We introduce a domain-specific reward mechanism that maximizes yield recovery while minimizing chemical usage by effectively handling noisy infection data and enforcing physical field constraints via action masking. We conduct a rigorous empirical evaluation across diverse, realistic biotic stress scenarios, capturing varying infection distributions and severity levels in row-crop fields. The proposed scheme is evaluated thoroughly, showing the framework's effectiveness and robustness. Experimental results demonstrate that our approach significantly reduces non-target spraying, chemical consumption, and operational costs compared to baseline methods. Optimizing Navigation And Chemical Application in Precision Agriculture With Deep Reinforcement Learning And Conditional Action Tree Mahsa Khosravi a, Zhanhong Jiang b, Joshua R Waite b, Sarah Jones c, Hernan Torres c, Arti Singh c, Baskar Ganapathysubramanian b, Asheesh Kumar Singh c, Soumik Sarkar b a Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, Iowa, USA b Department of Mechanical Engineering, Iowa State University, Ames, Iowa, USA c Department of Agronomy, Iowa State University, Ames, Iowa, USAAbstract This paper presents a novel reinforcement learning (RL)-based planning scheme for optimized robotic management of biotic stresses in precision agriculture.

Efficient crop production requires early detection of pest outbreaks and timely treatments; we consider a solution based on a fleet of multiple autonomous miniaturized unmanned aerial vehicles (nano-UAVs) to visually detect pests and a single slower heavy vehicle that visits the detected outbreaks to deliver treatments. To cope with the extreme limitations aboard nano-UAVs, e.g., low-resolution sensors and sub-100 mW computational power budget, we design, fine-tune, and optimize a tiny image-based convolutional neural network (CNN) for pest detection. Despite the small size of our CNN (i.e., 0.58 GOps/inference), on our dataset, it scores a mean average precision (mAP) of 0.79 in detecting harmful bugs, i.e., 14% lower mAP but 32x fewer operations than the best-performing CNN in the literature. Our CNN runs in real-time at 6.8 frame/s, requiring 33 mW on a GWT GAP9 System-on-Chip aboard a Crazyflie nano-UAV. Then, to cope with in-field unexpected obstacles, we leverage a global+local path planner based on the A* algorithm. The global path planner determines the best route for the nano-UAV to sweep the entire area, while the local one runs up to 50 Hz aboard our nano-UAV and prevents collision by adjusting the short-distance path. Finally, we demonstrate with in-simulator experiments that once a 25 nano-UAVs fleet has combed a 200x200 m vineyard, collected information can be used to plan the best path for the tractor, visiting all and only required hotspots. In this scenario, our efficient transportation system, compared to a traditional single-ground vehicle performing both inspection and treatment, can save up to 20 h working time.

With the help of a digital twin structure, Agriculture 4.0 technologies like weather APIs (Application programming interface), GPS (Global Positioning System) modules, and NPK (Nitrogen, Phosphorus and Potassium) soil sensors and machine learning recommendation models, we seek to revolutionize agricultural production through this concept. In addition to providing precise crop growth forecasts, the combination of real-time data on soil composition, meteorological dynamics, and geographic coordinates aims to support crop recommendation models and simulate predictive scenarios for improved water and pesticide management.

Global climate change has reduced crop resilience and pesticide efficacy, making reliance on synthetic pesticides inevitable, even though their widespread use poses significant health and environmental risks. While these pesticides remain a key tool in pest management, previous machine-learning applications in pesticide and agriculture have focused on classification or regression, leaving the fundamental challenge of generating new molecular structures or designing novel candidates unaddressed. In this paper, we propose Pesti-Gen, a novel generative model based on variational auto-encoders, designed to create pesticide candidates with optimized properties for the first time. Specifically, Pesti-Gen leverages a two-stage learning process: an initial pre-training phase that captures a generalized chemical structure representation, followed by a fine-tuning stage that incorporates toxicity-specific information. The model simultaneously optimizes over multiple toxicity metrics, such as (1) livestock toxicity and (2) aqua toxicity to generate environmentally friendly pesticide candidates. Notably, Pesti-Gen achieves approximately 68\% structural validity in generating new molecular structures, demonstrating the model's effectiveness in producing optimized and feasible pesticide candidates, thereby providing a new way for safer and more sustainable pest management solutions.

A very crucial part of Bangladeshi people's employment, GDP contribution, and mainly livelihood is agriculture. It plays a vital role in decreasing poverty and ensuring food security. Plant diseases are a serious stumbling block in agricultural production in Bangladesh. At times, humans can't detect the disease from an infected leaf with the naked eye. Using inorganic chemicals or pesticides in plants when it's too late leads in vain most of the time, deposing all the previous labor. The deep-learning technique of leaf-based image classification, which has shown impressive results, can make the work of recognizing and classifying all diseases trouble-less and more precise. In this paper, we've mainly proposed a better model for the detection of leaf diseases. Our proposed paper includes the collection of data on three different kinds of crops: bell peppers, tomatoes, and potatoes. For training and testing the proposed CNN model, the plant leaf disease dataset collected from Kaggle is used, which has 17,430 images. The images are labeled with 14 separate classes of damage. The developed CNN model performs efficiently and could successfully detect and classify the tested diseases. The proposed CNN model may have great potency in crop disease management.

