges and opportunities. Indian Journal of Weed Science, 2022; 54（4）: 397–410.
Pradhan A, Dixit A, Keram K S, Dewangan P K. Weed management in dry direct-seeded rice under rainfed ecology of Southern Chhattisgarh. Indian Journal of Weed Science, 2023; 55（2）: 149–152.
Poojitha K, Murthy K N, Sanjay M T, Dhanapal G N. Weed management efficacy of herbicides and allelochemicals in direct-seeded rice. Indian Journal of Weed Science, 2023; 55（2）: 153–155.
Sangramsingh P K, Dash S. Sole and sequential application of herbicide and straw mulch on weed growth and productivity of direct seeded rainfed rice （Oryza sativa L.）. Bangladesh Journal of Botany, 2021; 50（3）: 671–677.
Mahapatra A, Saha S, Munda S, Bhabani S S, Meher S, Jangde H K. Bio-efficacy of herbicide mixtures on weed dynamics in direct wet-seeded rice. Indian Journal of Weed Science, 2023; 55（1）: 18–23.
Kumar R, Mishra J S, Kumar S, Choudhary A K, Singh A K, Hans H, et al. Weed competitive ability and productivity of transplanted rice cultivars as influenced by weed management practices. Indian Journal of Weed Science, 2023; 55（1）: 13–17.
Teja K C, Duary B, Dash S. Sole and combined application of herbicides on composite weed flora of transplanted rice. Indian Journal of Weed Science, 2016; 48（3）: 254–258.
Duary B. Efficacy of imazosulfuron on weed flora and productivity of wet season transplanted rice in West Bengal. International Journal of Bioresource Science, 2014; 1（1）: 1–6.
Dash S, Duary B, Sar K. Efficacy of fenoxaprop-p-ethyl and penoxsulam for weed management with special emphasis on Echinochloa spp. in transplanted summer rice. Indian Journal of Weed Science, 2021; 53（1）: 78–80.
Kundra V, Aulakh C S, Bhullar M S. Integration of allelopathic water extracts with cultural practices for weed management in organic wheat. Indian Journal of Weed Science, 2023; 55（1）: 24–31.
Mishra J S, Kumar R, Mondal S, Poonia S P, Rao K K, Dubey R, et al. Tillage and crop establishment effects on weeds and productivity of a rice-wheat-mungbean rotation. Field Crops Research, 2022; 284: 108577.
Sarita, Singh I, Mehriya M L, Samota M K. A study of wheat-weed response and economical analysis to fertilization and post-emergence herbicides under arid climatic conditions. Frontiers in Agronomy, 2022; 4: 914091.
Kaul A, Singh B, Singh M. Weed management with pre-and post-emergence herbicides in Kharif maize in sub-mountainous area of Punjab, India. Indian Journal of Weed Science, 2023; 55（1）: 32–35.
Khedwal R S, Yadav D B, Hooda V S, Dahiya S, Chaudhary A. Effect of planting methods, hybrids and weed management on weeds and productivity of rainy season maize. Indian Journal of Weed Science, 2023; 55（1）: 99–102.
Kumar B, Prasad S, Mandal D, Kumar R. Influence of integrated weed management practices on weed dynamics, productivity and nutrient uptake of rabi maize （Zea mays L.）. International Journal of Current Microbiology and Applied Sciences, 2017; 6（4）: 1431–1440.
Teja K C, Duary B, Dash S, Bhowmick M K, Mallikarjun M. Efficacy of imazethapyr and other herbicides on weed growth and yield of kharif blackgram. International Journal of Agriculture, Environment and Biotechnology, 2016; 9（6）: 967–971.
Rathi J P S, Tewari A N, Kumar M. Integrated weed management in blackgram （Vigna mungo L.）. Indian Journal of Weed Science, 2004; 36（3,4）: 218–220.
Mohanty P, Sar K, Duary B, Mishra G. Effect of sole and ready-mix herbicides on weeds and productivity of summer greengram in Odisha. Indian Journal of Weed Science, 2023; 55（1）: 50–53.
