Kerry B. Walsh

What is he best known for?

The fields of study he is best known for:

• Botany
• Ecology
• Agriculture

Kerry B. Walsh spends much of his time researching Botany, Linear regression, Calibration, Artificial intelligence and Near-infrared spectroscopy. His study in Botany focuses on Carica in particular. Kerry B. Walsh has included themes like Partial least squares regression and Dry matter in his Linear regression study.

His studies deal with areas such as Standard error and Brix as well as Calibration. His work on RGB color model, Machine vision and Segmentation as part of general Artificial intelligence research is frequently linked to Tree, thereby connecting diverse disciplines of science. His Near-infrared spectroscopy study combines topics from a wide range of disciplines, such as Soluble solids and Analytical chemistry.

His most cited work include:

• Short-Wavelength Near-Infrared Spectra of Sucrose, Glucose, and Fructose with Respect to Sugar Concentration and Temperature (155 citations)
• Prediction of mango eating quality at harvest using short-wave near infrared spectrometry (123 citations)
• Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content (118 citations)

What are the main themes of his work throughout his whole career to date?

Kerry B. Walsh mostly deals with Horticulture, Botany, Agronomy, Near-infrared spectroscopy and Partial least squares regression. As a part of the same scientific family, Kerry B. Walsh mostly works in the field of Horticulture, focusing on Dry matter and, on occasion, Cultivar. His work on Phloem, Xylem and Vascular bundle as part of general Botany research is often related to Vascular transport, thus linking different fields of science.

In general Agronomy study, his work on Sorghum, Shoot and Compost often relates to the realm of Rumen, thereby connecting several areas of interest. His work in Near-infrared spectroscopy addresses issues such as Calibration, which are connected to fields such as Linear regression. His Partial least squares regression study is concerned with the field of Statistics as a whole.

He most often published in these fields:

• Horticulture (22.66%)
• Botany (20.69%)
• Agronomy (15.27%)

What were the highlights of his more recent work (between 2016-2021)?

• Statistics (10.84%)
• Orchard (5.91%)
• Horticulture (22.66%)

In recent papers he was focusing on the following fields of study:

Kerry B. Walsh mainly focuses on Statistics, Orchard, Horticulture, Machine vision and Tree. His research in Statistics intersects with topics in Artificial neural network, Random forest and Dry matter. His study looks at the relationship between Orchard and topics such as Pixel, which overlap with Test set.

His Horticulture study incorporates themes from Thinning and Hydrogen peroxide. His Machine vision research incorporates elements of Segmentation, Fruit weight, Canopy, Panicle and Agricultural engineering. His Partial least squares regression research includes elements of Cultivar, Cross-validation, Mean squared error, Calibration and Data set.

Between 2016 and 2021, his most popular works were:

• Natural product-derived phytochemicals as potential agents against coronaviruses: A review. (94 citations)
• Deep learning - method overview and review of use for fruit detection and yield estimation (71 citations)
• Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘ MangoYOLO ’ (67 citations)

In his most recent research, the most cited papers focused on:

• Botany
• Ecology
• Agriculture

Kerry B. Walsh focuses on Context, Orchard, Artificial intelligence, Dry matter and Partial least squares regression. His work carried out in the field of Orchard brings together such families of science as Pixel and Machine vision. His work on RGB color model as part of general Artificial intelligence study is frequently connected to Tree, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.

His Dry matter research integrates issues from Thinning, Girdling, Water stress, Statistics and Brix. His Statistics research incorporates themes from Ripening, Cultivar and Feature selection. He has researched Partial least squares regression in several fields, including Mean squared error and Hyperspectral imaging.