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Feed is the most main expenditure in outdoor intensive culture system. The improvement of feeding efficiency has great significance for increasing production and reducing costs. Presently, with the development of precision agriculture, automatic adjustments of the feeding amount according to the demands of the fish has become a developing trend. The objective of this paper was to develop an automatic feeding decision-making system based on the water quality parameters to solve the problem of inefficiency in artificial feeding control. In this study, we proposed an effective control method using adaptive neural fuzzy inference system (ANFIS) to achieve this purpose. First, two input variables (dissolved oxygen saturation [DO]; temperature [T]) and one output variable (feeding percent [FP]) were selected and defined. Second, the model of the linguistic variables and the optimal fuzzy rule base were obtained by the training and learning by utilization of hybrid learning methods. Finally, an ANFIS controller for on-demand feeding was developed and the performance was compared with Fuzzy logic control (FLC) and artificial control (AC) by the Nash-Sutcliffe efficiency coefficient (NS), the root mean squared error (RMSE), and fish growth parameters. The results indicated that the NS and RMSE of the ANFIS model were 0.8539 and 0.0541, respectively, and were better for forecasting feeding decisions compared with the FLC and AC methods. Compared with the AC, there was no significant differences in promoting fish growth (P > 0.05), whereas the feed conversion rate (FCR) was reduced by14.35%. In addition, the mean of ammonia nitrogen concentration decreased by 22.59%, and the mean of turbidity increased 5.5 cm to 28.9 cm, reducing eutrophication and pollution of water in pond. Therefore, applying those approaches based on ANFIS control to the feeding decision system in outdoor intensive culturing is flexible and effective, and has potential for the design of fine feeding equipment and to guide this practice for other species.

Multi-feature fusion of morphology and texture featuresStepwise regression analysis to distinguish disease areas from natural brown areasCalculate the ratio of the total area of the diseased area to the area of the leaf area to obtain the disease level Abstract. In this research, an evaluation method involving digital image processing and stepwise regression was studied to establish an efficient and accurate rating system for studying rice blast disease. For this purpose, the R-G image was segmented by using maximum interclass variance method in which the lesion and naturally withered region was extracted from the leaves. Then, 240 lesion areas and 240 natural yellow areas were selected as samples. During the experiment, ten morphological features and five texture features were extracted. Subsequently, for lesion identification, stepwise regression analysis, SVM and BP neural network were used. In the results, regression analysis of naturally yellow areas showed the highest accuracy in lesion identification, reaching 93.33% for disaster-level assessment of identified lesion areas. On the basis of the results, it is evident that 153 samples were correctly classified into divisions of 160 tested different rice blast leaves, with 95.63% classification accuracy. This study has introduced a new method for objective assessment of leaf blast disease. Keywords: Disease classification, Lesion identification, Maximum interclass variance method, Rice blast, Stepwise regression.

In outdoor pond culturing systems, formulated feed is the most main nutrition source and expenditure. Improving the feeding management efficiency is of great significance for reducing economic losses and alleviate ecological environmental pollution. In recent years, with the development of precision agriculture and animal welfare rearing, providing appropriate feed quantity based on the nutrient requirements of fish has become an important developing trend. In this study, a hybrid method, combining the bioenergetics factorial model with fuzzy logic control technology, was proposed to predict the feed requirement of grass carp and construct a precision feeding control (PC) system in outdoor pond culturing conditions. First, prediction of the growth parameter of grass carp using the calibrated TGC (the thermal-unit growth coefficient) model in different growth stanzas; then, prediction of energy and nutrition demands based on the model of the bioenergetics factorial; and then, determination of the feed requirement and feed rate daily according to the nutrient composition of feed and apparent digestibility coefficient (ADC) of grass carp; finally, determination of the proportion of each feeding time in per day using fuzzy logic control technology (FLC) based on dissolved oxygen (DO) and water temperature (WT). In addition, the performance of the PC system and traditional feeding control (TC) system was evaluated by the Nash-Sutcliffe efficiency coefficient (NS), the root mean squared error (RMSE), the water quality parameters, and fish growth parameters, respectively. The results indicated that the PC system was superior to that of the TC treatment for forecasting feed decisions, the NS was promoted 0.534 to 0.936 and the RMSE was reduced 22.512 to 10.973, respectively. Meanwhile, Compared the growth parameters of fish with TC treatment, it did not affect the growth performance of fish but promote it to a certain extent, and the feed conversion rate (FCR) of fish was decreased by 12.24% (P