Patnaik P K, Dash S, Chowdhury M R, Das S P, Sar K, Pradhan S R. Weed growth and productivity of summer greengram （Vigna radiata L.） under sole and sequential application of herbicides. Research on Crops, 2022; 23（1）: 70–75.
Chowdhury R, Dash S, Pradhan A. Weed management in direct seeded rice-musterd-sesame crop sequence in lateritic soil of Birbhum, West Bengal. Pesticide Research Journal, 2021; 33（2）: 43–51.
Kumar S. Identification of trap crop for reducing broomrape infestation in the succeeding mustard. Agronomy Digest, 2002; 2: 99–101. GOOGLE SCHOLAR
Joshi N, Joshi S, Sharma J K, Shekhawat H S, Shukla U N. Efficacy of sequential application of pre-and post-emergence herbicides for weed management in sesame. Indian Journal of Weed Science, 2022; 54（3）: 279–282.
Mruthul T, Halepyati A S, Chittapur B M. Chemical weed management in sesame （Sesamum indicum L.）. Karnataka Journal of Agricultural Sciences, 2015; 28（2）: 151–154.
Teja K C, Duary B, Dash S, Mallick R B. Post-emergence application of imazethapyr for weed management in lentil. SATSA Mukhaptra Annual Technical Issue, 2017; 21: 183–188.
Singh G, Kaur H, Khanna V. Weed management in lentil with post-emergence herbicides. Indian Journal of Weed Science, 2014; 46（2）: 187–189.
Nalayini P, Blaise D, Mundafale H R. Stale seed bed technique and leguminous cover crops as components of integrated weed management in irrigated cotton. Indian Journal of Weed Science, 2023; 55（1）: 46–49.
Prabhu G, Halepyati A S, Pujari B T, Desai B K. Weed managementin Bt cotton （Gossypium hirsutum L.） under irrigation. Karnataka Journal of Agricultural Sciences, 2012; 25（2）: 183–186.
Roy D K, Ranjan S, Sow S. Weed management effect on weeds, productivity and economics of soybean. Indian Journal of Weed Science, 2023; 228–230.
Müller-Schärer H, Collins A R. Integrated weed management. In: Fath B D, Jorgensen S E （Ed. ）. Managing Soils and Terrestrial Systems. Boca Raton: CRC Press. 2020; pp.439–447.
Lamichhane J R, Devos Y, Beckie H J, Owen M D, Tillie P, Messéan A, et al. Integrated weed management systems with herbicide-tolerant crops in the European Union: lessons learnt from home and abroad. Critical reviews in biotechnology, 2017; 37（4）: 459–475.
Dash S, Sarkar S, Tripathy H P, Pattanaik P, Patnaik S. Robotics in weed management: A new paradigm in agriculture. In 2021 International Conference on Electronic Information Technology and Smart Agriculture （ICEITSA）, Huaihua, China: IEEE, 2021; pp.561–564.
Rani L, Thapa K, Kanojia N, Sharma N, Singh S, Grewal A S, et al. An extensive review on the consequences of chemical pesticides on human health and environment. Journal of Cleaner Production, 2021; 283: 124657.
D’Accolti A, Maggio S, Massaro A, Galiano A M, Birardi V, Pellicani L. Assessment of data fusion oriented on data mining approaches to enhance precision agriculture practices aimed at increase of durum wheat （Triticum turgidum L. var. durum） yield. Journal of Food, Nutrition and Agriculture, 2018; 1（1）: 47–54.
Gautam P V, Kushwaha H L, Kumar A, Kushwaha D K. Mechatronics application in precision sowing: A review. International Journal of Current Microbiology and Applied Sciences, 2019; 8（4）: 1793–1807.
Mogili U R, Deepak B B V L. Review on application of drone systems in precision agriculture. Procedia Computer Science, 2018; 133: 502–509.
Onashoga A, Ojesanmi O, Johnson F, Ayo F E. A fuzzy-based decision support system for soil selection in olericulture. Journal of Agricultural Informatics, 2018; 9（3）: 65–77.
Raja R, Slaughter D C, Fennimore S A, Nguyen T T, Vuong V L, Sinha N, et al. Crop signalling: A novel crop recognition technique for robotic weed control. Biosystems Engineering, 2019; 187: 278–291.
