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Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.
Walter A, Liebisch F, Hund A. Plant phenotyping: From bean weighing to image analysis. Plant Methods. 2015;11:14.
Pandey MK, Roorkiwal M, Singh VK. Emerging genomic tools for legume breeding: Current status and future perspectives. Front Plant Sci. 2020;11:589.
Yang AN, Ouyang H, Nkurikiyimfura O, Fang H, Waheed A, Li W, Wang YP, Zhan J. Genetic variation along an altitudinal gradient in the phytophthora infestans effector gene pi02860. Front Microbiol. 2022;13:484.
Rabieifaradonbeh M, Afsharifar A, Finetti-Sialer MM. Molecular and functional characterization of the barley yellow striate mosaic virus genes encoding phosphoprotein, p3, p6 and p9. Eur J Plant Pathol. 2021;161:1–15.
Mohanty SP, Hughes DP, Salathé M. Using deep learning for image-based plant disease detection. Front Plant Sci. 2016;7:1419.
Kamilaris A, Prenafeta-Boldú FX. Deep learning in agriculture: A survey. Comput Electron Agric. 2018;147:70–90.
Gao Z, Luo Z, Zhang W, Lv Z, Xu Y. Deep learning application in plant stress imaging: A review. AgriEngineering. 2020;2(3):430–446.
Mahlein A-K, Steiner U, Hillnhütter C, Dehne H-W, Oerke E-C. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods. 2012;8:3.
Kuska MT, Mahlein A-K. Hyperspectral imaging for plant disease detection and classification: Current status and future perspectives. Plant Dis. 2019;103:2028–2037.
Taheri M, D’Haese M, Fiems D, Hosseininia GH, Azadi H. Wireless sensor network for small-scale farming systems in Southwest Iran: Application of q-methodology to investigate farmers’ perceptions. Comput Electron Agric. 2020;177:Article 105682.
Singh A, Jones S, Ganapathysubramanian B, Sarkar S, Mueller D, Sandhu K, Nagasubramanian K. Challenges and opportunities in machine-augmented plant stress phenotyping. Trends Plant Sci. 2021;26(1):53–69.
Li L, Zhang Q, Huang D. A review of imaging techniques for plant phenotyping. Sensors. 2014;14(11):20078–20111.
Krüger J, Lausberger C, von Nostitz-Wallwitz I, Saake G, Leich T, Search. review. Repeat? An empirical study of threats to replicating slr searches. Empir Softw Eng. 2020;25(3):627 –677.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, et al. The prisma 2020 statement: An updated guideline for reporting systematic reviews. Int J Surg. 2021;88:Article 105906.
Saranya A, Subhashini R. A systematic review of explainable artificial intelligence models and applications: Recent developments and future trends. Decis Anal J. 2023;7:Article 100230.
Fossum ER. Active pixel sensors: Are ccds dinosaurs? Proc IEEE. 1993;81:1004–1010.
Gracia-Romero A, Kefauver SC, Vergara-Díaz O, Zaman-Allah MA, Prasanna BM, Cairns JE, Araus JL. Comparative performance of ground vs. aerially assessed rgb and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization. Front Plant Sci. 2017;8:2004.
Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;3(6):610–621.
Fuentes S, Poblete-Echeverría C, Ortega-Farías S. Leaf area index estimation in vineyards using a ground-based lidar scanner. Sensors. 2016;16:2100.
Arnal Barbedo JG. Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springerplus. 2013;2(1):660.
Zhang C, Kovacs JM, Lichti DD. Review of topographic and 3d imaging systems for plant phenotyping. Sensors. 2016;16:1–29.
Serbin SP, Singh A, Desai AR, Dubois SG, Jablonski AD, Kingdon CC, Kruger EL, Townsend PA. Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. Remote Sens Environ. 2015;167:78–87.
Gitelson AA, Merzlyak MN, Chivkunova OB. Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem Photobiol. 2009;74(1):38–45.
Zarco-Tejada PJ, González-Dugo V, Berni JAJ. Fluorescence, temperature and narrow-band indices acquired from a uav platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens Environ. 2012;117:322–337.
