The agricultural decision-making process is experience-based, knowledge-dependent, time-sensitive, complex, and driven by historical data. Planting, fertilization, irrigation, and chemigation are key categories in farm decision-making, and currently, there is no one-shot decision-support tool that covers all these activities. Generative Artificial Intelligence (AI) models are more advanced than traditional machine learning and deep learning models. These models have been trained on vast amounts of data from the internet, allowing them to accept unstructured data in various forms and generate human-like text, solutions to problems, and scenario predictions. Given this capability, we became interested in exploring the potential of generative AI in agricultural decision-making. We designed a study to evaluate how well these models can make management decisions in a row crop production environment with humans in the loop. The study began in March 2024 on sprinkler corn plots in North Platte, NE, managed by the TAPS (Testing AG Performance Solutions) program at the University of Nebraska-Lincoln. We evaluated the ChatGPT-4o generative AI model, developed by OpenAI, in terms of its ability to generate decisions for seed selection, cover crop termination, fertigation, irrigation, and chemigation in real time. The model's input included unstructured past management decisions from the TAPS program, 2024 pre-plant soil health lab reports, farm management decision request emails from the TAPS program manager throughout the growing season, and the latest sensor-based weather and soil water content data relevant to each decision category. We found that the decisions made by ChatGPT-4o were faster than human decision-making and were logical, practical, and executable. Notably, the plots managed by AI ranked number 8 in yield and 13th by agronomic efficiency among all 31 plots managed by experienced growers. These findings are promising and suggest that generative AI models can build generalized farm decision-support tools for row crop production.

Highlights This study presents a seven-year operational review of a large-scale cable-suspended phenotyping system. Standardized operation and data protocols enable the delivery of high-resolution, high-frequency phenotypic datasets. The platform supports a wide range of phenotyping applications, from morphological to physiological trait analysis. The system plays a critical role in developing advanced sensing technology for phenotyping and precision agriculture. ABSTRACT. High-throughput plant phenotyping (HTPP) significantly improves plant phenotyping efficiency by integrating advanced sensing technologies, data processing, and modeling techniques. Over the last two decades, field-based HTPP systems have evolved from handheld sensors to sophisticated robotic platforms. Large-scale, ground-based phenotyping facilities have made significant contributions to advancing technology through their high sensor payloads, proximal measurement capabilities, and unmatched spatial and temporal resolution. This review paper presents a detailed and quantitative analysis of the operational performance of the cable-suspended NU-Spidercam phenotyping facility at the University of Nebraska–Lincoln from 2017 to 2023, focusing on daily operations, data management strategies, and system maintenance. Additionally, the paper systematically summarizes representative studies performed at the facility across morphological, spectral, and physiological phenotyping domains. Finally, the discussion highlights future directions, emphasizing the NU-Spidercam’s role in validating mobile phenotyping platforms, enabling precision agriculture research, supporting fundamental remote sensing studies, and facilitating the transfer of advanced sensing techniques to affordable, mobile platforms such as drones. Keywords: Artificial intelligence, Computer vision, Deep learning, Field plant phenotyping, Large-scale facility, Machine learning, Operational review, Physiological phenotyping.

Measuring flame, air, and soil temperatures during wildland fires, including wildfires and controlled burns in land management contexts, is crucial for research and applications in fire ecology, safety, and management in a wide array of ecosystems, from grasslands to forests. However, open-source and commercial systems are needed for measuring and logging flame and air temperatures that are user-friendly, economical, modular, and customizable. This paper details the design, development, and validation of the FireLog system. Laboratory validation experiments demonstrated high measurement accuracy, with a minimum coefficient of determination (R2) of 0.98 and the highest observed root mean square error (RMSE) of 29.5 °C when compared with a Campbell Scientific data logger in furnace tests spanning 20-1000 °C. These results confirm the FireLog system's precision, repeatability, and robustness under controlled conditions. Field deployment during prescribed burns further validated its operational performance, confirming its ability to record temperature dynamics reliably in active fire environments. FireLog represents a practical and scalable tool for researchers and practitioners in fire science and land management.

