Site-specific disease management (SSDM) in crops has advanced rapidly through machine and deep learning (ML and DL) for real-time computer vision. Research evolved from handcrafted feature extraction to large-scale automated feature learning. With foundation models (FMs), crop disease datasets are now processed in fundamentally new ways. Unlike traditional neural networks, FMs integrate visual and textual data, interpret symptoms in text, reason about symptom-management relationships, and support interactive QA for growers and educators. Adaptive and imitation learning in robotics further enables field-based disease management. This review screened approx. 40 articles on FM applications for SSDM, focusing on large-language models (LLMs) and vision-language models (VLMs), and discussing their role in adaptive learning (AL), reinforcement learning (RL), and digital twin frameworks for targeted spraying. Key findings: (a) FMs are gaining traction with surging literature in 2023-24; (b) VLMs outpace LLMs, with a 5-10x increase in publications; (c) RL and AL are still nascent for smart spraying; (d) digital twins with RL can simulate targeted spraying virtually; (e) addressing the sim-to-real gap is critical for real-world deployment; (f) human-robot collaboration remains limited, especially in human-in-the-loop approaches where robots detect early symptoms and humans validate uncertain cases; (g) multi-modal FMs with real-time feedback will drive next-gen SSDM. For updates, resources, and contributions, visit, https://github.com/nitin-dominic/AgriPathogenDatabase, to submit papers, code, or datasets.

India, as a predominantly agrarian economy, faces significant challenges in agriculture, including substantial crop losses caused by diseases, pests, and environmental stress. Early detection and accurate identification of diseases across different crops are critical for improving yield and ensuring food security. This paper proposes a deep learning based solution for detecting multiple diseases in multiple crops, aimed to cover India's diverse agricultural landscape. We first create a unified dataset encompassing images of 17 different crops and 34 different diseases from various available repositories. Proposed deep learning model is trained on this dataset and outperforms the state-of-the-art in terms of accuracy and the number of crops, diseases covered. We achieve a significant detection accuracy, i.e., 99 percent for our unified dataset which is 7 percent more when compared to state-of-the-art handling 14 crops and 26 different diseases only. By improving the number of crops and types of diseases that can be detected, proposed solution aims to provide a better product for Indian farmers.

While conversational generative AI has shown considerable potential in enhancing decision-making for agricultural professionals, its exploration has predominantly been anchored in text-based interactions. The evolution of multimodal conversational AI, leveraging vast amounts of image-text data from diverse sources, marks a significant stride forward. However, the application of such advanced vision-language models in the agricultural domain, particularly for crop disease diagnosis, remains underexplored. In this work, we present the crop disease domain multimodal (CDDM) dataset, a pioneering resource designed to advance the field of agricultural research through the application of multimodal learning techniques. The dataset comprises 137,000 images of various crop diseases, accompanied by 1 million question-answer pairs that span a broad spectrum of agricultural knowledge, from disease identification to management practices. By integrating visual and textual data, CDDM facilitates the development of sophisticated question-answering systems capable of providing precise, useful advice to farmers and agricultural professionals. We demonstrate the utility of the dataset by finetuning state-of-the-art multimodal models, showcasing significant improvements in crop disease diagnosis. Specifically, we employed a novel finetuning strategy that utilizes low-rank adaptation (LoRA) to finetune the visual encoder, adapter and language model simultaneously. Our contributions include not only the dataset but also a finetuning strategy and a benchmark to stimulate further research in agricultural technology, aiming to bridge the gap between advanced AI techniques and practical agricultural applications. The dataset is available at https: //github.com/UnicomAI/UnicomBenchmark/tree/main/CDDMBench.

