Yield Estimation Throughout the Growing Season

The 1993 adverse weather and floods in the midwestern United States caused enormous damage. Apart from the impacts to urban areas, most the ponding and flood damage in the Upper Midwest occurred on farmland with significant effects on agricultural yields and production. Although public officials and policy makers knew that the agricultural damage was extensive during the summer of 1993, information was imprecise. Because the setting of policy parameters, such as those relating to disaster assistance, emergency wetlands reserve, and the emergency conservation program, depended directly on expectations of harvested yields, intraseason quantification of weather-induced impacts was required. Given this need, the Food and Agricultural Policy Research Institute (FAPRI) of Iowa State University was asked to estimate the extent of the flood damage in Iowa, detailing the impacts on acreage, yields, prices, and farm income (Smith et al. 1993). This experience of 1993 induced FAPRI to examine alternative procedures for estimating yields throughout the growing season. One of the more promising alternatives, in terms of parsimony and data availability, is presented in this paper

GeoAI approach to Vineyard Yield Estimation

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsKnowing in advance vineyard yield is a key issue for growers, winemakers, policy makers, and regulators being fundamental to achieve the best balance between vegetative and reproductive growth, and to allow more informed decisions like thinning, irrigation and nutrient management, schedule harvest, optimize winemaking operations, program crop insurance, fraud detection and grape picking workforce demand. In a long-term scenario of perceived climate change, it is also essential for planning and regulatory purposes at the regional level. Estimating yield is complex and requires knowing driving factors related to climate, plant, and crop management that directly influence the number of clusters per vine, berries per cluster, and berry weight. These three yield components explain 60%, 30%, and 10% of the yield. The traditional methods are destructive, labor-demanding, and time-consuming, with low accuracy primarily due to operator errors and sparse sampling (compared to the inherent spatial variability in a production vineyard). Those are supported by manual sampling, where yield is estimated by sampling clusters weight and the number of clusters per vine, historical data, and extrapolation considering the number of vines in a plot. As the extensive research in the area clearly shows, improved applied methodologies are needed at different spatial scales. The methodological approaches for yield estimation based on indirect methods are primarily applicable at small scale and can provide better estimates than the traditional manual sampling. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Despite surpassing the limitations assigned to traditional manual sampling methods with the same or better results on accuracy, they still lack a fundamental key aspect: the real application in commercial vineyards. Another gap is the lack of solutions for estimating yield at broader scales (e.g., regional level). The perception is that decisions are more likely to take place on a smaller scale, which in some cases is inaccurate. It might be the case in regulated areas and areas where support for small viticulturists is needed and made by institutions with proper resources and a large area of influence. This is corroborated by the fact that data-driven models based on Trellis Tension and Pollen traps are being used for yield estimation at regional scales in real environments in different regions of the world. The current dissertation consists of the first study to identify through a systematic literature review the research approaches for predicting yield in vineyards for wine production that can serve as an alternative to traditional estimation methods, to characterize the different new approaches identifying and comparing their applicability under field conditions, scalability concerning the objective, accuracy, advantages, and shortcomings. In the second study following the identified research gap, a yield estimation model based on Geospatial Artificial Intelligence (GeoAI) with remote sensing and climate data and a machine-learning approach was developed. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The results show that this approach makes it possible to estimate wine grape yield accurately in advance at different scales

Detailed design specification for the Yield Estimation Subsystem Data Management System (YESDAMS)

There are no author-identified significant results in this report

Evaluating Dryland Crop/Livestock System Alternatives for Risk Management under Declining Irrigation in the Texas Panhandle

Production budgets for dryland crop and crop/livestock systems are developed to estimate yields, costs and returns for dryland wheat and sorghum and for alternative dryland crop/livestock systems. A crop simulation model aids yield estimation. The yield and return distributions are used to estimate risk and relative risk for included alternatives.Relative Risk, Ogallala Aquifer, Crop-Livestock Systems, Wheat, Sorghum, Crop Production/Industries, Farm Management, Livestock Production/Industries, Production Economics, Productivity Analysis,

Rice-yield prediction with multi-temporal sentinel-2 data and 3D CNN: A case study in Nepal

Crop yield estimation is a major issue of crop monitoring which remains particularly challenging in developing countries due to the problem of timely and adequate data availability. Whereas traditional agricultural systems mainly rely on scarce ground-survey data, freely available multi-temporal and multi-spectral remote sensing images are excellent tools to support these vulnerable systems by accurately monitoring and estimating crop yields before harvest. In this context, we introduce the use of Sentinel-2 (S2) imagery, with a medium spatial, spectral and temporal resolutions, to estimate rice crop yields in Nepal as a case study. Firstly, we build a new large-scale rice crop database (RicePAL) composed by multi-temporal S2 and climate/soil data from the Terai districts of Nepal. Secondly, we propose a novel 3D Convolutional Neural Network (CNN) adapted to these intrinsic data constraints for the accurate rice crop yield estimation. Thirdly, we study the effect of considering different temporal, climate and soil data configurations in terms of the performance achieved by the proposed approach and several state-of-the-art regression and CNN-based yield estimation methods. The extensive experiments conducted in this work demonstrate the suitability of the proposed CNN-based framework for rice crop yield estimation in the developing country of Nepal using S2 data