Almost every known large industry today produces and distributes goods at scale over a supply chain. In a typical supply chain, as a product travels across the network of supply chain nodes–from the producer, to the intermediate nodes (e.g pre-processor, distributor etc.), all the way to the end consumer–each node adds'value' to the product until it is ready to be sold off at the final node (e.g. retail stores). Supply chains are customarily designed and operated to minimise costs or maximise profits (or both). One of the most important considerations is to decide'when' and'how' much to replenish each product at each node of the supply chain, as it proceeds from production to consumption. While this customary approach works well for products that'do not perish' or have an unlimited shelf life, a straightforward extension of this approach for perishable products (e.g.

In an interview, Rajeev Rastogi, vice-president of ML at Amazon India, said the company has developed computer vision programs that recognize defects such as cuts and scratches on tomatoes and onions to figure out when they have gone bad. The system uses a mix of convolutional neural networks (CNNs) and visual transformer (ViTs) algorithms. CNNs are deep learning algorithms that can take image input and assign importance to various aspects of that image, while ViTs are specialized versions of transformer algorithms, which can weigh the significance of each part of data it gets. "In our grocery business, produce quality is the single-most important customer input and the number one driver of repeat purchase," Rastogi said. "Currently, quality is processed manually, which doesn't really scale. It's also very error-prone, is costly and doesn't have high repeatability. So, we developed a computer vision system for grading fresh produce quality by analysing images of produce," he said.

My life has always involved centering the voices of those historically marginalized in order to foster equitable communities. Growing up, I lived in a small suburb just outside of Cleveland, Ohio and I was fortunate enough to attend Laurel School, an all-girls school focused on encouraging young women to think critically and solve difficult world problems. But my lived experience at school was so different from kids who lived even on my same street. I was grappling with watching families around me contend with an economic recession, losing any financial security that they had and I wanted to do everything I could to change that. Even though my favorite courses at the time were engineering and African American literature, I was encouraged to pursue economics.

Every day across Australia, truckies are driving thousands of kilometres to get fresh produce from farms to markets. But what if the truck could do this job, without a driver? The NASDAQ-listed company TuSimple is celebrating a milestone, after transporting watermelons from Arizona to Oklahoma City using an autonomous truck. There were two humans in the truck during the trial -- and they did take control of the vehicle at the front and back end of the journey -- but for more than 1,500 kilometres, the truck was driving itself. "Our business case is to take the human driver out," TuSimple's Jim Mullen said.

With ambitions to establish a network of autonomous trucking routes across the US, transport startup TuSimple is taking some steady and significant steps forward as it proves its technology through trials and expands into Europe. The latest test run for its self-driving trucks involved hauling a load of fresh produce over hundreds of miles across the US, where it demonstrated that it can complete such tasks in a fast and highly efficient fashion. Previously, we've seen TuSimple's Level 4 autonomous trucks use its variety of cameras and sensors to move goods as part of trials for the US Postal Service and shipping giant UPS. This time around, the startup has partnered with fresh produce provider The Giumarra Companies and Associated Wholesale Grocers to explore autonomous trucking's potential in the fresh food industry. The trial started in Nogales, Arizona, where TuSimple's truck was loaded up with fresh watermelons from Giumarra's facility.

When you check out your fresh produce, the AI recognizes the product, even if it's wrapped in layers When you check out your fresh produce, the AI recognizes the product, even if it's wrapped in layers Submit some samples of your voice and AI will be able to speak exactly as you - what could go wrong? Submit some samples of your voice and AI will be able to speak exactly as you - what could go wrong? Open AI's GPT-3 text generator is released into the wild and generates content that is impermissible Open AI's GPT-3 text generator is released into the wild and generates content that is impermissible "Metacognition" is our ability to reflect on our own certainty - today's AI's are all too confident "Metacognition" is our ability to reflect on our own certainty - today's AI's are all too confident "Turking" for Amazon and its competitors is a lifeline for some, and exploitative grind for others "Turking" for Amazon and its competitors is a lifeline for some, and exploitative grind for others

Computational tools for forecasting yields and prices for fresh produce have been based on traditional machine learning approaches or time series modeling. We propose here an alternate approach based on deep learning algorithms for forecasting strawberry yields and prices in Santa Barbara county, California. Building the proposed forecasting model comprises three stages: first, the station-based ensemble model (ATT-CNN-LSTM-SeriesNet_Ens) with its compound deep learning components, SeriesNet with Gated Recurrent Unit (GRU) and Convolutional Neural Network LSTM with Attention layer (Att-CNN-LSTM), are trained and tested using the station-based soil temperature and moisture data of Santa Barbara as input and the corresponding strawberry yields or prices as output. Secondly, the remote sensing ensemble model (SIM_CNN-LSTM_Ens), which is an ensemble model of Convolutional Neural Network LSTM (CNN-LSTM) models, is trained and tested using satellite images of the same county as input mapped to the same yields and prices as output. These two ensembles forecast strawberry yields and prices with minimal forecasting errors and highest model correlation for five weeks ahead forecasts. Finally, the forecasts of these two models are ensembled to have a final forecasted value for yields and prices by introducing a voting ensemble. Based on an aggregated performance measure (AGM), it is found that this voting ensemble not only enhances the forecasting performance by 5% compared to its best performing component model but also outperforms the Deep Learning (DL) ensemble model found in literature by 33% for forecasting yields and 21% for forecasting prices.

Ford's long-running Miami self-driving car tests might just prove vital during the pandemic. The automaker, Argo AI and The Education Fund are running an eight-week pilot program that uses autonomous Fusion Hybrid cars to deliver fresh produce to Miami-Dade County students learning at home. Education Fund members load fruits and vegetables into the test vehicles, which drop them off at the homes of about 50 families in a bid to promote healthier eating. Two Argo AI specialists sit in each car to monitor the test, take over if necessary and make the final (contactless) delivery. Still, it helps test the self-driving AI in a mix of real-world situations ranging from apartment complexes to driveways, including places without any parking.

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.

Harris Farm Markets has deployed artificial intelligence (AI) modelling across its supply chain to get the right amount of fresh produce on its shelves at the time consumers want it. The growing grocery chain partnered with enterprise AI provider DataRobot to reduce its own data science workload and make accurate predictions about produce availability and customer demand. Head of IT Phil Cribb said the company began with a focus "very much" around fresh produce. "That equates to roughly about 50 percent of the business ad it's obviously highly seasonal, it fluctuates a lot. "Traditionally it's been very much'touch, feel, see'.