Eli-Chukwu N C. Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 2019; 9（4）: 4377–4383.
Shamshiri R R, Weltzien C, Hameed I A, Yule I J, Grift T E, Balasundram S K, et al. Research and development in agricultural robotics: A perspective of digital farming. Int J Agric & Biol Eng, 2018; 11（4）: 1–14.
Gonzalez-de-Santos P, Ribeiro A, Fernandez-Quintanilla C, Lopez-Granados F, Brandstoetter M, Tomic S, et al. Fleets of robots for environmentally-safe pest control in agriculture. Precision Agriculture, 2017; 18: 574–614.
Ruigrok T, van Henten E, Booij J, Van Boheemen K, Kootstra G. Application-specific evaluation of a weed-detection algorithm for plant-specific spraying. Sensors, 2020; 20（24）: 7262.
Kiani S, Jafari A. Crop detection and positioning in the field using discriminant analysis and neural networks based on shape features. Journal of Agricultural Science and Technology, 2012; 14: 755–765.
Perez A J, Lopez F, Benlloch J V, Christensen S. Colour and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture, 2000; 25（3）: 197–212.
Ahmed F, Bari A S M H, Shihavuddin A S M, Al-Mamun H A, Kwan P. A study on local binary pattern for automated weed classification using template matching and support vector machine. In: 2011 IEEE 12th International Symposium on Computational Intelligence and Informatics （CINTI）, Budapest, Hungary: IEEE, 2011; pp.329–334.
Lati R N, Siemens M C, Rachuy J S, Fennimore S A. Intrarow weed removal in broccoli and transplanted lettuce with an intelligent cultivator. Weed Technology, 2016; 30: 655–663.
Maimaitijiang M, Ghulam A, Sidike P, Hartling S, Maimaitiyiming M, Peterson K, et al. Unmanned Aerial System （UAS）-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS Journal of Photogrammetry and Remote Sensing, 2017; 134: 43–58.
Rosle R, Che’Ya N N, Ang Y, Rahmat F, Wayayok A, Berahim Z, Fazlil Ilahi W F, Ismail M R, Omar M H. Weed detection in rice fields using remote sensing technique: A review. Applied Sciences, 2021; 11（22）: 10701.
Reddy G O. Satellite remote sensing sensors: principles and applications. In: Reddy G, Singh S （Eds）. Geospatial Technologies in Land Resources Mapping, Monitoring and Management. Berlin: Springer. 2018; pp.21–43.
Belal A A, El-Ramady H, Jalhoum M, Gad A, Mohamed E S. Precision farming technologies to increase soil and crop productivity. In: Abu-hashim M, Khebour Allouche F, Negm A. （Eds）. Agro-Environmental Sustainability in MENA Regions. Berlin: Springer. 2021; pp.117–154.
Jena J, Misra S R, Tripathi KP. Normalized difference vegetation index （NDVI） and its role in agriculture. Agriculture and Food: E-Newsletter, 2019; 1（12）: 387–389.
Zhang Y Y, Zhang B, Shen C, Liu H L, Huang J C, Tian K P, et al. Review of the field environmental sensing methods based on multi-sensor information fusion technology. Int J Agric & Biol Eng, 2024; 17（2）: 1–13.
Chen B W, Liu L Q, Zou Z X, Shi Z W. Target detection in hyperspectral remote sensing image: Current status and challenges. Remote Sensing, 2023; 15（13）: 3223.
Yang T L, Zhu S L, Zhang W J, Zhao Y Y, Song X X, Yang G S, et al. Unmanned aerial vehicle-scale weed segmentation method based on image analysis technology for enhanced accuracy of maize seedling counting. Agriculture, 2024; 14（2）: 175.
Joshi A, Pradhan B, Gite S, Chakraborty S. Remote-sensing data and deep-learning techniques in crop mapping and yield prediction: A systematic review. Remote Sensing, 2023; 15（8）: 2014.
