Precision spraying evaluation requires automation primarily in post-spraying imagery. In this paper we propose an eXplainable Artificial Intelligence (XAI) computer vision pipeline to evaluate a precision spraying system post-spraying without the need for traditional agricultural methods. The developed system can semantically segment potential targets such as lettuce, chickweed, and meadowgrass and correctly identify if targets have been sprayed. Furthermore, this pipeline evaluates using a domain-specific Weakly Supervised Deposition Estimation task, allowing for class-specific quantification of spray deposit weights in {\mu}L. Estimation of coverage rates of spray deposition in a class-wise manner allows for further understanding of effectiveness of precision spraying systems. Our study evaluates different Class Activation Mapping techniques, namely AblationCAM and ScoreCAM, to determine which is more effective and interpretable for these tasks. In the pipeline, inference-only feature fusion is used to allow for further interpretability and to enable the automation of precision spraying evaluation post-spray. Our findings indicate that a Fully Convolutional Network with an EfficientNet-B0 backbone and inference-only feature fusion achieves an average absolute difference in deposition values of 156.8 {\mu}L across three classes in our test set. The dataset curated in this paper is publicly available at https://github.com/Harry-Rogers/PSIE

The advancements in precision agriculture are vital to support the increasing demand for global food supply. Precision spot spraying is a major step towards reducing chemical usage for pest and weed control in agriculture. A novel spot spraying algorithm that autonomously detects weeds and performs trajectory planning for the sprayer nozzle has been proposed. Furthermore, this research introduces a vision-based autonomous navigation system that operates through the detected crop row, effectively synchronizing with an autonomous spraying algorithm. This proposed system is characterized by its cost effectiveness that enable the autonomous spraying of herbicides onto detected weeds.

University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) researchers plan to use artificial intelligence (AI) technology to quantify damage to fruits and vegetables caused by extreme weather events. One such extreme weather event was Hurricane Ian, which struck Florida on Sept. 28, 2022. The storm's damage to all crops, livestock and aquaculture products was initially estimated at up to $1.56 billion, based on farmer surveys. This figure is critical because growers need to know the extent of crop loss to file insurance claims and apply for other recovery aid. Ampatzidis has already developed Agroview and AgroSense at his lab at the Southwest Florida Research and Education Center.

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper health care, education, living conditions, wages, and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

Graybeards may remember the thrill they felt when pencil-laden math calculations moved warp speed ahead into the calculator age. These days, artificial intelligence (AI) promises to bring the same heat to agriculture that it did to math classes decades ago. Artificial intelligence is a technology that includes several subsets such as machine learning, says Rania Khalaf, Inari chief information and data officer. "Machine learning enables computers to mathematically predict outcomes or make classifications by finding patterns in large amounts of data," she says. "It then learns to update these patterns or classifications over time as it sees new data."

Greg Kruger pauses for what seems like an eternity during his presentation, but it actually just lasts six seconds. The senior agronomist for BASF's xarvio digital farming division did it to prove a point about BASF's Smart Farming joint collaboration with Bosch that includes precision spraying technology the firms call Smart Spraying. The strategy teams machine-learning algorithms with computer vision to enable "green-on-green" spraying that distinguishes between weeds and crops in-season. Kruger's presentation was part of a BASF media briefing held before this week's Commodity Classic in New Orleans. "In the six seconds that I paused, we've taken 1,000 images [with Smart Spraying] on the boom," says Kruger.

Artificial intelligence is not the scary, half-human half-robot movie character some might think it is, says Precision AI founder Daniel McCann. "AI is just a data processing system that points out patterns in huge volumes of data and that's it," he said during a presentation at the virtual Canada's Farm Show. For agriculture, it represents the future. McCann said he believes within the next 15 years common pieces of farm equipment, such as the broadcast sprayer, will become like a BlackBerry -- still around, not too common and not too efficient. But he said AI in agriculture is at an interim step along the way to that.

These days it's hard to find a public company that isn't talking up how artificial intelligence is transforming its business. From the obvious (Tesla using AI to improve auto-pilot performance) to the less obvious (Levis using AI to drive better product decisions), everyone wants in on AI. To get there, however, organizations are going to need to get a lot smarter about data. To even get close to serious AI you need supervised learning which, in turn, depends on labeled data. Raw data must be painstakingly labeled before it can be used to power supervised learning models.

Artificial intelligence allows farmers to spray weeds while keeping the crop untouched. With crop prices in the dumpster and the world's population growing among a changing climate, artificial intelligence is becoming a life-saving measure for many farmers. From automated planting and harvesting to unmanned vehicles for cultivation and soil sampling, AI has begun to make it more cost efficient for producers to do their job. One of the largest roadblocks is herbicides. According to a 2016 University of Illinois study, the chemical prices are on the rise and pose a big threat to a farmer's bottom line.

The initiatives, overseen by information technology executives, are aimed at helping hotels dig out of what has been a dire season, with steep declines in occupancy, staff layoffs and a fear among some guests of contracting Covid-19. "Without technology, there's no way those companies recover fully," said Les Ottolenghi, who was chief information officer at Caesars Entertainment Corp. until last November. Caesars merged last month with Eldorado Resorts Inc. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. U.S. hotel occupancy collapsed from about 60% in February to roughly 22% in April, according to data analytics firm STR, owned by CoStar Group Inc.