Mahlein A-K, Kuska MT, Thomas S, Wahabzada M, Behmann J, Rascher U, Kersting K. Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: Seamless combination of spectral and spatial features for plant disease detection. Sensors. 2014;14:19907–19933.
Prasad A, Sedgley M, Jutte N. Fluorescence imaging spectroscopy (fis) for disease detection in plants: A review. Aust J Agric Res. 2008;59:649–664.
Gray GR, Hope BJ, Qin X, Taylor BG, Whitehead CL. The characterization of photoinhibition and recovery during cold acclimation in arabidopsis thaliana using chlorophyll fluorescence imaging. Physiol Plant. 2003;119(3):365–375.
Li Y, Li B, Li H, Wei Y. Infrared thermal imaging: Fundamentals, research and applications. J Phys D Appl Phys. 2018;51:Article 503001.
Haboudane D, Miller JR, Tremblay N, Zarco-Tejada PJ, Dextraze L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens Environ. 2002;81(2-3):416–426.
Costa J, Grant O, Chaves M. Thermography to explore plant-environment interactions. J Exp Bot. 2013;64(13):3937–3949.
Gerhards M, Schlerf M, Mallick K, Udelhoven T. Challenges and future perspectives of multi-/hyperspectral thermal infrared remote sensing for crop water-stress detection: A review. Remote Sens. 2019;11(10):1240.
Vadivambal R, Jayas D. Applications of thermal imaging in agriculture and food industry–a review. Food Bioprocess Technol. 2010;4(2):186–199.
Jiang S, Tang L, Ju H. Dynamic monitoring of rs and gis resources and ecological environment based on high temperature materials. IOP Conf Ser: Mater Sci Eng. 2020;772:Article 012047.
Zhai J, Jin D, Zhou Y, Chen Y, Gao H. Assessment of changes in key ecosystem factors and water conservation with remote sensing in the zoige. Diversity. 2022;14(7):552.
Chybicki A, Łubniewski Z. Optimized avhrr land surface temperature downscaling method for local scale observations: Case study for the coastal area of the gulf of gdańsk. Open Geosci. 2017;9(1):32.
Marta A, Grifoni D, Mancini M, Orlando F, Guasconi F, Orlandini S. Durum wheat in-field monitoring and early-yield prediction: Assessment of potential use of high resolution satellite imagery in a hilly area of tuscany, Central Italy. J Agric Sci. 2013;153(1):68–77.
Berni J, Deery DM, Rozas-Larraondo P, Condon AG, Rebetzke GJ, James RA, Bovill WD, Furbank RT, Sirault XRR. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with lidar. Front Plant Sci. 2018;9:237.
Su Y, Wu F, Ao Z, Jin S, Qin F, Liu B, Pang S, Liu L, Guo Q. Evaluating maize phenotype dynamics under drought stress using terrestrial lidar. Plant Methods. 2019;15:11.
Vierling K, Vierling L, Gould W, Martinuzzi S, Clawges R. Lidar: Shedding new light on habitat characterization and modeling. Front Ecol Environ. 2008;6(2):90–98.
Kim JY, Glenn DM. Multi-modal sensor system for plant water stress assessment. Comput Electron Agric. 2017;141:27–34.
Russell SJ, Norvig P. Artificial intelligence: A modern approach. 3rd ed. Upper Saddle River (NJ): Pearson; 2016.
Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: Data mining, inference, and prediction. 2nd ed. New York (NY): Springer; 2009.
Bishop CM. Pattern recognition and machine learning . New York (NY): Springer; 2006.
Mitchell TM. Machine learning. New York (NY): McGraw-Hill; 1997.
Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255–260.
Gehan MA, Fahlgren N, Abbasi A, Berry JC, Callen ST, Chavez L, Doust AN, Feldman MJ, Gilbert KB, Hodge JG, et al. Plantcv v2: Image analysis software for high-throughput plant phenotyping. PeerJ. 2017;5:Article e4088.
Lipton ZC. The mythos of model interpretability. Queue. 2018;16:30–57.
