Despite progress in AI-based plant diagnostics, sugarcane farmers in low-resource regions remain vulnerable to leaf diseases due to the lack of scalable, efficient, and interpretable tools. Many deep learning models fail to generalize under real-world conditions and require substantial computational resources, limiting their use in resource-constrained regions. In this paper, we present SugarcaneLD-BD, a curated dataset for sugarcane leaf-disease classification; SugarcaneShuffleNet, an optimized lightweight model for rapid on-device diagnosis; and SugarcaneAI, a Progressive Web Application for field deployment. SugarcaneLD-BD contains 638 curated images across five classes, including four major sugarcane diseases, collected in Bangladesh under diverse field conditions and verified by expert pathologists. To enhance diversity, we combined SugarcaneLD-BD with two additional datasets, yielding a larger and more representative corpus. Our optimized model, SugarcaneShuffleNet, offers the best trade-off between speed and accuracy for real-time, on-device diagnosis. This 9.26 MB model achieved 98.02% accuracy, an F1-score of 0.98, and an average inference time of 4.14 ms per image. For comparison, we fine-tuned five other lightweight convolutional neural networks: MnasNet, EdgeNeXt, EfficientNet-Lite, MobileNet, and SqueezeNet via transfer learning and Bayesian optimization. MnasNet and EdgeNeXt achieved comparable accuracy to SugarcaneShuffleNet, but required significantly more parameters, memory, and computation, limiting their suitability for low-resource deployment. We integrate SugarcaneShuffleNet into SugarcaneAI, delivering Grad-CAM-based explanations in the field. Together, these contributions offer a diverse benchmark, efficient models for low-resource environments, and a practical tool for sugarcane disease classification. It spans varied lighting, backgrounds and devices used on-farm

In causal inference, whether through randomized controlled trials or observational studies, access to both treated and control units is essential for estimating the effect of a treatment on an outcome of interest. When treatment assignment is random, the average treatment effect (ATE) can be estimated directly by comparing outcomes between groups. In non-randomized settings, various techniques are employed to adjust for confounding and approximate the counterfactual scenario to recover an unbiased ATE. A common challenge, especially in observational studies, is the absence of units clearly labeled as controls-that is, units known not to have received the treatment. To address this, we propose positive-unlabeled (PU) learning as a framework for identifying, with high confidence, control units from a pool of unlabeled ones, using only the available treated (positive) units. We evaluate this approach using both simulated and real-world data. We construct a causal graph with diverse relationships and use it to generate synthetic data under various scenarios, assessing how reliably the method recovers control groups that allow estimates of true ATE. We also apply our approach to real-world data on optimal sowing and fertilizer treatments in sustainable agriculture. Our findings show that PU learning can successfully identify control (negative) units from unlabeled data based only on treated units and, through the resulting control group, estimate an ATE that closely approximates the true value. This work has important implications for observational causal inference, especially in fields where randomized experiments are difficult or costly. In domains such as earth, environmental, and agricultural sciences, it enables a plethora of quasi-experiments by leveraging available earth observation and climate data, particularly when treated units are available but control units are lacking.

Knowledge Graphs have become increasingly popular due to their wide usage in various downstream applications, including information retrieval, chatbot development, language model construction, and many others. Link prediction (LP) is a crucial downstream task for knowledge graphs, as it helps to address the problem of the incompleteness of the knowledge graphs. However, previous research has shown that knowledge graphs, often created in a (semi) automatic manner, are not free from social biases. These biases can have harmful effects on downstream applications, especially by leading to unfair behavior toward minority groups. To understand this issue in detail, we develop a framework -- AuditLP -- deploying fairness metrics to identify biased outcomes in LP, specifically how occupations are classified as either male or female-dominated based on gender as a sensitive attribute. We have experimented with the sensitive attribute of age and observed that occupations are categorized as young-biased, old-biased, and age-neutral. We conduct our experiments on a large number of knowledge triples that belong to 21 different geographies extracted from the open-sourced knowledge graph, Wikidata. Our study shows that the variance in the biased outcomes across geographies neatly mirrors the socio-economic and cultural division of the world, resulting in a transparent partition of the Global North from the Global South.

