Grassland pastures are crucial for the global food supply through their milk and meat production; hence, forage species monitoring is essential for cattle feed. Therefore, knowledge of pasture above-ground canopy features help understand the crop status. This paper finds how to construct machine learning models to predict above-ground canopy features in Brachiaria pasture from ground truth data (GTD) and remote sensing at larger (satellite data on the cloud) and smaller (unmanned aerial vehicles (UAV)) scales. First, we used above-ground biomass (AGB) data obtained from Brachiaria to evaluate the relationship between vegetation indices (VIs) with the dry matter (DM). Next, the performance of machine learning algorithms was used for predicting AGB based on VIs obtained from ground truth and satellite and UAV imagery. When comparing more than twenty-five machine learning models using an Auto Machine Learning Python API, the results show that the best algorithms were the Huber with R2 = 0.60, Linear with R2 = 0.54, and Extra Trees with R2 = 0.45 to large scales using satellite. On the other hand, short-scale best regressions are K Neighbors with an R2 of 0.76, Extra Trees with an R2 of 0.75, and Bayesian Ridge with an R2 of 0.70, demonstrating a high potential to predict AGB and DM. This study is the first prediction model approach that assesses the rotational grazing system and pasture above-ground canopy features to predict the quality and quantity of cattle feed to support pasture management in Colombia.

In recent years, applications of volatile organic compounds (VOCs) sensing technologies such as field asymmetric-waveform ion-mobility spectrometry (FAIMS) system in agriculture have accelerated. FAIMS system for VOCs sensing is attractive as it offers high sensitivity, selectivity, real-time monitoring, and portability. However, the development of a robust instrumentation system is needed for precise sampling, high accumulation of VOCs, and careful handling of samples. In this study, we developed a simple semi-automated VOC sampling (SAVS) system using a Raspberry Pi microcontroller, flowmeters, electromechanical solenoid, and cellphone-based app to control cleaning and sampling loops. The system was compared with customized headspace sampling apparatus (CHSA) and validated with a biomarker (acetone) identified to be associated with potato rot development during postharvest storage. The standard error within ion current data across different compensation voltage was lower using the SAVS system than the CHSA. In addition, the maximum peak values across scans displayed a high coefficient of variation using the CHSA (16.23%) than the SAVS system (4.51%). Future work will involve improving system efficiency by adapting multiple sample units, system miniaturization, and automating the flowmeter operation. Such automation is critical to characterize VOCs precisely and automatically across several samples for multiple applications such as pathogen detection, evaluation of crop responses, etc.

Pest attacks on plants can substantially change plants' volatile organic compounds (VOCs) emission profiles. Comparison of VOC emission profiles between non-infected/non-infested and infected/infested plants, as well as resistant and susceptible plant cultivars, may provide cues for a deeper understanding of plant-pest interactions and associated resistance. Furthermore, the identification of biomarkers-specific biogenic VOCs-associated with the resistance can serve as a non-destructive and rapid tool for phenotyping applications. This research aims to compare the VOCs emission profiles under diverse conditions to identify constitutive (also referred to as green VOCs) and induced (resulting from biotic/abiotic stress) VOCs released in potatoes and wheat. In the first study, wild potato Solanum bulbocastanum (accession# 22; SB22) was inoculated with Meloidogyne chitwoodi race 1 (Mc1), and Mc1 pathotype Roza (SB22 is resistant to Mc1 and susceptible to pathotype Roza), and VOCs emission profiles were collected using gas chromatography-flame ionization detection (GC-FID) at different time points. Similarly, in the second study, the VOCs emission profiles of resistant ('Hollis') and susceptible ('Alturas') wheat cultivars infested with Hessian fly insects were evaluated using the GC-FID system. In both studies, in addition to variable plant responses (susceptibility to pests), control treatments (non-inoculated or non-infested) were used to compare the VOCs emission profiles resulting from differences in stress conditions. The common VOC peaks (constitutive VOCs) between control and infected/infested samples, and unique VOC peaks (induced VOCs) presented only in infected/infested samples were analyzed. In the potato-nematode study, the highest unique peak was found two days after inoculation (DAI) for SB22 inoculated with Mc1 (resistance response). The most common VOC peaks in SB22 inoculated with both Mc1 and Roza were found at 5 and 10 DAI. In the wheat-insect study, only the Hollis showed unique VOC peaks. Interestingly, both cultivars released the same common VOCs between control and infected samples, with only a difference in VOC average peak intensity at 22.4 min retention time where the average intensity was 4.3 times higher in the infested samples of Hollis than infested samples of Alturas. These studies demonstrate the potential of plant VOCs to serve as a rapid phenotyping tool to assess resistance levels in different 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.

