A plant's phenome is defined by its physical and biochemical characteristics, and is the result of the interaction of its genome and its environment.

The likes of shepherd's pie and fish & chips soon be off Britain's dinner menu in favour of more eco-friendly options, according to a new report. Scientists have teamed up with HelloFresh to predict what Brits will be eating in just 10 years time as we fight to halt climate change. And the menu of the near future reveals five very bizarre options – with no meat in sight. There's a stir fry with noodles made out of kelp (a type of brown algae) as well as'meatballs' made with mushrooms on a bed of sorghum. There's also teff galette – a French-style tart made out of teff, a highly-nutritious ancient grain – served with dandelion salad.

Vegetation indices allow to efficiently monitor vegetation growth and agricultural activities. Previous generations of satellites were capturing a limited number of spectral bands, and a few expert-designed vegetation indices were sufficient to harness their potential. New generations of multi- and hyperspectral satellites can however capture additional bands, but are not yet efficiently exploited. In this work, we propose an explainable-AI-based method to select and design suitable vegetation indices. We first train a deep neural network using multispectral satellite data, then extract feature importance to identify the most influential bands. We subsequently select suitable existing vegetation indices or modify them to incorporate the identified bands and retrain our model. We validate our approach on a crop classification task. Our results indicate that models trained on individual indices achieve comparable results to the baseline model trained on all bands, while the combination of two indices surpasses the baseline in certain cases.

The first step toward Seed Phenotyping i.e. the comprehensive assessment of complex seed traits such as growth, development, tolerance, resistance, ecology, yield, and the measurement of pa-rameters that form more complex traits is the identification of seed type. Generally, a plant re-searcher inspects the visual attributes of a seed such as size, shape, area, color and texture to identify the seed type, a process that is tedious and labor-intensive. Advances in the areas of computer vision and deep learning have led to the development of convolutional neural networks (CNN) that aid in classification using images. While they classify efficiently, a key bottleneck is the need for an extensive amount of labelled data to train the CNN before it can be put to the task of classification. The work leverages the concepts of Contrastive Learning and Domain Randomi-zation in order to achieve the same. Briefly, domain randomization is the technique of applying models trained on images containing simulated objects to real-world objects. The use of synthetic images generated from a representational sample crop of real-world images alleviates the need for a large volume of test subjects. As part of the work, synthetic image datasets of five different types of seed images namely, canola, rough rice, sorghum, soy and wheat are applied to three different self-supervised learning frameworks namely, SimCLR, Momentum Contrast (MoCo) and Build Your Own Latent (BYOL) where ResNet-50 is used as the backbone in each of the networks. When the self-supervised models are fine-tuned with only 5% of the labels from the synthetic dataset, results show that MoCo, the model that yields the best performance of the self-supervised learning frameworks in question, achieves an accuracy of 77% on the test dataset which is only ~13% less than the accuracy of 90% achieved by ResNet-50 trained on 100% of the labels.

Agriculture has come a long way in the past century. We produce more food than ever before -- but our current model is unsustainable, and as the world's population rapidly approaches the 8 billion mark, modern food production methods will need a radical transformation if they're going to keep up. But luckily, there's a range of new technologies that might make it possible. In this series, we'll explore some of the innovative new solutions that farmers, scientists, and entrepreneurs are working on to make sure that nobody goes hungry in our increasingly crowded world. Ever since American citizens' industrial age migration from the country to the city, urban areas have tended to be associated with cutting-edge technologies.

The matter of growing enough food to feed the planet is a serious issue. Already, one in nine people lack sufficient sustenance, according to the United Nations--and the problem is only going to get worse. Mix a global population projected to reach 9 billion in 20 years, from 7.5 billion today, with drought and other effects of climate change, and a farm labor shortage in places like California, and there's a crisis in the making. Now a growing number of researchers and are turning to robotics to address the problem. Specifically, they're combining the sensing abilities of robots with data analysis made possible by artificial intelligence technology to improve farmers' ability to grow and manage their crops more intelligently.

The robot scans the stalks of sorghum, photographs them, looking for disease. It uses laser scanning to estimate their height and volume. And, every now and then, it reaches out a robotic arm, grabs hold of one of the stalks, and stabs it with a probe to measure the thickness of the rind. Welcome to the farm of the future. This little robo-farmer is just one project developed by FarmView, a multidisciplinary, multi-institution effort to put advanced technology to use on the farm.