Using UAV to Identify the Optimal Vegetation Index for Yield Prediction of Oil Seed Rape (Brassica napus L.) at the Flowering Stage

Suitability of the vegetation indices of normalized difference vegetation index (NDVI), blue normalized difference vegetation index (BNDVI), and normalized difference yellowness index (NDYI) obtained by means of UAV at the flowering stage of oil seed rape for the prediction of seed yield and usability of these vegetation indices in the identification of anomalies in the condition of the flowering growth were verified based on the regression analysis. Correlation analysis was performed to find the degree of yield dependence on the values of NDVI, BNDVI, and NDYI indices, which revealed a strong, significant linear positive dependence of seed yield on BNDVI (R = 0.98) and NDYI (R = 0.95). The level of correlation between the NDVI index and the seed yield was weaker (R = 0.70) than the others. Regression analysis was performed for a closer determination of the functional dependence of NDVI, BNDVI, and NDYI indices and the yield of seeds. Coefficients of determination in the linear regression model of NDVI, BNDVI, and NDYI indices reached the following values: R2 = 0.48 (NDVI), R2 = 0.95 (BNDVI), and R2 = 0.90 (NDYI). Thus, it was shown that increased density of yellow flowers decreased the relationship between NDVI and crop yield. The NDVI index is not appropriate for assessing growth conditions and prediction of yields at the flowering stage of oil seed rape. High accuracy of yield prediction was achieved with the use of BNDVI and NDYI. The performed analysis of NDVI, BNDVI, and NDYI demonstrated that particularly the BNDVI and NDYI indices can be used to identify problems in the development of oil seed rape growth at the stage of flowering, for their precise localization, and hence to targeted and effective remedial measures in line with the principles of precision agriculture.O

Quantifying Floral Resource Availability Using Unmanned Aerial Systems and Machine Learning Classifications to Predict Bee Community Structure

Bees are important for agricultural and non-agricultural ecosystems because they pollinate both wild plants and commercial crops. Flowers provide pollen and nectar resources that bees use to survive and reproduce. Measuring the relationship between the floral community and bee community may help apiarists and land managers to make informed decisions in managing wild and domesticated bee species. Manual methods to describe and count flowering vegetation is costly in time and personnel. Unmanned aerial vehicle (UAV) technology may be an efficient way to describe and count flowering vegetation on a large scale. UAVs with classification analysis and ground transect surveys were used to describe the variation in the flower communities at three field sites in non-agricultural environments. The variation in bee communities were also recorded at the field sites. Seven unique flower species were quantified using UAVs. Using the UAV imagery, it was determined that the period of flowering and changes of flower coverage for different species varied. Twenty-two unique flower species were described and counted using the ground transect surveys and 136 bees from 11 genera were recorded using net surveys. I tested the hypothesis that increased bee diversity and abundance would positively correlate with increased floral diversity and abundance using seven simple linear regression models. I found that the floral resource data collected from ground transect surveys predicts bee diversity, bee richness, and bee abundance. I also found floral abundance data captured by UAVs predicts bee abundance at the field sites. Finally, I found UAV floral abundance predicts ground transect floral abundance suggesting a positive relationship between different sampling methods. My results support previous research that suggests a high diversity of resources will support a high diversity of insects; and habitats with abundant flowers have greater possibilities for partitioning of available resources. My results also support UAVs as an efficient method for describing and counting floral resources in non-agricultural settings. Further research should include using UAV imagery to count flowers to predict bee communities on a landscape scale

An Agave Counting Methodology Based on Mathematical Morphology and Images Acquired through Unmanned Aerial Vehicles