Fuentes A, Yoon S, Park DS. Deep learning for plant identification using vein morphological patterns. Comput Electron Agric. 2017;138:146–159.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–444.
Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw. 2015;61:85–117.
Žibrat U, Oštir K, Kokalj Š, Polajnar J. Plant pests and disease detection using optical sensors / daljinsko zaznavanje rastlinskih bolezni in škodljivcev. Folia Biol et Geol. 2019;60(2):57–68.
Ghosh S, Das S. Deep learning for feature extraction and classification of electroencephalogram signals. J Med Syst. 2019;43:61.
Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D. Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci. 2016;2016.
Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge (MA): MIT Press; 2016.
Van De Schoot R et al. An open source machine learning framework for efficient and transparent systematic reviews. Nature machine intelligence. 2021;3:125–133.
Ramanjot, Mittal U, Wadhawan A, Singla J, Jhanjhi NZ, Ghoniem RM, Ray SK, Abdelmaboud A. Plant disease detection and classification: A systematic literature review. Sensors. 2023;23(10):4769.
Nagaraju M, Chawla P. Systematic review of deep learning techniques in plant disease detection. Int J Syst Assur Eng Manag. 2020;11:547–560.
Mewes T, Franke J, Menz G. Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection. Precis Agric. 2011;12:795–812.
Nagasubramanian K, Jones S, Singh AK, Sarkar S, Singh A, Ganapathysubramanian B. Plant disease identification using explainable 3d deep learning on hyperspectral images. Plant Methods. 2019;15:1–10.
Hughes DP, Salathé M. An open access repository of images on plant health to enable the development of mobile disease diagnostics. BMC Plant Biol. 2015;15:1–11.
Prajapati HB, Shah JP, Dabhi VK. Detection and classification of rice plant diseases. Intell Dec Technol. 2017;11:357–373.
Xu Y, Kong S, Gao Z, Chen Q, Jiao Y, Li C. Hlnet model and application in crop leaf diseases identification. Sustainability. 2022;14(14):8915.
Wen J, Shi Y, Zhou X, Xue Y. Crop disease classification on inadequate low-resolution target images. Sensors. 2020;20(16):4601.
Cristin R, Kumar BS, Priya C, Karthick K. Deep neural network based rider-cuckoo search algorithm for plant disease detection. Artif Intell Rev. 2020;53:4993–5018.
Alguliyev R, Imamverdiyev Y, Sukhostat L, Bayramov R. Plant disease detection based on a deep model. Soft Comput. 2021;25:13229–13242.
Chen J, Zhang D, Nanehkaran YA. Identifying plant diseases using deep transfer learning and enhanced lightweight network. Multimed Tools Appl. 2020;79:31497–31515.
Mahlein A-K. Plant disease detection by imaging sensors. Sensors. 2016;16:1074.
Tovar JC, Hoyer JS, Lin A, Tielking A, Callen ST, Elizabeth Castillo S, Miller M, Tessman M, Fahlgren N, Carrington JC, et al. Raspberry pi–powered imaging for plant phenotyping. Appl Plant Sci. 2018;6(3):Article e1031.
Rangarajan AK, Balu EJ, Boligala MS, Jagannath A, Ranganathan BN. A low-cost uav for detection of cercospora leaf spot in okra using deep convolutional neural network. Multimed Tools Appl. 2022;81(15):21565–21589.
Sun D, Zhu Y, Xu H, He Y, Cen H. Time-series chlorophyll fluorescence imaging reveals dynamic photosynthetic fingerprints of sos mutants to drought stress. Sensors. 2019;19(12):2649.
Raza S-E-A, Prince G, Clarkson JP, Rajpoot NM (Eds). Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS One. 2015;10(4):Article e0123262.
Yuan L, Pu R, Zhang J, Wang J, Yang H. Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale. Precis Agric. 2016;17(3):332–348.
Husin NA, Khairunniza-Bejo S, Abdullah AF, Kassim MSM, Ahmad D, Aziz MHA. Classification of basal stem rot disease in oil palm plantations using terrestrial laser scanning data and machine learning. Agronomy. 2020;10(11):1624.
