A ZigBee-Based Acquisition System for Agricultural Environment Information with Low Power and High Reliability

In order to guarantee the safety of products at modern agriculture, the whole factors of the environment of agricultural production process need to be monitored. That needs to collect a variety of environmental information. In this paper, based on ZigBee technology, a system for crop growth environment information and image information acquisition is implemented. System network scheme can reduce the interchannel interference and enhance ZigBee communication quality. In the process of transmission, the transmission of the image data uses a coding scheme based on historical data comparison. For the image transmission, an intelligent transmission scheme based on generating Feedback Report is proposed, which takes both the quality and efficiency into consideration. Based on these schemes, the system designed in this paper can realize efficient and reliable transmission of agricultural environmental information.

Forward-Backward Synergistic Acceleration Pursuit Algorithm Based on Compressed Sensing

We propose the Forward-Backward Synergistic Acceleration Pursuit (FBSAP) algorithm in this paper. The FBSAP algorithm inherits the advantages of the Forward-Backward Pursuit (FBP) algorithm, which has high success rate of reconstruction and does not necessitate the sparsity level as a priori condition. Moreover, it solves the problem of FBP that the atom can be selected only by the fixed step size. By mining the correlation between candidate atoms and residuals, we innovatively propose the forward acceleration strategy to adjust the forward step size adaptively and reduce the computation. Meanwhile, we accelerate the algorithm further in backward step by fusing the strategy proposed in Acceleration Forward-Backward Pursuit (AFBP) algorithm. The experimental simulation results demonstrate that FBSAP can greatly reduce the running time of the algorithm while guaranteeing the success rate in contrast to FBP and AFBP.

InstaCropNet:An efficient Unet-Based architecture for precise crop row detection in agricultural applications

Autonomous navigation in farmlands is one of the key technologies for achieving autonomous management in maize fields.Among various navigation techniques,visual navigation using widely available RGB images is a cost-effective solution.However,current mainstream methods for maize crop row detection often rely on highly specialized,manually devised heuristic rules,limiting the scalability of these methods.To simplify the solution and enhance its universality,we propose an innovative crop row annotation strategy.This strategy,by simulating the strip-like structure of the crop row's central area,effectively avoids interference from lateral growth of crop leaves.Based on this,we developed a deep learning network with a dual-branch architecture,InstaCropNet,which achieves end-to-end segmentation of crop row instances.Subsequently,through the row anchor segmen-tation technique,we accurately locate the positions of different crop row instances and perform line fitting.Experimental results demonstrate that our method has an average angular deviation of no more than 2°,and the accuracy of crop row detection reaches 96.5%.

Breakthrough detection in electrochemical discharge drilling to enhance machining stability

Film cooling holes are widely used in aero-engine turbine blades.These blades feature large numbers of holes with complex angles and require a high level of surface integrity.Electrochemical discharge drilling(ECDD)combines the high efficiency of electrical discharge drilling(EDD)with high quality of electrochemical drilling(ECD).However,due to the existence of a variety of energy for material removal,accurate and timely detection of breakthroughs is fraught with difficulties.An insufficient preset setting distance results in a tiny exit aperture,influencing the structure's shape.In addition,the electrode is prone to bending at a large overfeeding distance,causing secondary discharge damaging sidewall surface integrity.This paper compares and analyzes the characteristics of processing waveforms using EDD and ECDD.A novel breakthrough detection method is proposed based on the variance signal of average voltage(VSAV)to increase machining stability and achieve fabrication without a recast layer.This method extracts the fluctuation transformation by calculating the variance of the average.Following signal detection,the overfeeding distance is quantified.An experiment is used to validate the breakthrough detection with 100%accuracy in all tests.The optimum overfeeding distances for hole angles of 0°,30°,and 60° are obtained,and the stable removal of the recast layer is realized.Finally,the effectiveness of the method is verified on a typical workpiece with a double-wall structure and a nickel-based single crystal blade.