Chandrasena N R, Rao A N. Commemorating 50 Years （1967-2017） 50th Anniversary Celebratory Volume, Asian-Pacific Weed Science Society （APWSS）. Japan: Asian-Pacific Weed Science Society （APWSS）, Indian Society of Weed Science （ISWS）, India and The Weed Science Society of Japan （WSSJ）, 2017; 208p.
Rasmussen J, Nielsen J, Streibig J C, Jensen J E, Pedersen K S, Olsen S I. Pre-harvest weed mapping of Cirsium arvense in wheat and barley with off-the-shelf UAVs. Precision Agriculture, 2019; 20: 983–999.
Franco C, Guada C, Rodríguez J T, Nielsen J, Rasmussen J, Gómez D, Montero J. Automatic detection of thistle-weeds in cereal crops from aerial RGB images. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications: 17th International Conference, IPMU 2018, Cádiz, Spain: Springer, 2018; pp.441–452.
Rasmussen J, Nielsen J, Garcia-Ruiz F, Christensen S, Streibig J C. Potential uses of small unmanned aircraft systems （UAS） in weed research. Weed Research, 2013; 53（4）: 242–248.
Peña J M, Torres-Sánchez J, de Castro A I, Kelly M, López-Granados F. Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle （UAV） images. PloS One, 2013; 8（10）: e77151.
Louargant M, Jones G, Faroux R, Paoli J-N, Maillot T, Gée C, et al. Unsupervised classification algorithm for early weed detection in row-crops by combining spatial and spectral information. Remote Sensing, 2018; 10（5）: 761.
López-Granados F, Peña-Barragán J M, Jurado-Expósito M, Francisco-Fernández M, Cao R, Alonso-Betanzos A, Fontenla-Romero O. Multispectral classification of grass weeds and wheat （Triticum durum） using linear and nonparametric functional discriminant analysis and neural networks. Weed Research, 2008; 48（1）: 28–37.
Garcia-Ruiz F J, Wulfsohn D, Rasmussen J. Sugar beet （Beta vulgaris L.） and thistle （Cirsium arvensis L.） discrimination based on field spectral data. Biosystems Engineering, 2015; 139: 1–15.
Shapira U, Herrmann I, Karnieli A, Bonfil J D. Weeds detection by ground-level hyperspectral data. Theory into Practice, 2010; 38: 27–33.
Scherrer B, Sheppard J, Jha P, Shaw J A. Hyperspectral imaging and neural networks to classify herbicide-resistant weeds. Journal of Applied Remote Sensing, 2019; 13（4）: 044516.
Che’Ya N N, Gupta M, Doug G, Lisle A, Basnet B, Campbell G. Spectral discrimination of weeds using hyperspectral radiometry. In: Proceedings of the 5th Asian Conference on Precision Agriculture （ACPA）, Jeju, Korea, 2013; pp.325–333.
López-Granados F, Torres-Sánchez J, Serrano-Pérez A, de Castro A I, Mesas-Carrascosa F J, Pena J M. Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds. Precision Agriculture, 2016; 17（2）: 183–199.
Huang Y B, Reddy K N, Fletcher R S, Pennington D. UAV low-altitude remote sensing for precision weed management. Weed Technology, 2018; 32（1）: 2–6.
Raj R, Kar S, Nandan R, Jagarlapudi A. Precision agriculture and unmanned aerial vehicles （UAVs）. In: Avtar R, Watanabe T （eds）. Unmanned Aerial Vehicle: Applications in Agriculture and Environment. Springer, Cham. 2020; pp.7–23. Doi: 10.1007/978-3-030-27157-2_2.
Krishna K R. Agricultural drones: a peaceful pursuit. New York: Apple Academic Press. 2018; 412 p.
Hassanein M, El-Sheimy N. An efficient weed detection procedure using low-cost UAV imagery system for precision agriculture applications. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018; 181–187. doi: 10.5194/isprs-archives-XLII-1-181-2018.
Chen Y, Qi H L, Li G Z, Lan Y B. Weed control effect of unmanned aerial vehicle （UAV） application in wheat field. International Journal of Precision Agricultural Aviation, 2019; 2（2）: 25–31.
