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Introduction: Agricultural technologies developed by national and international research institutions were not benefiting the rural population of Ethiopia to the extent desired. As a response, integrated agricultural extension approaches are proposed as a key strategy to transform the smallholder farming sector. Improving Productivity and Market Success (IPMS) of Ethiopian Farmers project is one of the development projects initiated by integrating productivity enhancement technological schemes with market development model. This paper explores the impact of the project intervention in the smallholder farmers’ wellbeing. Methods: To test the research hypothesis of whether the project brought a significant change in the input use, marketed surplus, efficiency and income of farm households, we use a cross-section data from 200 smallholder farmers in Northwest Ethiopia, collected through multi-stage sampling procedure. To control for self-selection from observable characteristics of the farm households, we employ Propensity Score Matching (PSM). We finally use Data Envelopment Analysis (DEA) techniques to estimate technical efficiency of farm households. Results: The outcome of the research is in line with the premises that the participation of the household in the IPMS project improves purchased input use, marketed surplus, efficiency of farms and the overall gain from farming. The participant households on average employ more purchased agricultural inputs and gain higher gross margin from the production activities as compared to the non-participant households. The non-participant households on average supply less output (measured both in monetary terms and proportion of total produce) to the market as compared to their participant counterparts. Except for the technical efficiency of production in potato, project participant households are better-off in production efficiency compared with the non-participant counterparts. Conclusion: We verified the idea that Improving Productivity and Market Success (IPMS) of Ethiopian farmers’ project has contributed for the input and out market integration and/or market oriented agricultural production. Overall, we argue that these can be seen as an experimental model with a promising potential to improve the livelihood of the poor. Furthermore, we suggest that it is worthwhile to employ integrated agricultural extension programs with further targeting in the developing world. [ABSTRACT FROM AUTHOR]

This paper highlights the importance of optimized shape index for agricultural management system analysis that utilizes the contiguous bands of hyperspectral data to define the gradient of the spectral curve and improve image classification accuracy. Currently, a number of machine learning methods would resort to using averaged spectral information over wide bandwidths resulting in loss of crucial information available in those contiguous bands. The loss of information could mean a drop in the discriminative power when it comes to land cover classes with comparable spectral responses, as in the case of cultivated fields versus fallow lands. In this study, we proposed and tested three new optimized novel algorithms based on Moment Distance Index (MDI) that characterizes the whole shape of the spectral curve. The image classification tests conducted on two publicly available hyperspectral data sets (AVIRIS 1992 Indian Pine and HYDICE Washington DC Mall images) showed the robustness of the optimized algorithms in terms of classification accuracy. We achieved an overall accuracy of 98% and 99% for AVIRIS and HYDICE, respectively. The optimized indices were also time efficient as it avoided the process of band dimension reduction, such as those implemented by several well-known classifiers. Our results showed the potential of optimized shape indices, specifically the Moment Distance Ratio Right/Left (MDRRL), to discriminate between types of tillage (corn-min and corn-notill) and between grass/pasture and grass/trees, tree and grass under object-based random forest approach. [ABSTRACT FROM AUTHOR]

Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery. [ABSTRACT FROM AUTHOR]

Crop cultivar identification is fundamental for agricultural research, industry and policies. This paper investigates the feasibility of using visible/near infrared hyperspectral data collected with a miniaturized NIR spectrometer to identify cultivars of barley, chickpea and sorghum in the context of Ethiopia. A total of 2650 grains of barley, chickpea and sorghum cultivars were scanned using the SCIO, a recently released miniaturized NIR spectrometer. The effects of data preprocessing techniques and choosing a machine learning algorithm on distinguishing cultivars are further evaluated. Predictive multiclass models of 24 barley cultivars, 19 chickpea cultivars and 10 sorghum cultivars delivered an accuracy of 89%, 96% and 87% on hold-out sample. The Support Vector Machine (SVM) and Partial least squares discriminant analysis (PLS-DA) algorithms consistently outperformed other algorithms. Several cultivars, believed to be widely adopted in Ethiopia, were identified with perfect accuracy. These results advance the discussion on cultivar identification survey methods by demonstrating that miniaturized NIR spectrometers represent a low-cost, rapid and viable tool. We further discuss the potential utility of the method for adoption surveys, field-scale agronomic studies, socio-economic impact assessments and value chain quality control. Finally, we provide a free tool for R to easily carry out crop cultivar identification and measure uncertainty based on spectral data. [ABSTRACT FROM AUTHOR]

