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The increasing use of convolutional neural networks (CNNs) has brought about a significant transformation in numerous fields, such as image categorization and identification. In the development of a CNN model to classify images of sericea lespedeza [SL; Lespedeza cuneata (Dum-Cours) G. Don] from weed images, four architectures were explored: CNN model variant 1, CNN model variant 2, the Visual Geometry Group (VGG16) model, and ResNet50. CNN model variant 1 (batch normalization with adjusted dropout method) demonstrated 100% validation accuracy, while variant 2 (RMSprop optimization with adjusted learning rate) achieved 90.78% validation accuracy. Pre-trained models, like VGG16 and ResNet50, were also analyzed. In contrast, ResNet50's steady learning pattern indicated the potential for better generalization. A detailed evaluation of these models revealed that variant 1 achieved a perfect score in precision, recall, and F1 score, indicating superior optimization and feature utilization. Variant 2 presented a balanced performance, with metrics between 86% and 93%. VGG16 mirrored the behavior of variant 2, both maintaining around 90% accuracy. In contrast, ResNet50's results revealed a conservative approach for class 0 predictions. Overall, variant 1 stood out in performance, while both variant 2 and VGG16 showed balanced results. The reliability of CNN model variant 1 was highlighted by the significant accuracy percentages, suggesting potential for practical implementation in agriculture. In addition to the above, a smartphone application for the identification of SL in a field-based trial showed promising results with an accuracy of 98–99%. The conclusion from the above is that a CNN model with batch normalization has the potential to play a crucial role in the future in redefining and optimizing the management of undesirable vegetation. [ABSTRACT FROM AUTHOR]

Livestock management is challenging for resource-poor (R-P) farmers due to unavailability of quality feed, limited professional advice, and rumor-spreading about animal health condition in a herd. This research seeks to improve animal health in southern Africa by promoting sericea lespedeza (Lespedeza cuneata), a nutraceutical forage legume. An automated geospatial model for precision agriculture (PA) can identify suitable locations for its cultivation. Additionally, a novel approach of radio-frequency identifier (RFID) supported telemetry technology can track animal movement, and the analyses of data using artificial intelligence can determine sickness of small ruminants. This RFID-based system is being connected to a smartphone app (under construction) to alert farmers of potential livestock health issues in real time so they can take immediate corrective measures. An accompanying Decision Support System (DSS) site is being developed for R-P farmers to obtain all possible support on livestock production, including the designed PA and RFID-based DSS. [ABSTRACT FROM AUTHOR]

Lack of attention to rural healthcare for livestock in the southeastern United States has led to a focus on small ruminant farming, mainly using sericea lespedeza [SL; Lespedeza cuneata (Dum-Cours) G. Don], a drought-resistant forage species with nutraceutical benefits. Climate change has increased land availability for SL cultivation, further expanding the potential of this bioactive (anti-parasitic) legume. This study aims to create a geospatial engineering and technology-assisted model for identifying suitable SL production areas for supporting profitable small ruminant farming. The cultivation of SL depends on specific weather conditions and soil properties, with minimum requirements for temperature and rainfall, non-clay soil with reduced bulk density, and open land cover. The main objective was to develop an automated geospatial model using ArcGIS Pro ModelBuilder to assess SL production suitability. This model also aimed to identify appropriate locations for small ruminant production in Georgia in the southeastern United States, characterized by increasing temperature fluctuations. A web-based geographic information system (webGIS) platform was developed using the ArcGIS Online dashboard interface, allowing agriculturalists to access decision support for SL production suitability tailored to their land. This forage production suitability analysis, conducted in the context of climate change, offers valuable guidance for pasture managers in other nations with similar environmental attributes, promoting global adaptability and resilience. [ABSTRACT FROM AUTHOR]

Site-specific forage management (SSFM), comprising growth observation, impact assessment, and timely strategic response to small variations in sericea lespedeza (SL; Lespedeza cuneata (Dum-Cours.) G. Don) production, has been envisioned as a life-changing approach for resource-poor (R-P) farmers in developing countries, assisting in the effective rearing of their small ruminants. The application of geospatial technologies, including geographic information systems (GIS), remote sensing, global navigation satellite system, and information technology, can support SSFM but has not been widely used for site-specific forage management. From our previous studies, it appears that the entire range of condensed tannins of lespedeza, namely extractable condensed tannin (ECT), fiber-bound condensed tannin (FBCT), and protein-bound condensed tannin (PBCT), as well as crude protein (CP), are excellent for promoting small ruminant digestion and overall health. The goal of this study was to develop an SSFM strategy for SL to enhance animal production in areas of drought-prone, low pH, marginally infertile soils. To achieve this goal, study objectives were to: (i) develop statistical and artificial neural networks-based (ANN) models to identify if a sound correlation exists among forage growth environmental features and SL-ECT content; (ii) determine suitability criteria, including climate, soil, and land use/land cover (LULC), for mass scale production of SL and collect supporting environmental geospatial data; and (iii) develop an automated geospatial model for SL growth suitability analysis in relation to optimal areas for its production in a case-study location. Telemetric data and individual climatologic parameters (including minimum, maximum, and average temperature, humidity, dewpoint, soil temperatures at three depths, soil moisture, evapotranspiration, total solar radiation, and precipitation) were found to correlate well (>75%) with the forage production parameters, including values of SL-ECT from the Fort Valley State University (FVSU) research station in Georgia in the southern United States. A backpropagation neural network (BPNN) model was developed using similar climatic input parameters, along with elevation (topography) and a normalized difference vegetation index (NDVI) to estimate the forage's ECT with a testing root mean square error (RMSE) of 1.18%. With good correlation obtained between the climatic, soil, slope, and land cover input parameters, and SL-ECT as the output parameter, an SSFM model was developed with potential application to R-P farmers in areas suitable for SL establishment and growth. Eswatini (previously Swaziland), a landlocked country in southern Africa, in which numerous R-P small ruminant (sheep and goat) farmers reside, was used as the case study location to develop the SL production suitability model. Geospatial data were used for automated model development in an ArcGIS Pro ModelBuilder platform to provide information on where to grow SL efficiently to economically feed small ruminants. Land use/land cover, soil, topography, and climate based geospatial data of the region helped in the development of the automated SSFM geospatial model for spatial growth suitability location determination to assist farmers of Eswatini with their SL production decision making. This automated model can easily be replicated for farmers in other countries in Africa, as well as in other parts of the world having similar climatic conditions. [ABSTRACT FROM AUTHOR]