Abstract After deregulation of the agricultural markets in South Africa in 1997, the estimated maize crop could no longer be verified against the actual crop, due to the lack of control data from the Maize Control Board. This drove the need to explore remotely sensed data as a supplement to the current crop estimation methodology to improve crop estimations. Input data for the development of a Geographic Information System (GIS)-based model consisted of objective yield point data, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI) images and rainfall grids. Rainfall grids were interpolated from weather station data. NDVI values were obtained from the MODIS sensor aboard the Terra platform. Objective yield point field survey data for the 2001/2002 growing season were utilised since dry-land or irrigated conditions were recorded for that season. MODIS NDVI values corresponded well with the growing stages and age of the maize plants after being adjusted to reflect the crop’s age rather than the Julian date. Rainfall values were extracted from rainfall grids and also aligned with the age of the maize plants. This is a suggested alternative to the traditional method of using the mean NDVI for several districts in a region over a Julian growing period of 11 months according to Julian dates. South African maize production areas extend over seven (7) provinces with eight (8) different temperature and rainfall zones (du Plessis, 2004). Planting-date zones based on the uniform age of the maize plants were developed from objective yield Global Positioning System (GPS) points for the 2001/2002 growing season and compared with the 2004/2005 growing season (Frost and Kneen, 2006). Planting dates were interpolated from these planting zones for objective yield GPS points which were missing planting dates in the survey database. MODIS imagery is affordable (free) and four (4) images cover the whole of South Africa daily, while one (1) image covers the study area daily. Several recommendations, such as establishing yield equations for a normal, dry, and wet season were made. It is also suggested that dry-land and irrigated areas continue to be evaluated separately in future.

One important parameter of interest in remote sensing to date is yield variability. Proper understanding of yield variability provides insight on the geo-positional dependences of field yields and insight on zone management strategies. Estimating cotton yield and observing cotton yield variability has proven to be a challenging problem due to the complex fruiting behavior of cotton from reactions to environmental conditions. Current methods require expensive sensory equipment on large manned aircrafts and satellites. Other systems, such as cotton yield monitors, are often subject to error due to the collection of dust/trash on photo sensors. This study was aimed towards the development of a miniature unmanned aerial system that utilized a first-person view (FPV) color camera for measuring cotton yield variability. Outcomes of the study led to the development of a method for estimating cotton yield variability from images of experimental cotton plot field taken at harvest time in 2014. These plots were treated with nitrogen fertilizer at five different rates to insure variations in cotton yield across the field. The cotton yield estimates were based on the cotton unit coverage (CUC) observed as the cotton boll image signal density. The cotton boll signals were extracted via their diffusion potential in the image intensity space. This was robust to gradients in illumination caused by cloud coverage as well as fruiting positions in the field. These estimates were provided at a much higher spatial resolution (9.0 cm2) at comparable correlations (R2=0.74) with current expensive systems. This method could prove useful for the development of low cost automated systems for cotton yield estimation as well as yield estimation systems for other crops. / Master of Science

Master of Science / Department of Agronomy / William T. Schapaugh Jr / Crops research moving forward faces many challenges to improve crop performance. In breeding programs, phenotyping has time and economic constraints requiring new phenotyping techniques to be developed to improve selection efficiency and increase germplasm entering the pipeline. The objectives of these studies were to examine the changes in spectral reflectance with soybean breeding from 1923 to 2010, evaluate band regions most significantly contributing to yield estimation, evaluate spectral reflectance data for yield estimation modeling across environments and growth stages and to evaluate the usefulness of spectral data as an optimized phenotyping technique in breeding programs. Twenty maturity group III (MGIII) and twenty maturity group IV (MGIV) soybeans, arranged in a randomized complete block design, were grown in Manhattan, KS in 2011 and 2012. Spectral reflectance data were collected over the growing season in a total of six irrigated and water- stressed environments. Partial least squares and multiple linear regression were used for spectral variable selection and yield estimation model building. Significant differences were found between genotypes for yield and spectral reflectance data, with the visible (VI) having greater differences between genotypes than the near-infrared (NIR). This study found significant correlations with year of release (YOR) in the VI and NIR portions of the spectra, with newer released cultivars tending to have lower reflectance in the VI and high reflectance in the NIR. Spectral reflectance data accounted for a large portion of variability for seed yield between genotypes using the red edge and NIR portions of the spectra. Irrigated environments tended to explain a larger portion of seed yield variability than water-stressed environments. Growth stages most useful for yield estimation was highly dependent upon the environment as well as maturity group. This study found that spectral reflectance data is a good candidate for exploration into optimized phenotyping techniques and with further research and validation datasets, may be a suitable indirect selection technique for breeding programs.