The emergence of high-throughput, high-density genotyping methods combined with increasingly powerful computing systems has created opportunities to further discover and exploit the genes controlling agronomic performance in elite maize breeding populations. Understanding the genetic basis of population structure in an elite set of materials is an essential step in this genetic discovery process. This paper presents a genotype-based population analysis of all maize inbreds whose Plant Variety Protection certificates had expired as of the end of 2013 (283 inbreds) as well as 66 public founder inbreds. The results provide accurate population structure information and allow for important inferences in context of the historical development of North American elite commercial maize germplasm. Genotypic data was obtained via genotyping-by-sequencing on 349 inbreds. After filtering for missing data, 77,314 high-quality markers remained. The remaining missing data (average per individual was 6.22 percent) was fully imputed at an accuracy of 83 percent. Calculation of linkage disequilibrium revealed that the average r2 of 0.20 occurs at approximately 1.1 Kb. Results of population genetics analyses agree with previously published studies that divide North American maize germplasm into three heterotic groups: Stiff Stalk, Non-Stiff Stalk, and Iodent. Principal component analysis shows that population differentiation is indeed very complex and present at many levels, yet confirms that division into three main sub-groups is optimal for population description. Clustering based on Nei’s genetic distance provides an additional empirical representation of the three main heterotic groups. Overall fixation index (FST), indicating the degree of genetic divergence between the three main heterotic groups, was 0.1361. Understanding the genetic relationships and population differentiation of elite germplasm may help breeders to maintain and potentially increase the rate of genetic gain, resulting in higher overall agronomic performance. [ABSTRACT FROM AUTHOR]

Using the spectrum data for quality prediction always suffers from noise and colinearity, so variable selection method plays an important role to deal with spectrum data. An efficient elastic net with regression coefficients method (Enet-BETA) is proposed to select the significant variables of the spectrum data in this paper. The proposed Enet-BETA method can not only select important variables to make the quality easy to interpret, but also can improve the stability and feasibility of the built model. Enet-BETA method is not prone to overfitting because of the reduction of redundant variables realized by elastic net method. Hypothesis testing is used to further simplify the model and provide a better insight into the nature of process. The experimental results prove that the proposed Enet-BETA method outperforms the other methods in terms of prediction performance and model interpretation. [ABSTRACT FROM AUTHOR]

The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets. [ABSTRACT FROM AUTHOR]

Development of easily defoliating sugarcane varieties is urgently needed to facilitate efficient mechanical harvesting, reduce production costs, and increase sugar yield in China. In order to quantify the defoliation characteristics of sugarcane, we investigated eight traits in two field experiments with a range of sugarcane varieties at maturity. The length (LSR) and angle (ASR) of the sheath ruptured from the stalk, defoliation force (DF), and self-defoliation (SD) were the traits with the greatest contribution to the quantitative assessment of sugarcane defoliation based on a principal component analysis, which accounted for more than 75% of the cumulative variability. A small set of traits, namely SD, ASR, and DF measured at the 10th leaf were selected as predictors. Using these predictors, 37 out of 38 sugarcane cultivars were correctly classified into three groups (easy, difficult and intermediate in terms of ease of defoliation) that had been clustered based on six traits. These simplified measurements will be applied to screen new lines in the sugarcane breeding program in China. [ABSTRACT FROM AUTHOR]