The app can detect Fusarium wilt, Xanthomonas wilt, bunchy top disease, black sigatoka, yellow sigatoka, and corm weevil. Fusarium Tropical race 4 fungus (TR4) has decimated banana plantations and smallholders' crops in Asia and Africa and has now spread to Latin America. Last week, Colombian officials officially confirmed the presence of TR4 in La Guajira province, declaring a state of national emergency as a result. Developed with support from Bioversity International and the International Center for Tropical Agriculture (CIAT), the AI-powered tool is built into an app called Tumaini – Swahili for'hope' – that allows farmers to take action quickly, thus preventing a widespread outbreak. The information is also uploaded to a global system that allows for large-scale monitoring.

We are a Ukraine-based company which means that our parents and grandparents lived in the era of infamous Soviet collective farms, where tractors were considered to be an ultimate technology. For them, a smart farm will sound like a fairy tale. So let it be, a fairy tale of a smart farm. First of all, what is a smart farm? Smart Farming is a concept of farming management using modern Information and Communication Technologies to increase the quantity and quality of products.

Nearly a third of the world's food supply gets thrown out -- from produce surplus in farmers' fields to expired products discarded by retailers to leftovers. That's the issue Timothy Richards, the Morrison Chair of Agribusiness in the W. P. Carey School of Business at Arizona State University, will be trying to solve with a new grant from the USDA's Agriculture and Food Research Initiative (NIFA). "Food waste occurs at virtually all stages of the supply chain from the farmer to the retailer to the consumer -- resulting in the disposal of potentially usable food in nearly every sector of the food system in the distribution channel between farmers and consumers," Richards said. The goal of the research is to combine grocers' inventory with machine learning algorithms to develop a better system for matching supply to consumer demand fluctuations. This would ensure customers get what they want without the need for excess food.

This ebook, based on the latest ZDNet / TechRepublic special feature, explores how infrastructure around the world is being linked together via sensors, machine learning and analytics. As growing numbers of internet-connected sensors are built into cars, planes, trains and buildings, businesses are amassing vast amounts of data. Tapping into that data to extract useful information is a challenge that's starting to be met using the pattern-matching abilities of machine learning (ML) -- a subset of the field of artificial intelligence (AI). Firms are increasingly feeding data collected by Internet of Things (IoT) sensors -- situated everywhere from farmers' fields to train tracks -- into machine-learning models and using the resulting information to improve their business processes, products and services. One of the most visible pioneers is Siemens, whose Internet of Trains project has enabled it to move from simply selling trains and infrastructure to offering a guarantee its trains will arrive on time.

We introduce a large scale crowdsourced text adventure game as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dialogue with other agents. Models and humans can both act as characters within the game. We describe the results of training state-of-the-art generative and retrieval models in this setting. We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions. In particular, we show that grounding on the details of the local environment, including location descriptions, and the objects (and their affordances) and characters (and their previous actions) present within it allows better predictions of agent behavior and dialogue. We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.

Never thought that artificial intelligence (AI) can save our planet? Disaster response, smart farming, and pollution control are only a few ways environmental scientists are saving the earth with AI. When we think of the innovative technology AI, the first and the foremost thing that pops up in our mind might be robots. Though robots are one of the applications of AI, there are many other applications that increasingly have the potential to help humankind. One such AI application area where the technology shines through is in the field of environmental services.

Artificial intelligence (AI) is a technology that exhibits behavior that could be interpreted as human intelligence. Can we apply artificial intelligence in agriculture? Can a computer be better than man in making decisions related to other living organisms in a complex environment? Can an algorithm beat farmer's gut instinct and experience? In recent years, agriculture has gone through a major revolution.