This paper presents OG-RAG, an Ontology-Grounded Retrieval Augmented Generation method designed to enhance LLM-generated responses by anchoring retrieval processes in domain-specific ontologies. While LLMs are widely used for tasks like question answering and search, they struggle to adapt to specialized knowledge, such as industrial workflows or knowledge work, without expensive fine-tuning or sub-optimal retrieval methods. Existing retrieval-augmented models, such as RAG, offer improvements but fail to account for structured domain knowledge, leading to suboptimal context generation. Ontologies, which conceptually organize domain knowledge by defining entities and their interrelationships, offer a structured representation to address this gap. OG-RAG constructs a hypergraph representation of domain documents, where each hyperedge encapsulates clusters of factual knowledge grounded using domain-specific ontology. An optimization algorithm then retrieves the minimal set of hyperedges that constructs a precise, conceptually grounded context for the LLM. This method enables efficient retrieval while preserving the complex relationships between entities. OG-RAG applies to domains where fact-based reasoning is essential, particularly in tasks that require workflows or decision-making steps to follow predefined rules and procedures. These include industrial workflows in healthcare, legal, and agricultural sectors, as well as knowledge-driven tasks such as news journalism, investigative research, consulting and more. Our evaluations demonstrate that OG-RAG increases the recall of accurate facts by 55% and improves response correctness by 40% across four different LLMs. Additionally, OG-RAG enables 30% faster attribution of responses to context and boosts fact-based reasoning accuracy by 27% compared to baseline methods.

In a windowless shack on the far outskirts of Fresno, an ominious red glow illuminates a lab filled with X-ray machines, shelves of glowing boxes, a quietly humming incubator and a miniature wind tunnel. While the scene looks like something straight out of a sci-fi movie, its actually part of an experimental program to prevent a damaging almond pest from successfully mating. With California almond growers reeling from dropping nut prices and rising costs, the pests have only added to their woes. Every year, the navel orangeworm eats through roughly 2% of California's almonds before they can make it to grocery store shelves. Last year, it was almost double that.

Automated persistent and fine-grained monitoring of orchards at the individual tree or fruit level helps maximize crop yield and optimize resources such as water, fertilizers, and pesticides while preventing agricultural waste. Towards this goal, we present a 4D spatio-temporal metric-semantic mapping method that fuses data from multiple sensors, including LiDAR, RGB camera, and IMU, to monitor the fruits in an orchard across their growth season. A LiDAR-RGB fusion module is designed for 3D fruit tracking and localization, which first segments fruits using a deep neural network and then tracks them using the Hungarian Assignment algorithm. Additionally, the 4D data association module aligns data from different growth stages into a common reference frame and tracks fruits spatio-temporally, providing information such as fruit counts, sizes, and positions. We demonstrate our method's accuracy in 4D metric-semantic mapping using data collected from a real orchard under natural, uncontrolled conditions with seasonal variations. We achieve a 3.1 percent error in total fruit count estimation for over 1790 fruits across 60 apple trees, along with accurate size estimation results with a mean error of 1.1 cm. The datasets, consisting of LiDAR, RGB, and IMU data of five fruit species captured across their growth seasons, along with corresponding ground truth data, will be made publicly available at: https://4d-metric-semantic-mapping.org/

A robot dog equipped with a flamethrower could be used to stop weeds growing on farms, potentially offering a replacement for harmful herbicides. Even highly targeted herbicides can cause environmental problems, affecting local wildlife, and "superweeds" are quickly evolving resistance to the most common weed-killers like glyphosate. In search of an alternative solution, Dezhen Song at Texas A&M University and his colleagues have developed a weed control system that uses a brief burst of heat from a propane-powered flamethrower controlled by a robotic arm, attached to a Spot robot manufactured by Boston Dynamics. Rather than incinerate the weeds, the robot is designed to identify and heat up the centre of the plant, which can stop it growing for several weeks, says Song. "The weeds don't die – you just suppress their growth so it gives your crop a chance to fight the weed." Song and his team first tested the flame nozzle to make sure they could accurately target the weeds' centre.

Research in the food domain is at times limited due to data sharing obstacles, such as data ownership, privacy requirements, and regulations. While important, these obstacles can restrict data-driven methods such as machine learning. Federated learning, the approach of training models on locally kept data and only sharing the learned parameters, is a potential technique to alleviate data sharing obstacles. This systematic review investigates the use of federated learning within the food domain, structures included papers in a federated learning framework, highlights knowledge gaps, and discusses potential applications. A total of 41 papers were included in the review. The current applications include solutions to water and milk quality assessment, cybersecurity of water processing, pesticide residue risk analysis, weed detection, and fraud detection, focusing on centralized horizontal federated learning. One of the gaps found was the lack of vertical or transfer federated learning and decentralized architectures.