Pranaswi D, Jagtap M P, Asewar B V, Gokhale D N, Shinde G U. Weed control efficiency with herbicide application by the combination of Drone and Knapsack sprayer in wheat （Triticum aestivum L.）. The Pharma Innovation Journal, 2022; 11（1）: 741–744.
Gibbs J L, Peters T M, Heck L P. Comparison of droplet size, coverage, and drift potential from uav application methods and ground application methods on row crops. Transactions of the ASABE, 2021; 64（3）: 819–828.
Meesaragandla S, Jagtap M P, Khatri N, Madan H, Vadduri A A. Herbicide spraying and weed identification using drone technology in modern farms: A comprehensive review. Results in Engineering, 2024; 21: 101870.
Xue X, Lan Y. Agricultural aviation applications in USA. Transactions of the CSAM, 2013; 44（5）: 194–201.
Hiremath C, Khatri N, Jagtap M P. Comparative studies of knapsack, boom, and drone sprayers for weed management in soybean （Glycine max L.）. Environmental Research, 2024; 240: 117480.
Akandewa A, Linda N G. Analysis and Detection of Weeds Using Artificial Neural Networks. Information Technology Engineering Journals, 2022; 7（2）: 123–130.
Yoon J. ANN-based collaborative sensor calibration and GA-approach to sensor mutation management. In: 2017 6th IIAI International Congress on Advanced Applied Informatics （IIAI-AAI）, Hamamatsu, Japan: IEEE, 2017; pp.897–902.
Hu W Z, Wane S O, Zhu J K, Li D S, Zhang Q, Bie X T, et al. Review of deep learning-based weed identification in crop fields. Int J Agric & Biol Eng, 2023; 16（4）: 1–10.
Loddo A, Loddo M, Di Ruberto C. A novel deep learning-based approach for seed image classification and retrieval. Computers and Electronics in Agriculture, 2021; 187: 106269.
Zhang N, Wei X, Chen H, Liu W C. FPGA implementation for CNN-based optical remote sensing object detection. Electronics, 2021; 10（3）: 282.
Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11（4）: 32–44.
Ma H J, Liu Y L, Ren Y H, Wang D C, Yu L J, Yu J X. Improved CNN classification method for groups of buildings damaged by earthquake, based on high resolution remote sensing images. Remote Sensing, 2020; 12（2）: 260.
Afza F, Sharif M, Mittal M, Khan M A, Hemanth D J. A hierarchical three-step superpixels and deep learning framework for skin lesion classification. Methods, 2022; 202: 88–102.
Dyrmann M, Karstoft H, Midtiby H S. Plant species classification using deep convolutional neural network. Biosystems Engineering, 2016; 151: 72–80.
Dyrmann M. Automatic detection and classification of weed seedlings under natural light conditions. PhD dissertation. Odense: University of Southern Denmark, 2017. 311 p.
Sharpe S M, Schumann A W, Yu J, Boyd N S. Vegetation detection and discrimination within vegetable plasticulture row-middles using a convolutional neural network. Precision Agriculture, 2020; 21: 264–277.
López-Correa J M, Moreno H, Ribeiro A, Andújar D. Intelligent weed management based on object detection neural networks in tomato crops. Agronomy, 2022; 12（12）: 2953.
Tang Z, Su Y C, Er M J, Qi F, Zhang L, Zhou J Y. A local binary pattern-based texture descriptors for classification of tea leaves. Neurocomputing, 2015; 168: 1011–1023.
Chowdhury S, Verma B, Stockwell D. A novel texture feature based multiple classifier technique for roadside vegetation classification. Expert Systems with Applications, 2015; 42（12）: 5047–5055.
Chen Y J, Wu Z N, Zhao B, Fan C X, Shi S W. Weed and corn seedling detection in field based on multi feature fusion and support vector machine. Sensors, 2021; 21（1）: 212.
Jiang L C, Basri M, Omar D, Rahman M B A, Salleh A B, Rahman R N Z R A, et al. Green nano-emulsion intervention for water-soluble glyphosate isopropylamine （IPA） formulations in controlling Eleusine indica （E. indica）. Pesticide Biochemistry and Physiology, 2012; 102（1）: 19–29.