Current agricultural data management and analysis paradigms are to large extent traditional, in which data collecting, curating, integration, loading, storing, sharing and analyzing still involve too much human effort and know-how. The experts, researchers and the farm operators need to understand the data and the whole process of data management pipeline to make fully use of the data. The essential problem of the traditional paradigm is the lack of a layer of orchestrational intelligence which can understand, organize and coordinate the data processing utilities to maximize data management and analysis outcome. The emerging reasoning and tool mastering abilities of large language models (LLM) make it a potentially good fit to this position, which helps a shift from the traditional user-driven paradigm to AI-driven paradigm. In this paper, we propose and explore the idea of a LLM based copilot for autonomous agricultural data management and analysis. Based on our previously developed platform of Agricultural Data Management and Analytics (ADMA), we build a proof-of-concept multi-agent system called ADMA Copilot, which can understand user's intent, makes plans for data processing pipeline and accomplishes tasks automatically, in which three agents: a LLM based controller, an input formatter and an output formatter collaborate together. Different from existing LLM based solutions, by defining a meta-program graph, our work decouples control flow and data flow to enhance the predictability of the behaviour of the agents. Experiments demonstrates the intelligence, autonomy, efficacy, efficiency, extensibility, flexibility and privacy of our system. Comparison is also made between ours and existing systems to show the superiority and potential of our system.

Effective weed management is a significant challenge in agronomic crops which necessitates innovative solutions to reduce negative environmental impacts and minimize crop damage. Traditional methods often rely on indiscriminate herbicide application, which lacks precision and sustainability. To address this critical need, this study demonstrated an AI-enabled robotic system, Weeding robot, designed for targeted weed management. Palmer amaranth (Amaranthus palmeri S. Watson) was selected as it is the most troublesome weed in Nebraska. We developed the full stack (vision, hardware, software, robotic platform, and AI model) for precision spraying using YOLOv7, a state-of-the-art object detection deep learning technique. The Weeding robot achieved an average of 60.4% precision and 62% recall in real-time weed identification and spot spraying with the developed gantry-based sprayer system. The Weeding robot successfully identified Palmer amaranth across diverse growth stages in controlled outdoor conditions. This study demonstrates the potential of AI-enabled robotic systems for targeted weed management, offering a more precise and sustainable alternative to traditional herbicide application methods.

As the world’s population grows and the demand for food rises, more attention has been paid to increase crop yields and enhance global food security. Modern remote sensing technologies enable us to capture spectral features (such as NDVI) of crop canopy, which are widely used to assess crop growth, health, and stress conditions. We noticed in the literature that crop NDVI shows short-term variation within a day. Therefore, in this study, we leveraged the Spidercam Field Phenotyping Facility at University of Nebraska-Lincoln to measure and quantify the diurnal variation of canopy NDVI for corn and soybean crops. The experiments and data collection were conducted in 2022 and 2023, with canopy reflectance measured by a spectrometer (400-1000 nm) at multiple days covering different growth stages. In each day, measurements were taken at multiple time points within a time window ±3 hours centered around solar noon. Our analysis showed a clear concave-shaped, diurnal trend in NDVI for both crops, with the lowest NDVI at solar noon. More analyses will be performed to quantify this diurnal pattern and dissect the sources of variation due to solar angle and change in canopy morphology. This research will further improve the accuracy and relevance of NDVI in plant phenotyping and many other scientific disciplines and applications.

Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing the full utilization of imaging sensors. This paper reported AICropCAM, a field-deployable imaging framework that integrated edge image processing, Internet of Things (IoT), and LoRaWAN for low-power, long-range communication. The core component of AICropCAM is a stack of four Deep Convolutional Neural Networks (DCNN) models running sequentially: CropClassiNet for crop type classification, CanopySegNet for canopy cover quantification, PlantCountNet for plant and weed counting, and InsectNet for insect identification. These DCNN models were trained and tested with >43,000 field crop images collected offline. AICropCAM was embodied on a distributed wireless sensor network with its sensor node consisting of an RGB camera for image acquisition, a Raspberry Pi 4B single-board computer for edge image processing, and an Arduino MKR1310 for LoRa communication and power management. Our testing showed that the time to run the DCNN models ranged from 0.20 s for InsectNet to 20.20 s for CanopySegNet, and power consumption ranged from 3.68 W for InsectNet to 5.83 W for CanopySegNet. The classification model CropClassiNet reported 94.5 % accuracy, and the segmentation model CanopySegNet reported 92.83 % accuracy. The two object detection models PlantCountNet and InsectNet reported mean average precision of 0.69 and 0.02 for the test images. Predictions from the DCNN models were transmitted to the ThingSpeak IoT platform for visualization and analytics. We concluded that AICropCAM successfully implemented image processing on the edge, drastically reduced the amount of data being transmitted, and could satisfy the real-time need for decision-making in PA. AICropCAM can be deployed on moving platforms such as center pivots or drones to increase its spatial coverage and resolution to support crop monitoring and field operations.

