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| Filter results9 paper(s) found. |
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1. Effective Use of a Debris Cleaning Brush for Mechanical Wild Blueberry HarvestingWild blueberries are an important horticultural crop native to northeastern North America. Management of wild blueberry fields has improved over the past decade causing increased plant density and leaf foliage. The majority of wild blueberry fields are picked mechanically using tractor mounted harvesters with 16 rotating rakes that gently comb through the plants. The extra foliage has made it more difficult for the cleaning brush to remove unwanted debris (leaf, stems, weeds, etc.) from the picker... K. Esau, Q. Zaman, A. Farooque, A. Schumann |
2. Experiences in the Development of Commercial Web-Based Data Engines to Support UK Growers Within an Industry-Academic PartnershipThe lifecycle of Precision Agriculture data begins the moment that the measurement is taken, after which it may pass through each multiple data processes until finally arriving as an output employed back in the production system. This flow can be hindered by the fact that many farm datasets have different spatial resolutions. This makes the process to aggregate or analyse multiple Precision Agriculture layers arduous and time consuming. Precision Decisions Ltd located in Yorkshire,... J. Taylor, Y. Shahar, P. James, C. Blacker, S. Leese, R. Sanderson, R. Kavanagh |
3. Design of Ground Surface Sensing Using RADARGround sensing is the key task in harvesting head control system. Real time sensing of field topography under vegetation canopy is very challenging task in wild blueberry cropping system. This paper presents the design of an ultra-wide band RADAR sensing, scanning device to recognize the soil surface level under the canopy structure. Requirements for software and hardware were considered to determine the usability of the ultra-wide band RADAR system.An automated head elevation... M.M. Mohamed, Q. Zaman, T. Esau, A. Farooque |
4. Integration of High Resolution Multitemporal Satellite Imagery for Improving Agricultural Crop Classification: a Case StudyTimely and accurate agriculture information is vital for ensuring global food security. Satellite imagery has already been proved as a reliable tool for remote crop mapping. Planet satellite imagery provides high cadence, global satellite coverage with higher temporal and spatial resolution than the Landsat-8 and Sentinel-2. This study examined the potential of utilizing high-resolution multitemporal imagery along with and normalized difference vegetation index (NDVI) to map the agricultural crops... U. Ali, T. Esau, A. Farooque, Q. Zaman |
5. Suitability of ML Algorithms to Predict Wild Blueberry Harvesting LossesThe production of wild blueberries (Vaccinium angustifolium.) is contributing 112.2 million dollars to the Canada’s revenue which can be further increased through controlling harvest losses. A precise prediction of blueberry harvesting losses is necessary to mitigate such losses. In this study, the performance of three machine learning (ML) models was evaluated to predict the wild blueberry harvest losses on the ground. The data from four commercial fields in Atlantic Canada were... H. Khan, T. Esau, A. Farooque, F. Abbas |
6. Predicting Soil Cation Exchange Capacity from Satellite Imagery Using Random Forest ModelsCrop yield variability is often attributed to spatial variation in soil properties. Remote sensing offers a practical approach to capture soil surface properties over large areas, enabling the development of detailed soil maps. This study aimed to predict cation exchange capacity (CEC), a key indicator of soil quality, in the agricultural fields of the Lower Mississippi Alluvial Valley using digital soil mapping techniques. A total of 15,586 soil samples were collected from agricultural fields... I. Muller, J. Czarnecki, M. Li, B.K. Smith |
7. Application of Advanced Soft Computing to Estimate Potato Tuber Yield: a Case Study from Atlantic CanadaThe potato crop plays a crucial role in the economy of Atlantic Canada, particularly in Prince Edward Island and New Brunswick, where it contributes significantly to potato production. To help farmers make informed decisions for sustainable and profitable farming, this study was conducted to examine the variations in potato tuber yield based on thirty soil properties collected over four growing seasons through experimental trials. The study employed an advanced and explainable ensemble model called... Q.U. Zaman, A. Farooque, M. Jamei, T.J. Esau |
8. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and VisionAdvancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor. While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi |
9. A Growth Stage Centric Approach to Field Scale Corn Yield Estimation by Leveraging Machine Learning Methods from Multimodal DataField scale yield estimation is labor-intensive, typically limited to a few samples in a given field, and often happens too late to inform any in-season agronomic treatments. In this study, we used meteorological data including growing degree days (GDD), photosynthetic active radiation (PAR), and rolling average of rainfall combined with hybrid relative maturity, organic matter, and weekly growth stage information from three small-plot research locations... L. Waltz, S. Katari, S. Khanal, T. Dill, C. Porter, O. Ortez, L. Lindsey, A. Nandi |