Normalization of anisotropic solar reflectance is an essential factor that needs to be considered for field-based phenotyping applications to ensure reliability, consistency, and interpretability of time-series multispectral data acquired using an unmanned aerial vehicle (UAV). Different models have been developed to characterize the bidirectional reflectance distribution function. However, the substantial variation in crop breeding trials, in terms of vegetation structure configuration, creates challenges to such modeling approaches. This study evaluated the variation in standard vegetation indices and its relationship with ground-reference data (measured crop traits such as seed/grain yield) in multiple crop breeding trials as a function of solar zenith angles (SZA). UAV-based multispectral images were acquired and utilized to extract vegetation indices at SZA across two different latitudes. The pea and chickpea breeding materials were evaluated in a high latitude (46°36′39.92″ N) zone, whereas the rice lines were assessed in a low latitude (3°29′42.43″ N) zone. In general, several of the vegetation index data were affected by SZA (e.g., normalized difference vegetation index, green normalized difference vegetation index, normalized difference red-edge index, etc.) in both latitudes. Nevertheless, the simple ratio index (SR) showed less variability across SZA in both latitude zones amongst these indices. In addition, it was interesting to note that the correlation between vegetation indices and ground-reference data remained stable across SZA in both latitude zones. In summary, SR was found to have a minimum anisotropic reflectance effect in both zones, and the other vegetation indices can be utilized to evaluate relative differences in crop performances, although the absolute data would be affected by SZA.

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.

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 R 2 = 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.

Remote sensing technology for monitoring plant trains has a huge potential to accelerate breeding process. In this paper, we have studied on remote sensing of using an unmanned aerial vehicle (UAV) system for plant traits phenotyping in rice. The images of rice canopy were taken by a RGB camera from the UAV at three growing stages; Vegetative (VG), Flowering (FW) and Grain filling (GF). Typical color indices (r, g, b, INT, VIG, L*, a*, b*, H) were calculated by image processing. Single regression analysis was conducted between rice plant traits (leaf area index (LAI), grain yield, above ground biomass, plant height, panicle length, grain filling rate, tiller number) and color indices. The index a* at FW and GF had close liner relationships with LAI (the coefficient of determination R-2 > 0.70) and grain yield (R-2 > 0.50). Moreover, a* and g at FW and GF showed high R-2 with plant height and grain filling rate (R-2 > 0.50). The R-2 between grain yield and color indices increased above 0.5 for about 40 of models at three growing stages by multiple regression analysis. In particular, the models of H and INT and of H and L* at VG were closely related (R-2 > 0.70). Our findings show the analysis of color images taken by UAV remote sensing is useful to assessing four rice traits; LAI, grain yield, plant height and grain filling rate at early stage, and especially more available for grain yield estimation.

Application of field based high-throughput phenotyping (FB-HTP) methods for monitoring plant performance in real field conditions has a high potential to accelerate the breeding process. In this paper, we discuss the use of a simple tower based remote sensing platform using modified single-lens reflex cameras for phenotyping yield traits in rice under different nitrogen (N) treatments over three years. This tower based phenotyping platform has the advantages of simplicity, ease and stability in terms of introduction, maintenance and continual operation under field conditions. Out of six phenological stages of rice analyzed, the flowering stage was the most useful in the estimation of yield performance under field conditions. We found a high correlation between several vegetation indices (simple ratio (SR), normalized difference vegetation index (NDVI), transformed vegetation index (TVI), corrected transformed vegetation index (CTVI), soil-adjusted vegetation index (SAVI) and modified soil-adjusted vegetation index (MSAVI)) and multiple yield traits (panicle number, grain weight and shoot biomass) across a three trials. Among all of the indices studied, SR exhibited the best performance in regards to the estimation of grain weight (R2=0.80). Under our tower-based field phenotyping system (TBFPS), we identified quantitative trait loci (QTL) for yield related traits using a mapping population of chromosome segment substitution lines (CSSLs) and a single nucleotide polymorphism data set. Our findings suggest the TBFPS can be useful for the estimation of yield performance during early crop development. This can be a major opportunity for rice breeders whom desire high throughput phenotypic selection for yield performance traits.

