Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco

Lin, Chenxi; Jin, Zhenong; Mulla, D. J.; Ghosh, Rahul; Guan, Kaiyu; Kumar, Vipin; Cai, Yaping

doi:10.3390/rs13091740

Cited by 22 publications

(10 citation statements)

References 64 publications

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“…Two classifiers have been developed and evaluated, one at the sub-image level and the other at the olive grove farm-level. In both cases, the CNN was composed of 4 consecutive convolutional operations with (32,64,128,256) filters each (see Figure 8). Every filter had a kernel size operation of (3 × 3).…”

Section: Planting System Classifier Based On Convolutional Neural Net...mentioning

confidence: 99%

“…Although images obtained from satellites have the advantage of covering wide areas quickly, delineation studies of olive tree crowns with these images alone are scarcer given the lower availability of satellite images with very high spatial resolution [32][33][34]. In some cases, it is necessary to implement complementary techniques, such as pan-sharpening fusion techniques, to allow obtaining images with higher spatial resolution than the originals [35,36].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Planting Systems in Olive Groves Based on Open-Source, High-Resolution Images and Convolutional Neural Networks

et al. 2022

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This paper aims to evaluate whether an automatic analysis with deep learning convolutional neural networks techniques offer the ability to efficiently identify olive groves with different intensification patterns by using very high-resolution aerial orthophotographs. First, a sub-image crop classification was carried out. To standardize the size and increase the number of samples of the data training (DT), the crop images were divided into mini-crops (sub-images) using segmentation techniques, which used a different threshold and stride size to consider the mini-crop as suitable for the analysis. The four scenarios evaluated discriminated the sub-images efficiently (accuracies higher than 0.8), obtaining the largest sub-images (H = 120, W = 120) for the highest average accuracy (0.957). The super-intensive olive plantings were the easiest to classify for most of the sub-image sizes. Nevertheless, although traditional olive groves were discriminated accurately, too, the most difficult task was to distinguish between the intensive plantings and the traditional ones. A second phase of the proposed system was to predict the crop at farm-level based on the most frequent class detected in the sub-images of each crop. The results obtained at farm level were slightly lower than at the sub-images level, reaching the highest accuracy (0.826) with an intermediate size image (H = 80, W = 80). Thus, the convolutional neural networks proposed made it possible to automate the classification and discriminate accurately among traditional, intensive, and super-intensive planting systems.

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Section: Planting System Classifier Based On Convolutional Neural Net...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Planting Systems in Olive Groves Based on Open-Source, High-Resolution Images and Convolutional Neural Networks

et al. 2022

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“…GeoEye, WorldView-2, and -3 images were used to detect OOMW disposal areas [57] and monitor olive trees' health status using VIs [96,97]. Another important commercial source of data, albeit limitedly used RS research in olive growing [41,98,99], is the Planetscope constellation launched in the middle of the past decade. PlanetScope constellation is composed of many small nano-satellites, 3U CubeSats, also called "Doves" and equipped with MS sensors providing up to 3 m spatial resolution with a one-day revisiting time [100,101].…”

Section: Satellitesmentioning

confidence: 99%

“…The introduction of new, relatively recent platforms, including nano-satellites, and mounting sensors providing high or ultra-high resolution images, less than 3 m and 1 m, respectively, have made satellites more competitive with UAVs in PA applications [15]. However, to our knowledge, only three papers dealing with olive groves used nanosatellite images such as Cubesats from PlanetScope [41,98,99]. This is despite the affordable price of the images compared to satellites with similar or higher resolution, such as Pleiades and WorldView [272].…”

Section: Final Remarks and Conclusionmentioning

confidence: 99%

Twenty Years of Remote Sensing Applications Targeting Landscape Analysis and Environmental Issues in Olive Growing: A Review

Messina

Modica

2022

Remote Sensing

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The olive (Olea europaea L.) is an iconic tree linked to the birth of some of the most ancient civilizations and one of the most important cultivated tree species in the Mediterranean basin. Over the last few decades, given the high socio-economic importance of the olive sector, there has been much research involving remote sensing (RS) applications in olive growing, especially in precision agriculture. This review article is part of a review that aims to cover the past, from the 2000s onwards, and the most recent applications of remote sensing (RS) in olive growing to be able to include research and all topics related to the use of RS on olive trees. As far as the use of RS platforms such as satellites, aircraft, and unmanned aerial vehicles (UAVs) in olive growing is concerned, a review of the literature showed the presence of several works devoted to it. A brief introduction on the history of the olive tree and its distribution and cultivation around the world, together with a summary of the leading RS platforms (a good portion of which are satellites) used in olive research, anticipates the discussion of four topics about olive growing that have as their common thread positive (and non-positive) impacts on the environment: preservation of olive landscape and soil erosion, identification of olive groves, olive oil mill wastewater (OOMW) and relative environmental risks, irrigation water management and the use of RS platforms for water stress monitoring. The preservation of olive groves as an element of Mediterranean identity and strategic economic resource in agriculture depends on sustainable environmental management alongside technological advances brought by precision agriculture.

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“…The correct acquisition of images is necessary so that the regions of interest are of good quality. Various vectors have been used for image acquisition, such as human operators with cameras or smartphones, fixed cameras, cameras on land vehicles, aerial vehicles (autonomous or not), and satellites ( Lin et al, 2021 ). Collecting image data in a complex 3D space, such as an orchard, is a relatively recent challenge made possible by the recent development of new technologies.…”

Section: Introductionmentioning

confidence: 99%

Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review

Popescu,

Ichim,

Stoican

2023

Front. Plant Sci.

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Orchard monitoring is a vital direction of scientific research and practical application for increasing fruit production in ecological conditions. Recently, due to the development of technology and the decrease in equipment cost, the use of unmanned aerial vehicles and artificial intelligence algorithms for image acquisition and processing has achieved tremendous progress in orchards monitoring. This paper highlights the new research trends in orchard monitoring, emphasizing neural networks, unmanned aerial vehicles (UAVs), and various concrete applications. For this purpose, papers on complex topics obtained by combining keywords from the field addressed were selected and analyzed. In particular, the review considered papers on the interval 2017-2022 on the use of neural networks (as an important exponent of artificial intelligence in image processing and understanding) and UAVs in orchard monitoring and production evaluation applications. Due to their complexity, the characteristics of UAV trajectories and flights in the orchard area were highlighted. The structure and implementations of the latest neural network systems used in such applications, the databases, the software, and the obtained performances are systematically analyzed. To recommend some suggestions for researchers and end users, the use of the new concepts and their implementations were surveyed in concrete applications, such as a) identification and segmentation of orchards, trees, and crowns; b) detection of tree diseases, harmful insects, and pests; c) evaluation of fruit production, and d) evaluation of development conditions. To show the necessity of this review, in the end, a comparison is made with review articles with a related theme.

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Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco

Cited by 22 publications

References 64 publications

Detection of Planting Systems in Olive Groves Based on Open-Source, High-Resolution Images and Convolutional Neural Networks

Detection of Planting Systems in Olive Groves Based on Open-Source, High-Resolution Images and Convolutional Neural Networks

Twenty Years of Remote Sensing Applications Targeting Landscape Analysis and Environmental Issues in Olive Growing: A Review

Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review

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