Remote sensing for agricultural applications: A meta-review

Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild. Despite their ubiquity, these real-world distribution shifts are under-represented in the datasets widely used in the ML community today. To address this gap, we present WILDS, a curated collection of 8 benchmark datasets that reflect a diverse range of distribution shifts which naturally arise in real-world applications, such as shifts across hospitals for tumor identification; across camera traps for wildlife monitoring; and across time and location in satellite imaging and poverty mapping. On each dataset, we show that standard training results in substantially lower out-of-distribution than in-distribution performance, and that this gap remains even with models trained by existing methods for handling distribution shifts. This underscores the need for new training methods that produce models which are more robust to the types of distribution shifts that arise in practice. To facilitate method development, we provide an open-source package that automates dataset loading, contains default model architectures and hyperparameters, and standardizes evaluations. Code and leaderboards are available at this https URL.

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

http://diposit.ub.edu/dspace/bitstream/2445/152621/1/677195.pdf

Translating High-Throughput Phenotyping into Genetic Gain

Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

/pdf/unmanned-aerial-vehicle-remote-sensing-for-field-based-crop-4442qummes.pdf

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

Current approaches to field phenotyping are laborious or permit the use of only a few sensors at a time. In an effort to overcome this, a fully automated robotic field phenotyping platform with a dedicated sensor array that may be accurately positioned in three dimensions and mounted on fixed rails has been established, to facilitate continual and high-throughput monitoring of crop performance. Employed sensors comprise of high-resolution visible, chlorophyll fluorescence and thermal infrared cameras, two hyperspectral imagers and dual 3D laser scanners. The sensor array facilitates specific growth measurements and identification of key growth stages with dense temporal and spectral resolution. Together, this platform produces a detailed description of canopy development across the crops entire lifecycle, with a high-degree of accuracy and reproducibility.

/pdf/field-scanalyzer-an-automated-robotic-field-phenotyping-4kt6vi2hpp.pdf

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

Recording growth stage information is an important aspect of precision agriculture, crop breeding and phenotyping. In practice, crop growth stage is still primarily monitored by-eye, which is not only laborious and time-consuming, but also subjective and error-prone. The application of computer vision on digital images offers a high-throughput and non-invasive alternative to manual observations and its use in agriculture and high-throughput phenotyping is increasing. This paper presents an automated method to detect wheat heading and flowering stages, which uses the application of computer vision on digital images. The bag-of-visual-word technique is used to identify the growth stage during heading and flowering within digital images. Scale invariant feature transformation feature extraction technique is used for lower level feature extraction; subsequently, local linear constraint coding and spatial pyramid matching are developed in the mid-level representation stage. At the end, support vector machine classification is used to train and test the data samples. The method outperformed existing algorithms, having yielded 95.24, 97.79, 99.59% at early, medium and late stages of heading, respectively and 85.45% accuracy for flowering detection. The results also illustrate that the proposed method is robust enough to handle complex environmental changes (illumination, occlusion). Although the proposed method is applied only on identifying growth stage in wheat, there is potential for application to other crops and categorization concepts, such as disease classification.

/pdf/automated-method-to-determine-two-critical-growth-stages-of-mm7rp59k5w.pdf

Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering.

Maximizing NO3- uptake during seedling development is important as it has a major influence on plant growth and yield. However, little is known about the processes leading to, and involved in, the initiation of root NO3- uptake capacity in developing seedlings. This study examines the physiological processes involved in root NO3- uptake and metabolism, to gain an understanding of how the NO3- uptake system responds to meet demand as maize seedlings transition from seed N use to external N capture. The concentrations of seed-derived free amino acids within root and shoot tissues are initially high, but decrease rapidly until stabilizing eight days after imbibition (DAI). Similarly, shoot N% decreases, but does not stabilize until 12-13 DAI. Following the decrease in free amino acid concentrations, root NO3- uptake capacity increases until shoot N% stabilizes. The increase in root NO3- uptake capacity corresponds with a rapid rise in transcript levels of putative NO3- transporters, ZmNRT2.1 and ZmNRT2.2. The processes underlying the increase in root NO3- uptake capacity to meet N demand provide an insight into the processes controlling N uptake.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jipb.12525

Transition from a maternal to external nitrogen source in maize seedlings

Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments. In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1) comparison with ground-truth images, (2) variation along a day with changes in ambient illumination, (3) comparison with manual measurements and (4) an estimation of performance along the full life cycle of a wheat canopy. The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc.

/pdf/multi-feature-machine-learning-model-for-automatic-4s8cjk4c1s.pdf

Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping.

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Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering.

Transition from a maternal to external nitrogen source in maize seedlings

Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping.