Browsing by Author "Hoisington, David"

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    Comparing Regression and Classification Models to Estimate Leaf Spot Disease in Peanut (Arachis hypogaea L.) for Implementation in Breeding Selection
    Chapu, Ivan; Chandel, Abhilash; Sie, Emmanuel Kofi; Okello, David Kalule; Oteng-Frimpong, Richard; Okello, Robert Cyrus Ongom; Hoisington, David; Balota, Maria (MDPI, 2024-04-30)
    Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance. The objectives of this study were to compare between regression and classification for breeding, and to identify the best models and indices to be used for selection. We evaluated 223 genotypes in three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic data were collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera and GreenSeeker. RGB indices derived from the images, along with the normalized difference vegetation index (NDVI), were used to model LLS resistance using statistical and machine learning methods. Both regression and classification methods were also evaluated for selection. Random Forest (RF), the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algorithms for both regression and classification. The ANN (R2: 0.81, RMSE: 22%) was the best regression algorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass (78% and 73% accuracy) classification. The classification accuracy of the models decreased with the increase in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index were strongly associated with LLS and useful for selection. Our study demonstrates that the integration of remote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plant breeders in managing large populations effectively.
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    Evaluation of Production and Pest Management Practices in Peanut (Arachis hypogaea) in Ghana
    Seidu, Ahmed; Abudulai, Mumuni; Dzomeku, Israel K.; Mahama, Georgie Y.; Nboyine, Jerry A.; Appaw, William; Akromah, Richard; Arthur, Stephen; Bolfrey-Arku, Grace; Mochiah, M. Brandford; Jordan, David L.; Brandenburg, Rick L.; MacDonald, Greg; Balota, Maria; Hoisington, David; Rhoads, Jamie (MDPI, 2024-05-06)
    The economic return for peanut (Arachis hypogaea L.) in Ghana is often low due to limitations in the availability of inputs or their adoption, which are needed to optimize yield. Six experiments were conducted in Ghana in 2020 and 2021 to determine the impact of planting date, cultivar, fertilization, pest management practices, and harvest date on peanut yield, financial return, and pest reaction. A wide range of interactions among these treatment factors were often observed for infestations of aphids (Aphis gossypii Glover); groundnut rosette disease (Umbravirus: Tombusviridaee); millipedes (Peridontopyge spp.); white grubs (Schyzonicha spp.); wireworms (Conoderus spp.); termites (Microtermes and Odontotermes spp.); canopy defoliation as a result of early leaf spot disease caused by Passalora arachidicola (Hori) and late leaf spot caused by Nothopassalora personata (Berk. and M. A. Curtis); and the scarification and boring of pods caused by arthropod feeding. Pod yield and economic return increased for the cultivar Chitaochi and Sarinut 2 when fertilizer was applied and when fertilizer was applied at early, mid-, and late planting dates. Pod yield and economic return increased when a combination of locally derived potassium soaps was used for aphid suppression and one additional hand weeding was used in the improved pest management practice compared with the traditional practice without these inputs. Pearson correlations for yield and economic return were negatively correlated for all pests and damage caused by pests. The results from these experiments can be used by farmers and their advisors to develop production packages for peanut production in Ghana.