Blue agave is an important commercial crop in Mexico, and it is the main source of the traditional mexican beverage known as tequila. The variety of blue agave crop known as Tequilana Weber is a crucial element for tequila agribusiness and the agricultural economy in Mexico. The number of agave plants in the field is one of the main parameters for estimating production of tequila. In this manuscript, we describe a mathematical morphology-based algorithm that addresses the agave automatic counting task. The proposed methodology was applied to a set of real images collected using an Unmanned Aerial Vehicle equipped with a digital Red-Green-Blue (RGB) camera. The number of plants automatically identified in the collected images was compared to the number of plants counted by hand. Accuracy of the proposed algorithm depended on the size heterogeneity of plants in the field and illumination. Accuracy ranged from 0.8309 to 0.9806, and performance of the proposed algorithm was satisfactory.This research was supported by the Spanish Ministerio de Economía y Competitividad, contract TIN2015-64395-R (MINECO/FEDER, UE), as well as by the Basque Government, contract IT900-16. This work was also supported in part by CONACYT (Mexico), grant 258033

Assessment of vegetation índices derived from UAV images for predicting biometric variables in bean during ripening stage

Here, we report the prediction of vegetative stages variables of canary bean crop employing RGB and multispectral images obtained from UAV during the ripening stage, correlating the vegetation indices with biometric variables measured manually in the field. Results indicated a highly significant correlation of plant height with eight vegetation indices derived from UAV images from the canary bean, which were evaluated by multiple regression models, obtaining a maximum correlation of R2 = 0.79. On the other hand, the estimated indices of multispectral images did not show significant correlations

A survey of image-based computational learning techniques for frost detection in plants

Frost damage is one of the major concerns for crop growers as it can impact the growth of the plants and hence, yields. Early detection of frost can help farmers mitigating its impact. In the past, frost detection was a manual or visual process. Image-based techniques are increasingly being used to understand frost development in plants and automatic assessment of damage resulting from frost. This research presents a comprehensive survey of the state-of the-art methods applied to detect and analyse frost stress in plants. We identify three broad computational learning approaches i.e., statistical, traditional machine learning and deep learning, applied to images to detect and analyse frost in plants. We propose a novel taxonomy to classify the existing studies based on several attributes. This taxonomy has been developed to classify the major characteristics of a significant body of published research. In this survey, we profile 80 relevant papers based on the proposed taxonomy. We thoroughly analyse and discuss the techniques used in the various approaches, i.e., data acquisition, data preparation, feature extraction, computational learning, and evaluation. We summarise the current challenges and discuss the opportunities for future research and development in this area including in-field advanced artificial intelligence systems for real-time frost monitoring

Prediction of biometric variables through multispectral images obtained from UAV in beans (Phaseolus vulgaris L.) during ripening stage

Here, we report the prediction of vegetative stages variables of canary bean crop by means of RGB and multispectral images obtained from UAV during the ripening stage, correlating the vegetation indices with biometric variables measured manually in the field. Results indicated a highly significant correlation of plant height with eight RGB image vegetation indices for the canary bean crop, which were used for predictive models, obtaining a maximum correlation of R2 = 0.79. On the other hand, the estimated indices of multispectral images did not show significant correlations

Yielding to the image: how phenotyping reproductive growth can assist crop improvement and production

Reproductive organs are the main reason we grow and harvest most plant species as crops, yet they receive less attention from phenotyping due to their complexity and inaccessibility for analysis. This review highlights recent progress towards the quantitative high-throughput phenotyping of reproductive development, focusing on three impactful areas that are pivotal for plant breeding and crop production. First, we look at phenotyping phenology, summarizing the indirect and direct approaches that are available. This is essential for analysis of genotype by environment, and to enable effective management interpretation and agronomy and physiological interventions. Second, we look at pollen development and production, in addition to anther characteristics, these are critical points of vulnerability for yield loss when stress occurs before and during flowering, and are of particular interest for hybrid technology development. Third, we elaborate on phenotyping yield components, indirectly or directly during the season, with a numerical or growth related approach and post-harvest processing. Finally, we summarise the opportunities and challenges ahead for phenotyping reproductive growth and their feasibility and impact, with emphasis on plant breeding applications and targeted yield increases