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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]

Quick and accurate quantification of lake water quality (WQ) is essential for its management and improvement. Use of geotechnology (remote sensing, GIS, and GPS) applications is a step forward in improving our ability to effectively quantify and manage the WQ of ungauged lakes. Beaver Reservoir, a drinking water source for over 280,000 people in northwest Arkansas, is facing increased chlorophyll-a (Chl-a) and suspended matter (SM) content in the lake. This study is designed to qualitatively predict the Chl-a and SM content in the lake on a spatial basis from Landsat-TM image digital information. A Learning Vector Quantization (LVQ) classification neural model was used to predict the qualitative (oligotrophic, moderately oligotrophic, and mildly trophic) classes for several spatial positions in the lake. The geostatistical tool in ArcGIS was used to spatially map the Chl-a and SM extent around the lake. The LVQ classification model predicted the Chl-a extent with more than 90% accuracy having only one point misclassified out of 14 testing points. The LVQ model prediction for SM resulted in four misclassified points out of 14 testing points with prediction accuracy of 72%. The final spatial zoning maps for Chl-a and SM extent in the entire lake could be used to help water use managers and end users design management strategies, and also demonstrates a relatively low cost WQ prediction mechanism that can be applied in developing countries and elsewhere when detailed in-situ monitoring is not feasible. [ABSTRACT FROM AUTHOR]

In the present work, an attempt has been made to use different artificial neural network (ANN) architectures to achieve more accurate prediction of drill wear. Large numbers of drilling operations, using mild steel as the work-piece and high speed steel (HSS) as the drill, have been performed and drill flank wear has been measured intermittently. Experimental results show a strong dependency of direct and indirect process parameters with drill wear. Experimentally obtained data have been used to train different ANN architectures using different combinations of important process parameters as input and measured flank wear as the output of the network. Relative performances of different ANN based drill wear prediction schemes in regard to prediction of drill wear have been compared. From the present work it has been observed that inclusions of more sensor signals as input to the network results a better-trained network, which can predict wear more accurately. It has also been observed from the present work that standard back propagation neural network (BPNN) predicts wear more accurately compared to fuzzy back propagation network (FBPN) and self-organizing method (SOM), through BPNN is slow in convergence. [ABSTRACT FROM AUTHOR]