Submitted by WALTER MALDONADO JÚNIOR null (walter@rainformatica.com.br) on 2016-03-29T21:27:14Z No. of bitstreams: 1 principal.pdf: 75187969 bytes, checksum: ed5b4271338552ed5f58e72f73d7073d (MD5) / Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-03-30T11:37:55Z (GMT) No. of bitstreams: 1 maldonadojunior_w_dr_jabo.pdf: 75187969 bytes, checksum: ed5b4271338552ed5f58e72f73d7073d (MD5) / Made available in DSpace on 2016-03-30T11:37:55Z (GMT). No. of bitstreams: 1 maldonadojunior_w_dr_jabo.pdf: 75187969 bytes, checksum: ed5b4271338552ed5f58e72f73d7073d (MD5) Previous issue date: 2016-02-22 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / A estimativa da produtividade é um fator importante no planejamento de um processo produtivo. No caso dos citros, pode colaborar com o gerenciamento do processo industrial e servir como orientação para os produtores, apresentando papel decisivo no mercado do produto e no manejo de tratos culturais. Vários estudos de técnicas para estimativa da produção da cultura vêm sendo realizados mas ainda apresentando limitações. Devido à correlação entre o número de frutos visíveis na imagem de uma planta e o número real de frutos na mesma já apontada em estudos anteriores, foi desenvolvido um método de amostragem automático e não-destrutivo, por meio da extração das características de frutos verdes em imagens digitais. Utilizou-se uma combinação das técnicas de conversão do modelo de cores, limiarização, equalização do histograma de níveis de cinza, filtragem espacial com os operadores de Laplace e Sobel e suavização gaussiana. Além disso, foi desenvolvido e testado um algoritmo para o reconhecimento e contagem dos frutos nessas imagens, com taxas de detecção de falso-positivos de 3\% em imagens de boa qualidade. É possível se estimar a média do número de frutos visíveis por planta com um erro tolerado de 5\% com até 46 imagens e em aproximadamente 8 minutos, sem nenhuma interação humana. A ausência de flash e a incidência de luz solar direta sobre a planta podem prejudicar consideravelmente o desempenho do algoritmo. / Yield estimation is an important factor in a production process planning. In the case of citrus orchards, can be useful for processing plants management and as guidance for farmers, showing a decisive role in the product market strategies and cultivation practices. Several techniques are being studied for estimating citrus crop yield, but still presenting significant limitations. On the basis of the known correlation between the number of visible fruits in a digital image and the total of fruits present in an orange tree, an automatic and non-destructive method for green fruit feature extraction was developed with a combination of the techniques of color model conversion, thresholding, histogram equalization, spatial filtering with Laplace and Sobel operators and gaussian blur. In addition, we built and tested an algorithm to recognize and count the fruits, with detection rates of false-positives of 3\% for images acquired in good conditions. It is possible to estimate the mean number of visible fruits in the trees within a tolerated error of 5\% with up to 46 images and taking approximately 8 minutes without any human interaction. The absence of flash light or the direct incidence of solar light on the plant can significantly detract the algorithm results. / CNPq: 140600/2013-2