Jones HG. Irrigation scheduling: Advantages and pitfalls of plant-based methods. J Exp Bot. 2004;55(407):2427–2436.
Chandel NS, Chakraborty SK, Rajwade YA, Dubey K, Tiwari MK, Jat D. Identifying crop water stress using deep learning models. Neural Comput & Applic. 2021;33:5353–5367.
Xu Z, Guo X, Zhu A, He X, Zhao X, Han Y, Subedi R. Using deep convolutional neural networks for image-based diagnosis of nutrient deficiencies in rice. Comput Intell Neurosci. 2020;2020:7307252.
Poobalasubramanian M, Park ES, Faqeerzada MA, Kim T, Kim MS, Baek I, Cho BK. Identification of early heat and water stress in strawberry plants using chlorophyll-fluorescence indices extracted via hyperspectral images. Sensors. 2022;22(22):8706.
Sibiya M, Sumbwanyambe M. Automatic fuzzy logic-based maize common rust disease severity predictions with thresholding and deep learning. Pathogens. 2021;10(2):131.
Uğuz S, Uysal N. Classification of olive leaf diseases using deep convolutional neural networks. Neural Comput & Applic. 2021;33:4133–4149.
Prabu M, Chelliah BJ. Mango leaf disease identification and classification using a cnn architecture optimized by crossover-based levy flight distribution algorithm. Neural Comput & Applic. 2022;34(9):7311–7324.
Di J, Li Q. A method of detecting apple leaf diseases based on improved convolutional neural network. PLoS One. 2022;17(2):Article e0262629.
Pan Q, Gao M, Wu P, Yan J, AbdelRahman MA. Image classification of wheat rust based on ensemble learning. Sensors. 2022;22(16):6047.
Selvaraj MG, Vergara A, Ruiz H, Safari N, Elayabalan S, Ocimati W, Blomme G. Ai-powered banana diseases and pest detection. Plant Methods. 2019;15(1):1–11.
Liu J, Wang X. Early recognition of tomato gray leaf spot disease based on mobilenetv2-yolov3 model. Plant Methods. 2020;16:1–16.
Abdulridha J, Ehsani R, Abd-Elrahman A, Ampatzidis Y. A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses. Comput Electron Agric. 2019;156:549–557.
Gayathri Devi T, Neelamegam P. Image processing based rice plant leaves diseases in Thanjavur, tamilnadu. Clust Comput. 2019;22(5):13415–13428.
Cohen B, Edan Y, Levi A, Alchanatis V. Early detection of grapevine (vitis vinifera) downy mildew (peronospora) and diurnal variations using thermal imaging. Sensors. 2022;22(9):3585.
Martinez-Martinez V, Gomez-Gil J, Machado ML, Pinto FA. Leaf and canopy reflectance spectrometry applied to the estimation of angular leaf spot disease severity of common bean crops. PLoS One. 2018;13(4):Article e0196072.
Wang T, Thomasson JA, Isakeit T, Yang C, Nichols RL. A plant-by-plant method to identify and treat cotton root rot based on uav remote sensing. Remote Sens. 2020;12(15):2453.
Kurmi Y, Gangwar S, Chaurasia V, Goel A. Leaf images classification for the crops diseases detection. Multimed Tools Appl. 2022;81:8155–8178.
Savian F, Martini M, Ermacora P, Paulus S, Mahlein A-K. Prediction of the kiwifruit decline syndrome in diseased orchards by remote sensing. Remote Sens. 2020;12(14):2194.
Subramanian M, Shanmugavadivel K, Nandhini P. On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Comput & Applic. 2022;34(3):13951–13968.
Vallabhajosyula S, Sistla V, Kolli VKK. Transfer learning-based deep ensemble neural network for plant leaf disease detection. J Plant Dis Protect. 2022;129(3):545–558.
Fraiwan M, Faouri E, Khasawneh N. Classification of corn diseases from leaf images using deep transfer learning. Plan Theory. 2022;11(20):2668.
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