Lim C J, Basri M, Omar D, Abdul Rahman M B, Salleh A B, Raja Abdul Rahman R N Z. Green nanoemulsion-laden glyphosate isopropylamine formulation in suppressing creeping foxglove （A. gangetica）, slender button weed （D. ocimifolia） and buffalo grass （P. conjugatum）. Pest Management Science, 2013; 69（1）: 104–111.
Abigail M E A, Samuel S M, Chidambaram R. Application of rice husk nanosorbents containing 2, 4-dichlorophenoxyacetic acid herbicide to control weeds and reduce leaching from soil. Journal of the Taiwan Institute of Chemical Engineers, 2016; 63: 318–326.
Evy Alice Abigail M. Biochar-based nanocarriers: fabrication, characterization, and application as 2,4-dichlorophenoxyacetic acid nanoformulation for sustained release. 3 Biotech, 2019; 9: 317.
Zhang L L, Chen C W, Zhang G L, Liu B, Wu Z Y, Cai D Q. Electrical-driven release and migration of herbicide using a gel-based nanocomposite. Journal of Agricultural and Food Chemistry, 2020; 68（6）: 1536–1545.
Heydari M, Yousefi A R, Nikfarjam N, Rahdar A, Kyzas G Z, Bilal M. Plant-based nanoparticles prepared from protein containing tribenuron-methyl: fabrication, characterization, and application. Chemical and Biological Technologies in Agriculture, 2021; 8: 53.
Zainuddin N J, Ashari S E, Salim N, Asib N, Omar D, Lian G E C. Optimization and characterization of palm oil-based nanoemulsion loaded with Parthenium hysterophorus crude extract for natural herbicide formulation. Journal of Oleo Science, 2019; 68（8）: 747–757.
Takeshita V, Carvalho B L, Galhardi J A, Munhoz-Garcia G V, Pimpinato R F, Oliveira H C, et al. Development of a preemergent nanoherbicide: From efficiency evaluation to the assessment of environmental fate and risks to soil microorganisms. ACS Nanoscience Au, 2022; 2（4）: 307–323.
Shen Z C, Zhou X H, Sun X H, Xu H, Chen H Y, Zhou H J. Preparation of 2, 4-dichlorophenoxyacetic acid loaded on cysteamine-modified polydopamine and its release behaviors. Journal of Applied Polymer Science, 2019; 136（20）: 47469.
López-Cabeza R, Poiger T, Cornejo J, Celis R. A clay-based formulation of the herbicide imazaquin containing exclusively the biologically active enantiomer. Pest Management Science, 2019; 75（7）: 1894–1901.
Xiang Y B, Zhang G L, Chi Y, Cai D Q, Wu Z Y. Fabrication of a controllable nanopesticide system with magnetic collectability. Chemical Engineering Journal, 2017; 328: 320–330.
Bhaskar R, Pandey S P, Kumar U, Kim H, Jayakodi S K, Gupta M K, et al. Nanobionics for sustainable crop production: Recent development to regulate plant growth and protection strategies from pests. OpenNano, 2024; 15: 100198.
Grillo R, Fraceto L F, Amorim M J, Scott-Fordsmand J J, Schoonjans R, Chaudhry Q. Ecotoxicological and regulatory aspects of environmental sustainability of nanopesticides. Journal of Hazardous Materials, 2021; 404: 124148.
Peixoto S, Henriques I, Loureiro S. Long-term effects of Cu （OH）2 nanopesticide exposure on soil microbial communities. Environmental Pollution, 2021; 269: 116113.
Kah M, Kookana R S, Gogos A, Bucheli T D. A critical evaluation of nanopesticides and nanofertilizers against their conventional analogues. Nature Nanotechnology, 2018; 13（8）: 677–684.
Dhanpal G N, Nagarjun P, Bai S K., Sindhu K K. Nanotechnology in weed management. The Mysore Journal of Agricultural Sciences, 2019; 53（4）: 1–10.
Shergill L S, Bejleri K, Davis A, Mirsky S B. Fate of weed seeds after impact mill processing in midwestern and mid-Atlantic United States. Weed Science, 2020; 68（1）: 92–97.
Copyright (c) 2024 International Journal of Agricultural and Biological Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.