Secondary hardwood products manufacturers have been placing heavy emphasis on lumber yield improvements in recent years. More attention has been on lumber grade and cutting technology rather than cutting bill design. However, understanding the underlying physical phenomena of cutting bill requirements and yield is essential to improve lumber yield in rough mills. This understanding could also be helpful in constructing a novel lumber yield estimation model. The purpose of this study was to advance the understanding of the phenomena relating cutting bill requirements and yield. The scientific knowledge gained was used to describe and quantify the effect of part length, width, and quantity on yield. Based on this knowledge, a statistics based approach to the lumber yield estimation problem was undertaken. Rip-first rough mill simulation techniques and statistical methods were used to attain the study's goals. To facilitate the statistical analysis of the relationship of cutting bill requirements and lumber yield, a theoretical concept, called cutting bill part groups, was developed. Part groups are a standardized way to describe cutting bill requirements. All parts required by a cutting bill are clustered within 20 individual groups according to their size. Each group's midpoint is the representative part size for all parts falling within an individual group. These groups are made such that the error from clustering is minimized. This concept allowed a decrease in the number of possible factors to account for in the analysis of the cutting bill requirements - lumber yield relationship. Validation of the concept revealed that the average error due to clustering parts is 1.82 percent absolute yield. An orthogonal, 220-11 fractional factorial design of resolution V was then used to determine the contribution of different part sizes to lumber yield. All 20 part sizes and 113 of a total of 190 unique secondary interactions were found to be significant (a = 0.05) in explaining the variability in yield observed. Parameter estimates of the part sizes and the secondary interactions were then used to specify the average yield contribution of each variable. Parts with size 17.50 inches in length and 2.50 inches in width were found to contribute the most to higher yield. The positive effect on yield due to parts smaller than 17.50 by 2.50 inches is less pronounced because their quantity is relatively small in an average cutting bill. Parts with size 72.50 by 4.25 inches, on the other hand, had the most negative influence on high yield. However, as further analysis showed, not only the individual parts required by a cutting bill, but also their interaction determines yield. By adding a sufficiently large number of smaller parts to a cutting bill that requires large parts to be cut, high levels of yield can be achieved. A novel yield estimation model using linear least squares techniques was derived based on the data from the fractional factorial design. This model estimates expected yield based on part quantities required by a standardized cutting bill. The final model contained all 20 part groups and their 190 unique secondary interactions. The adjusted R2 for this model was found to be 0.94. The model estimated 450 of the 512 standardized cutting bills used for its derivation to within one percent absolute yield. Standardized cutting bills, whose yield level differs by more than two percent can thus be classified correctly in 88 percent of the cases. Standardized cutting bills whose part quantities were tested beyond the established framework, i.e. the settings used for the data derivation, were estimated with an average error of 2.19 percent absolute yield. Despite the error observed, the model ranked the cutting bills as to their yield level quite accurately. However, cutting bills from actual rough mill operations, which were well beyond the framework of the model, were found to have an average estimation error of 7.62 percent. Nonetheless, the model classified four out of five cutting bills correctly as to their ranking of the yield level achieved. The least squares estimation model thus is a helpful tool in ranking cutting bills for their expected yield level. Overall, the model performs well for standardized cutting bills, but more work is needed to make the model generally applicable for cutting bills whose requirements are beyond the framework established in this study. / Ph. D.

<p>Integrating unoccupied aerial systems (UAS) into agricultural remote sensing has revolutionized several domains, including crop yield estimation. This research arises from the need to combat citrus greening disease, a major threat to citrus production. Accurately estimating crop yields is crucial for evaluating the effectiveness of treatments and controls for this disease. In response, our study examined the efficacy of phenotypic data extracted from multi-temporal RGB and multispectral UAS images in estimating individual citrus tree yields before harvest and then using this as an indicator to analyze the effectiveness of the treatments and control choice.</p> <p>This study presents machine learning-based regression models for estimating individual citrus tree yields, utilizing the diverse features extracted to provide comprehensive insights into the citrus trees under investigation. Four machine learning algorithms, random forest regression, extreme gradient boosting regression, adaptive boosting, and support vector regression, were employed to build the yield estimation models. The experiment was designed in two phases: single-temporal and multi-temporal modeling.</p>