The development of an intelligent agricultural decision-supporting system for crop selection and disease forecasting in Bangladesh is the main objective of this work. The economy of the nation depends heavily on agriculture. However, choosing crops with better production rates and efficiently controlling crop disease are obstacles that farmers have to face. These issues are addressed in this research by utilizing machine learning methods and real-world datasets. The recommended approach uses a variety of datasets on the production of crops, soil conditions, agro-meteorological regions, crop disease, and meteorological factors. These datasets offer insightful information on disease trends, soil nutrition demand of crops, and agricultural production history. By incorporating this knowledge, the model first recommends the list of primarily selected crops based on the soil nutrition of a particular user location. Then the predictions of meteorological variables like temperature, rainfall, and humidity are made using SARIMAX models. These weather predictions are then used to forecast the possibilities of diseases for the primary crops list by utilizing the support vector classifier. Finally, the developed model makes use of the decision tree regression model to forecast crop yield and provides a final crop list along with associated possible disease forecast. Utilizing the outcome of the model, farmers may choose the best productive crops as well as prevent crop diseases and reduce output losses by taking preventive actions. Consequently, planning and decision-making processes are supported and farmers can predict possible crop yields. Overall, by offering a detailed decision support system for crop selection and disease prediction, this work can play a vital role in advancing agricultural practices in Bangladesh.

Lingga Madu dreams that one day his company will design and sell fashionable clothes at a price everyone can afford. Born of past experience, it is a business vision very much focused on embracing the future. Having grown up in Jogja – where the average daily wage of US$3 means it ranks as one of Indonesia's poorest cities – the 32-year-old software engineer recalled how the latest fashions were beyond the reach of most people. That memory stayed with him and in 2014 Madu founded Sale Stock, a Jakarta-based e-commerce company where the guiding principle is to make more by charging less. A moneymaking marriage between fashion design and artificial intelligence (AI) allowing the latest AI technology to mine and analyse market data and customer behaviour to a level beyond human capability, thereby identifying designs that will sell and tailoring production accordingly.

"AI is the new Electricity" – Andrew Ng* Since the advent of 20th century electricity became the main source of invention in every major industry ranging from transportation, manufacturing to healthcare, communications and many more. Today Artificial Intelligence (AI) is bringing the same big transformation across all the major industries. The part of AI that is rapidly growing and which is driving most of these transformations is Deep Learning. Today, Deep Learning has become one of the most sought after skills in the technology world. Agriculture is one industry where Deep Learning scientists and researchers are working with farmers to help them with their produce.

Born of research in the Amazon forest, the Plantix mobile app is helping farmers on three continents quickly identify plant diseases using artificial intelligence. For several years in the Brazilian rain forest, a team of young German researchers studied the emission and mitigation of greenhouse gases due to changing land use. The team's analysis was yielding new knowledge, but the farmers they worked with weren't interested in those findings. They wanted to know how to treat crops being ravaged by pathogens. "They couldn't understand why we can estimate the carbon stock of their soil, but we couldn't give them an idea of how to treat damaged plants in an appropriate way," said Robert Strey, one of the researchers.

Food security is threatened by many things. In some regions, climate variability causes droughts that make vital resources scarce. In others, political turmoil creates logistical blockades for farming, harvesting, and shipping produce. But, practically everywhere, plant disease can wipe out entire crops with little warning. A team of researchers at Pennsylvania State University and the École Polytechnique Fédérale de Lausanne, Switzerland have turned the keen eye of artificial intelligence toward agriculture, using deep learning algorithms to help detect crop disease before it spreads.

In a world with more mobile phones than flush toilets, digital devices are now standard equipment among even the world's poorest and most remote people. Farmers in these areas are getting tools for their devices that help deliver water, nutrients, and medicine to plants as needed; test for crop diseases and malnourishment; and survey their soil for future planning. In some cases, these emerging apps are the biggest new technologies resource-poor farms have seen in hundreds of years. That is not very surprising to Rajiv "Raj" Khosla, professor of Precision Agriculture at the College of Agricultural Sciences of Colorado State University. "What we're finding is that many small-scale farmers in resource-poor environments are still farming in the 1500s. They're looking for leapfrog technologies," he said.