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.

SummaryNitrogen (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.

Polyphagotarsonemus latus is one a major pest of Valencia orange in Colombia. To study its biology, it was established an experiment on young leaves of the Valencia orange at 25 ± 5°C, 70 ± 5% RH, and 12:12 L:D photoperiod. To characterize the nature of damage caused by this mite on leaves and young fruits, a trial in screenhouse conditions using young leaf and small fruits (0.1 - 0.3 cm in diameter) and fruits between 1.0–3.0 cm in diameter, which were infested with 5, 15 and 30 adult females of P. latus, was established. The total duration of life cycle was 3.2 days; the female had an oviposition period of 7.4 days and longevity 9.6 days. Life table parameters were as follows: net reproductive rate (Ro) = 93.3, intrinsic rate of natural increase (rm) = 1.2, finite multiplication rate (λ) = 3.7, doubling time (TD) = 0.6 days and generation time (T) = 3.4. The first signs of damage and shoot drop in newly developed shoots occurred between 7.0 and 11.8 days after infestation. Small fruit infested with 5-30 mites showed damage 2.4 days after infestation. Shoot drop started 7.0 days after infestation. In the case of fruits between 1.0 and 3.0 cm in diameter, the damage and the collapse of the structure was between 3.0 and 13.5 days, respectively. These results explain how a mite with a very short life cycle and huge biotic potential causes such severe damage in shoots and newly formed fruits of Valencia orange.

Improved root system architecture can enhance agronomic performance by increasing water and nitrogen (N) acquisition efficiency. However, little is known about interaction between root system architecture and agronomic performance under field environments. To gain a better understanding about the genetic basis of these relationships, we evaluated a set of chromosome segment substitution lines (CSSLs) derived from crosses between a tropical japonica rice cultivar ‘Curinga’ and a wild species Oryza rufipogon accession IRGC105491. Root system architectural traits were investigated using the CSSLs at 40 days old seedlings using the root basket method under hydroponic conditions, and agronomic performances were also tested under field conditions with different N treatments. Agronomic performances were computed as the ratio of a trait value under low to high N treatments, including grain yield and biomass yield as nitrogen-deficiency tolerance (NDT) traits. Root architecture and NDT trait QTLs were mapped using 238 SNP marker loci. A total of 13 QTLs for root system architectural, NDT and morpho-physiological traits were identified on chromosomes 1, 3, 4, 5, 7, 8, 9, 10 and 12. Interestingly, a QTL for deeper root number was identified the region of SNP markers between id1012330 and id1021697 on chromosome 1 under hydroponic conditions overlapped with a QTL for NDT trait of relative grain yield (qRGY1). These results suggest that deeper root trait is helpful to maintain grain yield under nitrogen-deficient conditions. The QTL associated root architecture could potentially be used in future rice-breeding efforts to increase agronomic performance under nitrogen-deficient conditions.

The main goal of any field phenomics platform is to enable plant breeders to detect and evaluate dynamic traits which so far are difficult or inefficient to measure while also requiring destructive sampling. Plant phenotyping is frequently slower and more expensive than genotyping due to the time interval data capture for traits under different environments. Advanced remote sensing technologies such as aerial phenotyping may help expedite the development and selection of climate smart plant varieties. This is an ongoing research project. The main objective of this research is to build an open source web based image analysis platform using opencv (open source computer vision) to analyze field data in the form of images. Computation of vegetative indices to predict plant performance under real field conditions would be a provided outcome.

Root plasticity, a trait that can respond to different soil environments, may assist plants to scavenge the nutrients in heterogeneous soils. The objective of this paper is to understand the NH4 +-responsive root architectural changes in rice for better acquisition of Nitrogen (N). Using a root basket method, we examined the variation in root plasticity among diverse rice genotypes grown under hydroponics with different NH4 + concentrations. Significant variation in NH4 +-responsive root architectural changes was observed among rice genotypes studied. From the hydroponic study, five contrasting genotypes with distinct rooting patterns (mono and dimorphic) were selected based on the ratio of deeper roots and rooting pattern value. These distinct genotypes were evaluated in the field to identify the role of root architecture on plant performance under different N applications. Our field results revealed that the dimorphic rooting genotypes enhance the grain yield and shoot biomass under N-deficit conditions than monomorphic root genotypes. We conclude that root architectural plasticity and dimorphic rooting pattern would be helpful to enhance the nitrogen-acquisition efficiency under N-deficit conditions in rice.