Molecular studies of primary and secondary dormancy in Avena fatua L., a serious weed of cereal and other crops, are intended to reveal the species-specific details of underlying molecular mechanisms which in turn may be useable in weed management. Among others, quantitative real-time PCR (RT-qPCR) data of comparative gene expression analysis may give some insight into the involvement of particular wild oat genes in dormancy release, maintenance or induction by unfavorable conditions. To assure obtaining biologically significant results using this method, the expression stability of selected candidate reference genes in different data subsets was evaluated using four statistical algorithms i.e. geNorm, NormFinder, Best Keeper and ΔCt method. Although some discrepancies in their ranking outputs were noticed, evidently two ubiquitin-conjugating enzyme homologs, AfUBC1 and AfUBC2, as well as one homolog of glyceraldehyde 3-phosphate dehydrogenase AfGAPDH1 and TATA-binding protein AfTBP2 appeared as more stably expressed than AfEF1a (translation elongation factor 1α), AfGAPDH2 or the least stable α-tubulin homolog AfTUA1 in caryopses and seedlings of A. fatua. Gene expression analysis of a dormancy-related wild oat transcription factor VIVIPAROUS1 (AfVP1) allowed for a validation of candidate reference genes performance. Based on the obtained results it can be recommended that the normalization factor calculated as a geometric mean of Cq values of AfUBC1, AfUBC2 and AfGAPDH1 would be optimal for RT-qPCR results normalization in the experiments comprising A. fatua caryopses of different dormancy status. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This study developed a technique system for the measurement, reconstruction, and trait extraction of rice canopy architectures, which have challenged functional–structural plant modeling for decades and have become the foundation of the design of ideo-plant architectures. The system uses the location-separation-measurement method (LSMM) for the collection of data on the canopy architecture and the analytic geometry method for the reconstruction and visualization of the three-dimensional (3D) digital architecture of the rice plant. It also uses the virtual clipping method for extracting the key traits of the canopy architecture such as the leaf area, inclination, and azimuth distribution in spatial coordinates. To establish the technique system, we developed (i) simple tools to measure the spatial position of the stem axis and azimuth of the leaf midrib and to capture images of tillers and leaves; (ii) computer software programs for extracting data on stem diameter, leaf nodes, and leaf midrib curves from the tiller images and data on leaf length, width, and shape from the leaf images; (iii) a database of digital architectures that stores the measured data and facilitates the reconstruction of the 3D visual architecture and the extraction of architectural traits; and (iv) computation algorithms for virtual clipping to stratify the rice canopy, to extend the stratified surface from the horizontal plane to a general curved surface (including a cylindrical surface), and to implement in silico. Each component of the technique system was quantitatively validated and visually compared to images, and the sensitivity of the virtual clipping algorithms was analyzed. This technique is inexpensive and accurate and provides high throughput for the measurement, reconstruction, and trait extraction of rice canopy architectures. The technique provides a more practical method of data collection to serve functional–structural plant models of rice and for the optimization of rice canopy types. Moreover, the technique can be easily adapted for other cereal crops such as wheat, which has numerous stems and leaves sheltering each other. [ABSTRACT FROM AUTHOR]

Most traits of agronomic importance are quantitative in nature, and genetic markers have been used for decades to dissect such traits. Recently, genomic selection has earned attention as next generation sequencing technologies became feasible for major and minor crops. Mixed models have become a key tool for fitting genomic selection models, but most current genomic selection software can only include a single variance component other than the error, making hybrid prediction using additive, dominance and epistatic effects unfeasible for species displaying heterotic effects. Moreover, Likelihood-based software for fitting mixed models with multiple random effects that allows the user to specify the variance-covariance structure of random effects has not been fully exploited. A new open-source R package called sommer is presented to facilitate the use of mixed models for genomic selection and hybrid prediction purposes using more than one variance component and allowing specification of covariance structures. The use of sommer for genomic prediction is demonstrated through several examples using maize and wheat genotypic and phenotypic data. At its core, the program contains three algorithms for estimating variance components: Average information (AI), Expectation-Maximization (EM) and Efficient Mixed Model Association (EMMA). Kernels for calculating the additive, dominance and epistatic relationship matrices are included, along with other useful functions for genomic analysis. Results from sommer were comparable to other software, but the analysis was faster than Bayesian counterparts in the magnitude of hours to days. In addition, ability to deal with missing data, combined with greater flexibility and speed than other REML-based software was achieved by putting together some of the most efficient algorithms to fit models in a gentle environment such as R. [ABSTRACT FROM AUTHOR]