Mathematical models of the egg-laying curves for Yangzhou geese exposed to both natural and artificial photoperiods were established to optimise the parameters for maximising geese reproductive performance and for the development of precision feeding methods. With the natural photoperiod, egg-laying starts in autumn when daily photoperiod decreases, but accelerates after the winter solstice, and reaches the peak in spring when photoperiod increases. An accumulating model was constructed based on the hypothesis that the egg-laying capacity of geese was determined by two components of the photoperiod: photo-stimulation and photo-inhibition. In addition, a second segmented model was constructed based on the hypothesis that the photo-stimulation only occurred with lengthening photoperiods after the winter solstice, and the lesser laying rate in autumn could be attributed to the non-photo-dependent animal-husbandry technologies. This model consists of a logistic model before the winter solstice, and an accumulating model after this solstice. The use of the logistic and accumulating resulted in more precise predictions that occurred with use of Model 1 with a greater R2 and lesser RMSE, AIC and BIC. Likewise, the egg-laying curves when there was consideration of artificial photoperiods could also be constructed with consideration of stimulatory and inhibitory photoperiodic effects. The model consists of an initial logistic and subsequently a quadratic polynomial model. With use of this model, there is consideration of changes in egg-laying patterns when there is a fixed photoperiod, with the model parameters reflecting the effects by photoperiod control-programs and age of the geese. In conclusion, new mathematical models have been developed to best fit egg-laying curves when there are both natural and artificial photoperiods. These models can contribute to development of precision-feeding technologies for breeding geese in future.

The present study emphasized the co-digestion of the thermal-H2O2 pretreated wheat straw (WS) and chicken manure (CM) with the waste activated sludge at four levels of C/N (35:1, 30:1, 25:1 and 20:1). All C/N compositions were found significant (P

Aiming at the shortage of traditional phase-locked method in the three-phase unbalanced conditions of power grid, this paper proposed a novel software phase-locked loop control strategy. This paper introduces in detail the working principle and designing method of the digital PLL by comparative experiment. The experimental results demonstrated that the new method is feasible and robust. In conclusion, the new control algorithm can quickly and accurately detect the correct phase voltage. Keywords-rapid phase lock; voltage; unbalanced grid voltages; grid voltage dips; grid inverter; negative sequence compensation

Spikelet number per panicle (SNPP) is one of the most important yield components used to estimate rice yields. The use of high-throughput quantitative image analysis methods for understanding the diversity of the panicle has increased rapidly. However, it is difficult to simultaneously extract panicle branch and spikelet/grain information from images at the same resolution due to the different scales of these traits. To use a lower resolution and meet the accuracy requirement, we proposed an interdisciplinary method that integrated image analysis and a 5-point calibration model to rapidly estimate SNPP. First, a linear relationship model between the total length of the primary branch (TLPB) and the SNPP was established based on the physiological characteristics of the panicle. Second, the TLPB and area (the primary branch region) traits were rapidly extracted by developing image analysis algorithm. Finally, a 5-point calibration method was adopted to improve the universality of the model. The number of panicle samples that the error of the SNPP estimates was less than 10% was greater than 90% by the proposed method. The estimation accuracy was consistent with the accuracy determined using manual measurements. The proposed method uses available concepts and techniques for automated estimations of rice yield information.