We propose a solution with edge image processing and long-range connectivity named AICropCAM that can be used in drones, ground platforms, or as distributed sensor networks for plant phenotyping. We have successfully run multiple image classification, segmentation, and object detection models on this platform. Classification models help classify images based on image quality, crop type, and phenological stage. Object detection models could detect and count the number of plants, weeds, and insects and expand to count the flowers, fruits, and leaves. Segmentation models can separate the canopy from the background and potentially segment traits that indicate the nutrient deficit or disease. Canopy segmentation results help estimate leaf area index and chlorophyll content. Because the models run sequentially, like a decision tree, there is flexibility to select the most accurate model considering the crop type and the crop’s phenological stage that helps scan fields with multiple crops. The generated information is geo-tagged and transmitted through low throughput long-range communication protocol (e.g., LoRa) to cloud data storage. AICropCAM reduces 2-megabyte image files to around 100-byte actionable data, resulting in massive savings in data storage and transmission costs. This edge image capturing and processing system is open to improvement with new neural network predictive models and faster edge computers. This system provides plant scientists and crop breeders a low-cost, flexible phenotyping tool to extract multiple crop traits related to abiotic and biotic stress responses.

As the global population continues to increase, the demand for food production rises accordingly. The water availability of crops has a significant impact on their yield during the processes of photosynthesis and transpiration. Crops exchange carbon dioxide and water with the atmosphere through stomata. When crops undergo water stress, they tend to close their stomata to reduce water loss. However, this can also negatively affect the crop's photosynthetic rate and carbon assimilation, leading to low yields. Stomatal conductance (SC) quantifies the rate of gas exchange between crops and the atmosphere and can inform the crop's water status. SC measurements require the use of contact-type instruments, which is time-consuming and labor-intensive. This study examined the accuracy of multiple linear regression (MLR), support vector regression (SVR), and convolutional neural network (CNN) models for SC estimation in corn and soybean using RGB, near-infrared, and thermal-infrared images from a field phenotyping platform. The results show that the CNN model outperformed other two models, with R 2 value of 0.52. Furthermore, adding soil moisture as a variable to the model improved its accuracy, decreasing model RMSE from 0.147 to 0.137 mol/(m2*s). This study highlights the potential of estimating SC from remote sensing platforms to help growers obtain information about their crop water status and plan irrigation more effectively.

CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task. OBJECTIVE: Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, trans-mission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring.METHODS: It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms.RESULTS AND CONCLUSION: The result showed that 33 variables measured by various sensors were demon-strated in these studies while 10 actuations were successfully integrated with Ag-IoT platforms. Perennial crops, which introduced less disturbance to Ag-IoT platforms than annual crops, were selected by 64% of researchers. Furthermore, studies in Ag-IoT system development were more focused on outdoor than indoor environments. Ag-IoT systems based on Arduino were most common among the studies while commercial platforms were least adopted, likely due to their inflexibility in customized developments. More researchers focused on agricultural applications than the IoT technology itself. Soil water content-based irrigation scheduling and controlled envi-ronment monitoring and controlling were the main applications. Other application areas included soil nutrient estimation, crop monitoring based on multiple vegetation indices, pest identification, and chemigation. SIGNIFICANCE: Several potential future research directions were identified at the end of the review, including integration of satellite-based internet connectivity to improve the IoT networks in non-connected farms, development of mobile IoT platforms (drones and autonomous ground vehicles) with continuous connectivity, and the use of edge-computing and machine-learning/deep-learning to enhance the capability of the Ag-IoT systems.

Stomatal conductance (SC) was utilized to indicate the rate of gas and water exchange through stomata on the leaf surface. When crops are experiencing water stress during the daytime, their stomata will close to prevent water loss. However, the crop will also receive less CO2 for photosynthesis under this condition. Consequently, accurate and efficient SC prediction can improve irrigation efficiency, particularly in the present day when water is scarce. The common way to estimate SC nowadays is to make predictions using other variables that can be measured quickly, such as conventional regression analysis. The limitation of conventional regression analysis is that it does not account for changes in SC when crops are subjected to both prolonged drought and high temperature stress. We intend to use time series prediction techniques, such as Long Short-Term Memory (LSTM), to determine the correlation between variables at different time scales. The objective of this study is to (1) Investigating the relationship between SC and persistent weather patterns. (2) Predict SC based on continuously collected soil moisture, plant canopy temperature and weather information. In this study, we measured the SC of soybean and maize by using a handheld leaf porometer. This was then compared to short-term historical crop SC predictions based on canopy temperature, soil moisture, and weather conditions. Autoregressive Integrated Moving Average (ARIMA) and LSTM based on Recurrent neural network (RNN) are used to predict SC, and the results are compared with the conventional modeling method. More details will be presented at the NAPPN conference.