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    High-Throughput Plant Phenotyping (HTPP) in Resource-Constrained Research Programs: A Working Example in Ghana
    Kassim, Yussif Baba; Oteng-Frimpong, Richard; Puozaa, Doris Kanvenaa; Sie, Emmanuel Kofi; Abdul Rasheed, Masawudu; Abdul Rashid, Issah; Danquah, Agyemang; Akogo, Darlington A.; Rhoads, James; Hoisington, David; Burow, Mark D.; Balota, Maria (MDPI, 2022-11-04)
    In this paper, we present a procedure for implementing field-based high-throughput plant phenotyping (HTPP) that can be used in resource-constrained research programs. The procedure relies on opensource tools with the only expensive item being one-off purchase of a drone. It includes acquiring images of the field of interest, stitching the images to get the entire field in one image, calculating and extracting the vegetation indices of the individual plots, and analyzing the extracted indices according to the experimental design. Two populations of groundnut genotypes with different maturities were evaluated for their reaction to early and late leaf spot (ELS, LLS) diseases under field conditions in 2020 and 2021. Each population was made up of 12 genotypes in 2020 and 18 genotypes in 2021. Evaluation of the genotypes was done in four locations in each year. We observed a strong correlation between the vegetation indices and the area under the disease progress curve (AUDPC) for ELS and LLS. However, the strength and direction of the correlation depended upon the time of disease onset, level of tolerance among the genotypes and the physiological traits the vegetation indices were associated with. In 2020, when the disease was observed to have set in late in medium duration population, at the beginning of the seed stage (R5), normalized green-red difference index (NGRDI) and variable atmospheric resistance index (VARI) derived at the beginning pod stage (R3) had a positive relationship with the AUDPC for ELS, and LLS. On the other hand, NGRDI and VARI derived from images taken at R5, and physiological maturity (R7) had negative relationships with AUDPC for ELS, and LLS. In 2021, when the disease was observed to have set in early (at R3) also in medium duration population, a negative relationship was observed between NGRDI and VARI and AUDPC for ELS and LLS, respectively. We found consistently negative relationships of NGRDI and VARI with AUDPC for ELS and LLS, respectively, within the short duration population in both years. Canopy cover (CaC), green area (GA), and greener area (GGA) only showed negative relationships with AUDPC for ELS and LLS when the disease caused yellowing and defoliation. The rankings of some genotypes changed for NGRDI, VARI, CaC, GA, GGA, and crop senescence index (CSI) when lesions caused by the infections of ELS and LLS became severe, although that did not affect groupings of genotypes when analyzed with principal component analysis. Notwithstanding, genotypes that consistently performed well across various reproductive stages with respect to the vegetation indices constituted the top performers when ELS, LLS, haulm, and pod yields were jointly considered.
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    RGB-image method enables indirect selection for leaf spot resistance and yield estimation in a groundnut breeding program in Western Africa
    Sie, Emmanuel Kofi; Oteng-Frimpong, Richard; Kassim, Yussif Baba; Puozaa, Doris Kanvenaa; Adjebeng-Danquah, Joseph; Masawudu, Abdul Rasheed; Ofori, Kwadwo; Danquah, Agyemang; Cazenave, Alexandre Brice; Hoisington, David; Rhoads, James; Balota, Maria (Frontiers, 2022-08-04)
    Early Leaf Spot (ELS) caused by the fungus Passalora arachidicola and Late Leaf Spot (LLS) also caused by the fungus Nothopassalora personata, are the two major groundnut (Arachis hypogaea L.) destructive diseases in Ghana. Accurate phenotyping and genotyping to develop groundnut genotypes resistant to Leaf Spot Diseases (LSD) and to increase groundnut production is critically important in Western Africa. Two experiments were conducted at the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute located in Nyankpala, Ghana to explore the effectiveness of using RGB-image method as a high-throughput phenotyping tool to assess groundnut LSD and to estimate yield components. Replicated plots arranged in a rectangular alpha lattice design were conducted during the 2020 growing season using a set of 60 genotypes as the training population and 192 genotypes for validation. Indirect selection models were developed using Red-Green-Blue (RGB) color space indices. Data was collected on conventional LSD ratings, RGB imaging, pod weight per plant and number of pods per plant. Data was analyzed using a mixed linear model with R statistical software version 4.0.2. The results showed differences among the genotypes for the traits evaluated. The RGB-image method traits exhibited comparable or better broad sense heritability to the conventionally measured traits. Significant correlation existed between the RGB-image method traits and the conventionally measured traits. Genotypes 73-33, Gha-GAF 1723, Zam-ICGV-SM 07599, and Oug-ICGV 90099 were among the most resistant genotypes to ELS and LLS, and they represent suitable sources of resistance to LSD for the groundnut breeding programs in Western Africa.