Varias especies de acaros de la familia Tenuipalpidae, especialmente las del genero Brevipalpus Donnadieu, son reconocidas como vectores de enfermedades virales en diferentes cultivos comerciales. La falta de estudios taxonomicos en esta familia de acaros en Colombia y la posibilidad de introducir en forma accidental especies de estos acaros planos, son una amenaza para la bioseguridad de la produccion fruticola del pais. El objetivo de este trabajo es contribuir a la identificacion de las especies de Tenuipalpidae en la vegetacion silvestre y cultivada en el departamento del Valle del Cauca y confirmar la identidad taxonomica de las especies de Brevipalpus en citricos, guayaba, maracuya, cafe y orquideas. Las recolecciones de ejemplares se realizaron en epocas seca y lluviosa en varios municipios del departamento. El montaje de los acaros se realizo en medio Hoyer. La especie predominante en todas las localidades y en los hospedantes recolectados fue Brevipalpus phoenicis Geijskes (1939). Por el contrario, B. obovatus Donnadieu se encontro en poblaciones muy bajas en limon Tahiti y mandarina en algunas localidades de Palmira. Tenuipalpus anacardii De Leon se presento en Anacardium excelsum (Bertero y Balb. ex Kunth) Skeels, en el municipio de Roldanillo y T. pacificus Baker se encontro en el corregimiento de Rozo, municipio de Palmira, sobre plantas importadas de un cultivo comercial de Dendrobium sp., mientras que Dolichotetranychus floridanus (Banks) solamente se encontro sobre Ananas comosus (L.) Merr., en muestras recolectadas en Cali

Several species of flat mites, family Tenuipalpidae, especially in the genus Brevipalpus Donnadieu, are recognized as vectors of viral diseases, which affect ornamentals and commercial crops. Both, the lack of taxonomical research related to this group in Colombia, and the high possibility of accidentally introducing members of this family into our country, are threads against biosecurity, mainly to the fruit tree production. The main goals of this project are to contribute towards flat mite species identification found in wild vegetation and crops in the Valle del Cauca and to confirm the taxonomic identity of Brevipalpus species found in citrus, guayaba, passion fruit, coffee, and orquids. Surveys were carried out during the dry and rainy (humid) seasons. Mites were mounted in Hoyer medium. Brevipalpus phoencis Geijskes (type) was the main species found over all the visited places and collected hosts. On the other hand, B. obovatus Donnadieu, was found in very low numbers on lemonTahiti and tangerine in some locations near the city of Palmira. Tenuipalpus anacardii De Leon was found on Anacardium excelsum, (Bertero y Balb. ex Kunth) Skeels, in the town of Roldanillo, and T. pacificus Baker was present in Rozo (Palmira's Township) in imported plants of the commercial crop of Dendrobium sp. Dolichotetranychus floridanus (Banks) was only found among Ananas comosus (L.) Merr. near the city of Cali.

Extreme weather events such as droughts and heat waves are likely to become more common in recent warmer climate. Thus, development of drought tolerant rice varieties has been an urgent research goal since rice is the staple food of many people on planet. To speed up development of drought tolerant rice varieties, unique research collaborations between advanced institutions and international research organization were developed and led by Japan International Research Center for Agricultural Sciences (JIRCAS). This collaboration brought diverse expertise together to facilitate a product development pipeline from gene to trait evaluation under target field environments. One of genes tested in this study was OsNAC6 gene. Twentyeight independent transgenic lines that possessed homozygous single copy of introduced OsNAC6 gene were generated from a commercial tropical japonica variety, Curinga. Evaluation of the transgenic lines under controlled and confined rainfed conditions in Colombia revealed superior performance of some transgenic lines in grain yield compared with non-transgenic plants. The transgenic lines of other genetic backgrounds, NERICA1 and NERICA4, also showed increased grain yield under the rainfed conditions. The results 36 Towards development of drought tolerant upland rice through international research collaborations from gene discovery to trait evaluation: in case of OsNAC6 gene as example strongly suggest that the gene works regardless of genetic background as common genetic component for drought tolerance in rice under field conditions leading to increased grain yield.