Early and late leaf spot are the most devastating, important fungal foliar diseases affecting peanut. The economic loss caused by peanut diseases justifies the necessity to develop more reliable detection methods. Point and image spectroscopy and thermal imaging were used in this study for the early detection of peanut leaf spot. The spectral reflectance factors differ significantly according to the health condition. The leaves of the healthy peanut showed a decreasing reflection in 1015 nm, whereas the heavily diseased leaves showed an increasing reflection. At the thermal infrared range, affected plants show a higher temperature than healthy ones. A presymptomatic decrease in leaf temperature using thermal imagery was found about 1.3°C lower than the healthy leaves. However, the diseased plant’s temperature was 2.2°C higher than that of the healthy one. The temperature difference allowed the discrimination between the infected and healthy leaves before the appearance of visible necrosis on leaves. Two simple indices, early leaf spot index (ELSI) and late leaf spot index (LLSI), were developed to allow early prediction of the peanut disease severity. The disease severity estimation using ELSI and LLSI has an overall accuracy of 78% and 89%, respectively. This demonstrates the efficiency of the proposed indices to estimate the peanut disease.

Recently, the capabilities of artificial neural networks (ANNs) in simulating dynamic systems have been proven. However, the common training algorithms of ANNs (e.g., back-propagation and gradient algorithms) are featured with specific drawbacks in terms of slow convergence and probable entrapment in local minima. Alternatively, novel training techniques, e.g., particle swarm optimization (PSO) and differential evolution (DE) algorithms might be employed for conquering these shortcomings. In this paper, ANN-PSO and ANN-DE models were applied for modeling groundwater qualitative parameters, i.e., SO₄ and sodium adsorption ratio (SAR). Three statistical parameters including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²) were used for assessing the models9 capabilities. The results showed that the ANN-DE presents more accurate results than ANN-PSO in modeling SAR and electrical conductivity (EC).

Artificial neural network (ANN) model was developed and tested for estimating soil phosphorus (P) in Kouhin watershed area (1000ha), Qazvin province, Iran using terrain analysis. Based on the soil distribution correlation, vegetation growth pattern across the topographically heterogeneous landscape, the topographic and vegetation attributes were used in addition to pedologic information for the development of ANN model in area for estimating of soil phosphorus. Totally, 85 samples were collected and tested for phosphorus contents and corresponding attributes were estimated by the digital elevation model (DEM). In order to develop the pedo-transfer functions, data linearity was checked, correlated and 80% was used for modeling and ANN was tested using 20% of collected data. Results indicate that 68% of the variation in soil phosphorus could be explained by elevation and Band 1 data and significant correlation was observed between input variables and phosphorus contents. There was a significant correlation between soil P and terrain attributes which can be used to derive the pedo-transfer function for soil P estimation to manage nutrient deficiency. Results showed that P values can be calculated more accurately with the ANN-based pedo-transfer function with the input topographic variables along with the Band 1

Assessing the vulnerability of groundwater is the first step toward careful management of water resources to avoid or, at least, to minimize impacts on agriculture. The objective of this study was to propose a simple method to assess the groundwater quality and to map its spatial variation in terms of suitability for irrigation in the Darb El-Arbaein area, Southwestern Desert, Egypt. Thirty-six surveyed wells were used to assess and map the groundwater quality. For calculating the Water Quality Index (WQI), a total of 20 (13 chemical, two physical, and five calculated) parameters were considered e.g., EC, pH, Cl, SAR, B, Zn, iron, Mn, Pb and Cd. The results of analyses were used to propose a water quality model. The different water quality maps were produced using GIS software. The results show that three water samples fall into the moderate WQI. Most of the samples (26) fall into the unsuitable WQI category. Seven samples fall into the suitable WQI category. Groundwater samples that fall into the low salinity hazard class and high WQI can be used for irrigation of most crops and the majority of soils. The WQI for the samples ranges from 47.9 to 88.6. The WQI distribution maps delineating an area of 266.66 ha are suitable for irrigation in villages (3,4) and areas of 382.35 ha are moderately suitability for villages (1,2). Since the final map shows the spatial distribution of irrigation water quality in the area, it is now much easier for a decision maker to assess the water quality for irrigation and to locate the most suitable site for drilling wells. The present study demonstrates high efficiency for GIS to analyze complex spatial data and groundwater quality mapping.