Crop mapping using supervised machine learning and deep learning: a systematic literature review

Machichi, Mouad Alami; Mansouri, Loubna El; Imani, Yasmina; Bourja, Omar; Lahlou, Ouiam; Zennayi, Yahya; Bourzeix, François; Houmma, Ismaguil Hanadé; Hadria, Rachid

doi:10.1080/01431161.2023.2205984

Cited by 19 publications

(3 citation statements)

References 200 publications

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“…In addition, several studies on the topic [21,[92][93][94][95][96] found that the LSTM architecture can be successfully applied to increase the overall accuracy of time series sequence classification using single or multi-source data and data filled with many different methods.In relation to LULC classification, Ref. [93] proposed a deep learning architecture based on the UNET model tested in 11 Sentinel-2 tiles using RF as the baseline model.…”

Section: Discussionmentioning

confidence: 99%

Imagery Time Series Cloud Removal and Classification Using Long Short Term Memory Neural Networks

Alonso-Sarria,

Valdivieso-Ros,

Gomariz-Castillo

2024

Remote Sensing

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The availability of high spatial and temporal resolution imagery, such as that provided by the Sentinel satellites, allows the use of image time series to classify land cover. Recurrent neural networks (RNNs) are a clear candidate for such an approach; however, the presence of clouds poses a difficulty. In this paper, random forest (RF) and RNNs are used to reconstruct cloud-covered pixels using data from other next in time images instead of pixels in the same image. Additionally, two RNN architectures are tested to classify land cover from the series, treating reflectivities as time series and also treating spectral signatures as time series. The results are compared with an RF classification. The results for cloud removal show a high accuracy with a maximum RMSE of 0.057 for RNN and 0.038 for RF over all images and bands analysed. In terms of classification, the RNN model obtained higher accuracy (over 0.92 in the test data for the best hyperparameter combinations) than the RF model (0.905). However, the temporal–spectral model accuracies did not reach 0.9 in any case.

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Section: Discussionmentioning

confidence: 99%

Imagery Time Series Cloud Removal and Classification Using Long Short Term Memory Neural Networks

Alonso-Sarria,

Valdivieso-Ros,

Gomariz-Castillo

2024

Remote Sensing

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“…In its development, the use of machine learning can be a method of extracting cropping frequency because it classifies based on the similarity of vegetation response patterns using the vegetation index. Unfortunately, several studies related to the use of machine learning have developed a lot for the identification of types of agricultural plants and only a few have used it for the identification of cropping frequency or planting patterns (Alami Machichi et al, 2023;Tariq et al, 2023;Tufail et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Untitled

2024

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Papua, known as one of the wettest regions in the world with annual precipitation ranging from 2400 to 4500 mm, faces a high risk of flooding, especially during La Niña, as observed in 2018 and 2022. Conversely, the region also experiences forest fires, mainly in the southern areas of Papua, during periods of extreme dry conditions brought about by El Niño events, as seen in 2002, 2004, and 2015. Given the increasing frequency of extreme climate events in the context of climate change, understanding the impact of global climate phenomena such as El Niño Southern Oscillation (ENSO) on Papua is crucial. This research aims to analyze the influence of ENSO and the Indian Ocean Dipole (IOD) on forest fires and flood risk in Papua, Indonesia. The analysis of forest fires utilizes MODIS hotspot and ERA5 precipitation data, employing quantitative modeling techniques such as Lasso and Elastic Net Regression. It integrates both ENSO and IOD indices into the precipitation indicators. In contrast, flood analysis is carried out through distribution and joint pattern analysis. The Elastic Net Regression yields promising results in modeling, with more than 96% of the tested models successfully predicting the total annual hotspots for each year, achieving an R-squared value of 90%. This suggests that the method and algorithm used can serve as a robust model for early hotspot prediction in the analyzed area. The warm phases of ENSO and IOD consistently exhibit a positive correlation with the dry season. However, the cold phases of these phenomena do not significantly impact heavy and extreme precipitation indicators in the studied region. The flood analysis reveals that La Niña has only a slight effect on all three precipitation patterns in the analyzed area, primarily by increasing the risk of extreme precipitation indicators. Conversely, negative IOD demonstrates inconsistency across all three precipitation patterns in Papua.

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“…Machine learning has long been applied in agriculture [1], and future technological developments in this industry heavily rely on automation. Applications of machine learning in agriculture include mapping [2,3], crop management [4][5][6], precision irrigation and spraying [7][8][9], livestock management [10][11][12], automated farm management [13][14][15], and pest control [16][17][18][19][20][21]. Machine learning applied to pest control is seen as a long-term solution to different problems in agriculture, such as non-destructive testing of the presence of insects inside the fruit [22,23].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Classification of Mango Pulp Weevil Activity Utilizing an Acoustic Sensor

Banlawe,

dela Cruz

2023

Micromachines

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The mango pulp weevil (MPW) is an aggressive pest that mates seasonally according to the cycle of the mango fruit. After discovering the existence of the mango pulp weevil in Palawan, the island has been under quarantine for exporting mangoes. Detection of the pest proves difficult as the pest does not leave a physical sign that the mango has been damaged. Infested mangoes are wasted as they cannot be sold due to damage. This study serves as a base study for non-invasive mango pulp weevil detection using MATLAB machine learning and audio feature extraction tools. Acoustic sensors were evaluated for best-fit use in the study. The rationale for selecting the acoustic sensors includes local availability and accessibility. Among the three sensors tested, the MEMS sensor had the best result. The data for acoustic frequency are acquired using the selected sensor, which is placed inside a soundproof chamber to minimize the noise and isolate the sound produced by each activity. The identified activity of the adult mango pulp weevil includes walking, resting, and mating. The Mel-frequency cepstral coefficient (MFCC) was used for feature extraction of the recorded audio and training of the SVM classifier. The study achieved 89.81% overall accuracy in characterizing mango pulp weevil activity.

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Crop mapping using supervised machine learning and deep learning: a systematic literature review

Cited by 19 publications

References 200 publications

Imagery Time Series Cloud Removal and Classification Using Long Short Term Memory Neural Networks

Imagery Time Series Cloud Removal and Classification Using Long Short Term Memory Neural Networks

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Machine Learning-Based Classification of Mango Pulp Weevil Activity Utilizing an Acoustic Sensor

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