While information about crops can be derived from many different modalities including hyperspectral imaging, multispectral imaging, fluorescence imaging, 3D laser scanning, etc. low-cost RGB imaging sensors in continuous monitoring of crops is a more practical and feasible alternative. In this research, an image processing pipeline was developed to monitor the growth of soybean crops in a research field of the University of Nebraska-Lincoln using their RGB images collected by overhead-phenocams within 30 days using Raspberry-Pi-Zero with a camera module where images were saved on an SD card. The images were stored in the JPG file format with 1920512 resolution, followed by a denoising step using a pretrained Denoising Deep Convolutional Neural Network (DCNN). Then, a semantic segmentation algorithm developed and named as SoySegNet was used to isolate the canopy of soybean crops from the background. A DeepLab v3+ DCNN was developed using the transfer learning technique based on the ResNet-18 DCNN, to perform the semantic segmentation. The semantic segmentation DCNN was trained with 119 pixel-labeled images and additional images generated using data augmentation techniques (i.e., random translation and reflection). The augmentation step increased the size of the image dataset used in the training, validation, and testing of the DCNN. The SoySegNet was able to identify soybean canopy with a pixel-level accuracy of 94%. Various vegetative indices (i.e., excess green index, excess green minus excess red, vegetative index, the color index of vegetation, visible atmospherically resistant index, red-green-blue vegetation index, modified green, red vegetation index, and normalized difference index) were computed using the segmented field images to monitor the growth rate of soybean crops. Furthermore, the proposed image processing pipeline was extended to count the soybean leaves in the segmented images using a deep neural network based on the You Only Look Once (YOLO) architecture and named as SoyCountNet. The SoyCountNet was trained with the same 119 labeled images used for SoySegNet, where the leaves were labeled using bounding boxes. Again, data augmentation techniques were used to increase the size of the training, validation, and testing data sets. The SoyCountNet consisted of ResNet50 DCNN as a feature extraction network and an object detection subnetwork. The SoyCountNet was able to count soybean leaves with a 0.36 precision in the field-segmented images of the soybean crops. This research demonstrated that the proposed image processing pipeline in conjunction with low-cost RGB imaging devices could provide a reliable and cost-effective framework for continuous crop monitoring. Novel application of this framework would be to generate meaningful data about the crop in real-time in edge computing devices of Low Power Wide Area Network (LPWAN) based agricultural Internet of Things (IoT) sensor networks.

Journal of Food and Agriculture is a half yearly publication by the Faculty of Agriculture and Plantation Management and Faculty of Livestock fisheries and Nutrition of the Wayamba University of Sri Lanka which provides a valuable forum for scientists endeavoring in research and development aspects in agriculture, food and nutrition. Full text articles available.

Precision agriculture is combined with irrigation control systems to observe the environment and respond accordingly. In this paper, we discussed developmental procedure of a novel irrigation control system called Sensor Based Self-powered Smart Irrigation Control System (SSSICS). In the current market, there are several irrigation controllers available and they are either timer based open loop (OL) or sensor-based closed loop (CL) irrigation controllers. Each method has different advantages and disadvantages, and SSSICS contains OL and CL control techniques to get rid of disadvantages. We used OL control technique to work in the Timer Mode(TM) and CL control technique to work in the Intelligent Mode (IM). We introduced a new graphical user interface (GUI) as a human-machine interface (HMI). This HMI and available inputs facilitated the farmer to set precise environmental parameters required for the crop to give maximum yield. For remote farm fields, it is necessary to have a remote communication (RC) and uninterrupted power supply. Text messages that receive and send via Global System for Mobile (GSM) Communication allow the farmer/user to perform RC with the control system (CS) of the SSSICS. We designed a Micro Hydro Power Generator (MHPG) array and solar system including rechargeable batteries to give the power required to the SSSICS. The SSSICS was tested in real environment and it was demonstrated that hybrid power generation concept by incorporating open loop and closed loop control techniques works properly in the hardware platform developed for the study.