With the goal of developing fully autonomous cooking robots, developing robust systems that can chop a wide variety of objects is important. Existing approaches focus primarily on the low-level dynamics of the cutting action, which overlooks some of the practical real-world challenges of implementing autonomous cutting systems. In this work we propose an autonomous framework to sequence together action primitives for the purpose of chopping fruits and vegetables on a cluttered cutting board. We present a novel technique to leverage vision foundational models SAM and YOLO to accurately detect, segment, and track fruits and vegetables as they visually change through the sequences of chops, finetuning YOLO on a novel dataset of whole and chopped fruits and vegetables. In our experiments, we demonstrate that our simple pipeline is able to reliably chop a variety of fruits and vegetables ranging in size, appearance, and texture, meeting a variety of chopping specifications, including fruit type, number of slices, and types of slices.

The researchers, from the University of Cambridge, programmed their robotic chef with a'cookbook' of eight simple salad recipes. After watching a video of a human demonstrating one of the recipes, the robot was able to identify which recipe was being prepared and make it. In addition, the videos helped the robot incrementally add to its cookbook. At the end of the experiment, the robot came up with a ninth recipe on its own. Their results, reported in the journal IEEE Access, demonstrate how video content can be a valuable and rich source of data for automated food production, and could enable easier and cheaper deployment of robot chefs.

Carrot is a famous nutritional vegetable and developed all over the world. Different diseases of Carrot has become a massive issue in the carrot production circle which leads to a tremendous effect on the economic growth in the agricultural sector. An automatic carrot disease detection system can help to identify malicious carrots and can provide a guide to cure carrot disease in an earlier stage, resulting in a less economical loss in the carrot production system. The proposed research study has developed a web application Carrot Cure based on Convolutional Neural Network (CNN), which can identify a defective carrot and provide a proper curative solution. Images of carrots affected by cavity spot and leaf bright as well as healthy images were collected. Further, this research work has employed Convolutional Neural Network to include birth neural purposes and a Fully Convolutional Neural Network model (FCNN) for infection order. Different avenues regarding different convolutional models with colorful layers are explored and the proposed Convolutional model has achieved the perfection of 99.8%, which will be useful for the drovers to distinguish carrot illness and boost their advantage.

Machine Learning is, as its name defines it, a machine who learns. The first reference of Machine Learning dates back to 1959 and attributed to artificial intelligence and computer gaming pioneer; Arthur Lee Samuel. Imagine yourself at the supermarket, shopping for your weekly meals. You pass through the fruit and veg section. You are looking to buy some fruits today, so you have to sort through the section to find fruits instead of vegetables.

You're looking for bruises on an apple or squeezing an avocado in your local supermarket, but the chances are it's already been checked – inside and out – by artificial intelligence. New software can analyze every aspect of fruit and vegetables before they reach supermarket shelves. It can determine a product's shelf life, and check for internal rot and pesticide residues. It integrates sensors and advanced optics into 360-degree cameras that see far more than the human eye, and that means a drastic reduction in food loss. As much as a fifth of all fresh produce is lost before it ever reaches the grocery store.

There's a reason so much of the produce sold in the grocery store often tastes like cardboard. Actually, there are several reasons. Most of them stem from the fact that tastiness is far down on the list of what the food industry encourages plant breeders to prioritize when developing new varieties -- called "cultivars" -- of produce. When they do want to focus on taste, breeders don't have good tools for quickly sampling the fruit from thousands of cultivars. In a surprising new paper, researchers at the University of Florida describe a new method for "tasting" produce based on its chemical profile.

Global food insecurity is expected to worsen in the coming decades with the accelerated rate of climate change and the rapidly increasing population. In this vein, it is important to remove inefficiencies at every level of food production. The recent advances in deep learning can help reduce such inefficiencies, yet their application has not yet become mainstream throughout the industry, inducing economic costs at a massive scale. To this point, modern techniques such as CNNs (Convolutional Neural Networks) have been applied to RPQD (Raw Produce Quality Detection) tasks. On the other hand, Transformer's successful debut in the vision among other modalities led us to expect a better performance with these Transformer-based models in RPQD. In this work, we exclusively investigate the recent state-of-the-art Swin (Shifted Windows) Transformer which computes self-attention in both intra- and inter-window fashion. We compare Swin Transformer against CNN models on four RPQD image datasets, each containing different kinds of raw produce: fruits and vegetables, fish, pork, and beef. We observe that Swin Transformer not only achieves better or competitive performance but also is data- and compute-efficient, making it ideal for actual deployment in real-world setting. To the best of our knowledge, this is the first large-scale empirical study on RPQD task, which we hope will gain more attention in future works.

An agricultural technology (ag-tech) startup in San Francisco, Plenty, plants its crops vertically indoors, in a year-round operation employing AI and robots that uses 95% less water and 99% less land than conventional farming. Plenty's vertical farm approach can produce the same quantity of fruits and vegetables as a 720-acre flat farm, on only two acres. "Vertical farming exists because we want to grow the world's capacity for fresh fruits and vegetables, and we know it's necessary," stated Nate Storey, cofounder and chief science officer of the startup Plenty, in an account in Intelligent Living. The yield of 400x that of flat farms makes vertical farming "not just an incremental improvement," and the fraction of water use "is also critical in a time of increasing environmental stress and climate uncertainty," Storey stated. "All of these are truly game-changers."

Editor's note: Devendra Chandani is co-founder and head of US at Intello Labs, based in New Jersey. Headquartered in Gurugram, India, Intello Labs uses machine learning tech to grade the quality of agricultural produce. The views expressed in this article are the author's own. Fresh fruits and vegetables are a critical ingredient for food companies that make anything from juices and smoothies through to sauces, pastes, and pulps. The characteristics of fresh produce differ by variety and season, unlike with many other raw materials.

The food industry has been taking heat from consumers and critics who are demanding healthier ingredients, transparency about where their meals come from and better treatment of animals. There is also a growing awareness of the harmful effect that food production can have on the environment. Now big food companies and entrepreneurs are taking advantage of advances in robotics and data science to meet those challenges--and the trend will likely continue as technology improves, and natural ingredients become easier to cultivate. It also helps that venture capitalists are flocking to the companies cooking up these innovations. This year is on pace to set a record for this decade for venture investment in food technology, according to the PitchBook Platform data provider.