Expression of the CCCH-tandem zinc finger protein gene OsTZF5 under a stress-inducible promoter mitigates the effect of drought stress on rice grain yield under field conditions.

Increasing drought resistance without sacrificing grain yield remains an ongoing challenge in crop improvement. In this study, we report that Oryza sativa CCCH-tandem zinc finger protein 5 (OsTZF5) can confer drought resistance and increase grain yield in transgenic rice plants. Expression of OsTZF5 was induced by abscisic acid, dehydration and cold stress. Upon stress, OsTZF5-GFP localized to the cytoplasm and cytoplasmic foci. Transgenic rice plants overexpressing OsTZF5 under the constitutive maize ubiquitin promoter exhibited improved survival under drought but also growth retardation. By introducing OsTZF5 behind the stress-responsive OsNAC6 promoter in two commercial upland cultivars, Curinga and NERICA4, we obtained transgenic plants that showed no growth retardation. Moreover, these plants exhibited significantly increased grain yield compared to non-transgenic cultivars in different confined field drought environments. Physiological analysis indicated that OsTZF5 promoted both drought tolerance and drought avoidance. Collectively, our results provide strong evidence that OsTZF5 is a useful biotechnological tool to minimize yield losses in rice grown under drought conditions.

Development and field performance of nitrogen use efficient rice lines for Africa.

Nitrogen (N) fertilizers are a major input cost in rice production, and its excess application leads to major environmental pollution. Development of rice varieties with improved nitrogen use efficiency (NUE) is essential for sustainable agriculture. Here, we report the results of field evaluations of marker-free transgenic NERICA4 (New Rice for Africa 4) rice lines overexpressing barley alanine amino transferase (HvAlaAT) under the control of a rice stress-inducible promoter (pOsAnt1). Field evaluations over three growing seasons and two rice growing ecologies (lowland and upland) revealed that grain yield of pOsAnt1:HvAlaAT transgenic events was significantly higher than sibling nulls and wild-type controls under different N application rates. Our field results clearly demonstrated that this genetic modification can significantly increase the dry biomass and grain yield compared to controls under limited N supply. Increased yield in transgenic events was correlated with increased tiller and panicle number in the field, and evidence of early establishment of a vigorous root system in hydroponic growth. Our results suggest that expression of the HvAlaAT gene can improve NUE in rice without causing undesirable growth phenotypes. The NUE technology described in this article has the potential to significantly reduce the need for N fertilizer and simultaneously improve food security, augment farm economics and mitigate greenhouse gas emissions from the rice ecosystem.

Overexpression of an Arabidopsis thaliana galactinol synthase gene improves drought tolerance in transgenic rice and increased grain yield in the field.

Drought stress has often caused significant decreases in crop production which could be associated with global warming. Enhancing drought tolerance without a grain yield penalty has been a great challenge in crop improvement. Here, we report the Arabidopsis thaliana galactinol synthase 2 gene (AtGolS2) was able to confer drought tolerance and increase grain yield in two different rice (Oryza sativa) genotypes under dry field conditions. The developed transgenic lines expressing AtGolS2 under the control of the constitutive maize ubiquitin promoter (Ubi:AtGolS2) also had higher levels of galactinol than the non-transgenic control. The increased grain yield of the transgenic rice under drought conditions was related to a higher number of panicles, grain fertility and biomass. Extensive confined field trials using Ubi:AtGolS2 transgenic lines in Curinga, tropical japonica and NERICA4, interspecific hybrid across two different seasons and environments revealed the verified lines have the proven field drought tolerance of the Ubi:AtGolS2 transgenic rice. The amended drought tolerance was associated with higher relative water content of leaves, higher photosynthesis activity, lesser reduction in plant growth and faster recovering ability. Collectively, our results provide strong evidence that AtGolS2 is a useful biotechnological tool to reduce grain yield losses in rice beyond genetic differences under field drought stress.

Comparative Analysis of Detectors and Feature Descriptors for Multispectral Image Matching in Rice Crops.

Precision agriculture has greatly benefited from advances in machine vision and image processing techniques. The use of feature descriptors and detectors allows to find distinctive keypoints in an image and the use of this approach for agronomical applications has become a widespread field of study. By combining near infrared (NIR) images, acquired with a modified Nikon D80 camera, and visible spectrum (VIS) images, acquired with a Nikon D300s, a proper crop identification could be obtained. Still, the use of different sensors brings an image matching challenge due to the difference between cameras and the possible distortions from each imaging technique. The aim of this paper is to compare the performance of several feature descriptors and detectors by comparing near infrared and visual spectral bands in rice crop images. Therefore, a group of 20 different scenes with different cameras and growth stages in a rice crop were evaluated. Thus, red, green, blue (RGB) and L, a, b (CIE L*a*b*) channels were extracted from VIS images in order to compare the matches obtained between each of them and the corresponding NIR image. The BRISK, SURF, SIFT, ORB, KAZE, and AKAZE methods were implemented, which act as descriptors and detectors. Additionally, a combination was made between the FAST algorithm for the detection of keypoints with the BRIEF, BRISK, and FREAK methods for features description. BF and FLANN matching methods were used. The algorithms were implemented in Python using OpenCV library. The green channel presented the highest number of correct matches in all methods. In turn, the method that presented the highest performance both in time and in the number of correct matches was the combination of the FAST feature detector and the BRISK descriptor.

Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz).

BACKGROUND: Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data. Many studies have demonstrated the usefulness of advanced remote sensing technologies coupled with machine learning (ML) approaches for accurate prediction of valuable crop traits. Until now, Cassava has received little to no attention in aerial image-based phenotyping and ML model testing. RESULTS: To accelerate image processing, an automated image-analysis framework called CIAT Pheno-i was developed to extract plot level vegetation indices/canopy metrics. Multiple linear regression models were constructed at different key growth stages of cassava, using ground-truth data and vegetation indices obtained from a multispectral sensor. Henceforth, the spectral indices/features were combined to develop models and predict cassava root yield using different Machine learning techniques. Our results showed that (1) Developed CIAT pheno-i image analysis framework was found to be easier and more rapid than manual methods. (2) The correlation analysis of four phenological stages of cassava revealed that elongation (EL) and late bulking (LBK) were the most useful stages to estimate above-ground biomass (AGB), below-ground biomass (BGB) and canopy height (CH). (3) The multi-temporal analysis revealed that cumulative image feature information of EL + early bulky (EBK) stages showed a higher significant correlation (r = 0.77) for Green Normalized Difference Vegetation indices (GNDVI) with BGB than individual time points. Canopy height measured on the ground correlated well with UAV (CHuav)-based measurements (r = 0.92) at late bulking (LBK) stage. Among different image features, normalized difference red edge index (NDRE) data were found to be consistently highly correlated (r = 0.65 to 0.84) with AGB at LBK stage. (4) Among the four ML algorithms used in this study, k-Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machine (SVM) showed the best performance for root yield prediction with the highest accuracy of R2 = 0.67, 0.66 and 0.64, respectively. CONCLUSION: UAV platforms, time series image acquisition, automated image analytical framework (CIAT Pheno-i), and key vegetation indices (VIs) to estimate phenotyping traits and root yield described in this work have great potential for use as a selection tool in the modern cassava breeding programs around the world to accelerate germplasm and varietal selection. The image analysis software (CIAT Pheno-i) developed from this study can be widely applicable to any other crop to extract phenotypic information rapidly.