mixing instructions for crossbow herbicide Crossbow Herbicide
SKU: 22176302657
mixing instructions for crossbow herbicide

mixing instructions for crossbow herbicide Crossbow Herbicide

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Description

mixing instructions for crossbow herbicide Crossbow HerbicideCrossbow specialty herbicide is recommended for control of most species of unwanted woody plants, as well as annual and perennial broadleaf weeds, growing on rangeland, permanent grass pastures, CRP, fence rows, non irrigation ditch banks, roadsides, other non crop areas, and industrial sites Concentrated Mixing Instructions: 2 4 quarts per acre (1. 45 2. 9 ounces per 1,000 square feet). For spot spray applications: 2 3 ounces per gallon of water.

Crossbow specialty herbicide is recommended for control of most species of unwanted woody plants, as well as annual and perennial broadleaf weeds, growing on rangeland, permanent grass pastures, CRP, fence rows, non-irrigation ditch banks, roadsides, other non-crop areas, and industrial sites

  • Concentrated
  • Mixing Instructions: 2-4 quarts per acre (1.45-2.9 ounces per 1,000 square feet). For spot spray applications: 2-3 ounces per gallon of water.
  • Active Ingredient - 2,4-dichlorophenoxyacetic acid, butoxyethyl ester - 34.4%
  • Controls most types of undesirable trees, such as ash, sumac and other woody plants as well as briars, poison ivy, thistles and most types of broadleaf weeds. Also known to control kudzu and bamboo with subsequent applications.
  • MSDS Sheet (.PDF)
  • Label Sheet (.PDF)

KEY WEEDS CONTROLLED

Alder
Amaranth, spiny
Ash
Beech
Bindweed, field
Birch
Black locust
Blackberry
Blueweed
Boneset
Buckbrush
Buttercup, annual
Buttercup, tall
Carrot, wild
Cascara
Cherry (except black)
Chickweed, mouse ear
Chicory
Cinquefoil
Clover, white
Common
Common persimmon
Cottonwood
Dandelion
Dock, curly
Dogfennel
Dogwood
Elderberry
Elm
Fleabane, annual
Galinsoga, hairy
Goatsbeard
Goldenrod
Hawthorn
Hazel
Henbit
Honeylocust
Honeysuckle
Horsenettle
Horseweed, (marestail)
Ironweed, western
Ivy, ground
Kochia
Kudzu
Lambsquarters
Lespedeza
Maples (except bigleaf and vine)
Marshelder
Milkweed
Multiflora rose
Mustard, wild
Oxalis
Pennycress, field
Pepperweed, field
Pigweed, redroot
Pine
Plantain, broadleaf
Plantain, narrow-leaf
Poison ivy
Poison oak
Pokeweed
Purslane, annual
Ragweed, common
Russian olive
Salmonberry
Sassafras (top growth)
Scotch broom
Sesbania, hemp
Sneezeweed, bitter
Sowthistle, annual
Sowthistle, perennial
Spurge, leafy
Spurge, thyme-leaf
Sumac
Sunflower
Sweetgum
Sycamore
Tamarack
Thistle, Canada
Thistle, Russian
Thistle, bull
Thistle, musk (nodding)
Trumpetcreeper
Vetch
Violet, wild
Virginia creeper
Wax myrtle (top growth)
White oak
Wild grape
Willow
Wormwood, biennial
Yarrow
Yellow rocket

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SKU: 22176302657

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William P Ross
Lowell, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
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Verified Purchase
Adam
Fort Morgan, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026
A
Verified Purchase
Amazon Customer
Louisville, US
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff! If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017
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mackster
Port Orchard, US
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper. As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture. So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money. The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
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Reviewed in the United States on May 15, 2018
S
Verified Purchase
Stergios Papadimitriou
Port Orchard, US
★★★★★ 5
The classic textbook on Deep Learning
Format: Hardcover
Deep Learning is the promising direction towards general purpose effective artificial intelligence. There is an explosion of fruitful research in recent years and a lot of applications pursued mainly from technology giants as Google, Amazon, etc. and outstanding research institutions. The book "Deep Learning " by Ian Goodfellow, Yoshua Bengio, Aaron Gourville, is an excellent piece of work. They manage to present rather difficult things in an understandable manner. The theoretical presentation is outstanding typical of "classic" books. Also, the book stays close to the practical applicability of all the methods and discusses applications extensively. There are a lot of other useful books on deep learning that follow a more practical approach by focusing on a particular deep learning software package, but this one book is certainly much more essential since it provides the required theoretical background in order to be able to do serious work on deep learning. I consider the book as "must have" for anyone that works on deep learning either in an academic or in an industrial environment.
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Reviewed in the United States on August 25, 2018

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