Remote sensing and machine learning for crop water stress determination in various crops: a critical review

Virnodkar, Shyamal; Pachghare, Vinod; Patil, V. C.; Jha, Sunil Kumar

doi:10.1007/s11119-020-09711-9

Cited by 174 publications

(111 citation statements)

References 94 publications

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“…Due to the broad range of applications of ML in agriculture, several reviews have been published in this research field. The majority of these review studies have been dedicated to crop disease detection [ 13 , 14 , 15 , 16 ], weed detection [ 17 , 18 ], yield prediction [ 19 , 20 ], crop recognition [ 21 , 22 ], water management [ 23 , 24 ], animal welfare [ 25 , 26 ], and livestock production [ 27 , 28 ]. Furthermore, other studies were concerned with the implementation of ML methods regarding the main grain crops by investigating different aspects including quality and disease detection [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…Although ML in agriculture has made considerable progress, several open problems remain, which have some common points of reference, despite the fact that the topic covers a variety of sub-fields. According to [ 23 , 24 , 28 , 32 ], the main problems are associated with the implementation of sensors on farms for numerous reasons, including high costs of ICT, traditional practices, and lack of information. In addition, the majority of the available datasets do not reflect realistic cases, since they are normally generated by a few people getting images or specimens in a short time period and from a limited area [ 15 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, more practical datasets coming from fields are required [ 18 , 20 ]. Moreover, the need for more efficient ML algorithms and scalable computational architectures has been pointed out, which can lead to rapid information processing [ 18 , 22 , 23 , 31 ]. The challenging background, when it comes to obtaining images, video, or audio recordings, has also been mentioned owing to changes in lighting [ 16 , 29 ], blind spots of cameras, environmental noise, and simultaneous vocalizations [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

381

146

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The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

381

146

View full text Add to dashboard Cite

show abstract

“…An advanced pa of ML called Deep learning (DL) has been widely investigated for big data analysis wit remote sensing [22][23][24] and computer vision [25] applications. DL has been attracting a lo Machine learning (ML) has promptly become the standard for data processing especially in agriculture, thanks to its ability of to process large amounts of information in a non-linear framework [19,20]. Despite the significant achievements of ML application, the technique has a fundamental limitation in that performance is subject to the features used, the quality of data collected and the specific targeted application [21].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

2021

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Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

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“…Research has found that equivalent water thickness (EWT [18]), LFMC [30 ], and the relative water content (RWC [31]) of leaves can better reflect vegetation water status. Currently, commonly used vegetation moisture inversion methods include radiation transfer model inversion [32][33][34], traditional regression models [35][36][37], and machine learning models [38]. Yebra M et al [39] used radiation transfer model inversion to estimate fuel moisture contents from MODIS reflectivity data and established a flammability index through logistic regression modeling to map fire risk in Australia.…”

Section: Introductionmentioning

confidence: 99%

Simulating Heat Stress of Coal Gangue Spontaneous Combustion on Vegetation Using Alfalfa Leaf Water Content Spectral Features as Indicators

Wang

Zhao

Yang

et al. 2021

Preprint

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Background: Vegetation heat-stress assessment in the reclamation areas of coal gangue dumps is of great significance in controlling spontaneous combustion. Methods: The study simulated the heat-stress environment of a coal gangue dump reclamation area through a temperature gradient experiment. We collected leaf spectrum and water content data on alfalfa plants commonly planted in such areas. We then obtained the optimal spectral features of appropriate leaf water content indicators through time series analysis, correlation analysis, and least absolute shrinkage operator (lasso) regression analysis. A spectral feature-based long short-term memory (SF-LSTM) model is proposed to estimate alfalfa's heat stress level. Results: Comparing three leaf water content indicators, we found that the live fuel moisture content (LFMC) varies significantly with time and has high regularity. Correlation analysis of the raw spectrum, first-derivative spectrum, vegetation indexes and leaf water content data shows that LFMC and spectral data were the most strongly correlated. Combined with lasso regression analysis, the optimal spectral features were the first-derivative spectral value at 1661 nm (abbreviated as FDS (1661)), RVI (1525, 1771), DVI (1412, 740) and NDVI (1447,1803). When the classification strategies were divided into three categories and the time sequence length of the spectral features was set to five consecutive monitoring dates, the SF-LSTM model had the highest accuracy in estimating the heat stress level in alfalfa. The accuracy of the training set was > 95% and the accuracy of the verification set was about 90%. Conclusion: The results provide an important theoretical basis and technical support for vegetation heat-stress assessment in coal gangue dump reclamation areas.

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Remote sensing and machine learning for crop water stress determination in various crops: a critical review

Cited by 174 publications

References 94 publications

Machine Learning in Agriculture: A Comprehensive Updated Review

Machine Learning in Agriculture: A Comprehensive Updated Review

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

Simulating Heat Stress of Coal Gangue Spontaneous Combustion on Vegetation Using Alfalfa Leaf Water Content Spectral Features as Indicators

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