Developing countries all over the world face numerous difficulties with regard to food security. The purpose of this research is to develop a new approach for evaluating wheat’s suitability for cultivation. To this end, geographical information systems (GIS) and fuzzy inference systems (FIS) are used as the most appropriate artificial intelligence (AI) tools. Outcomes of investigations carried out in the western Nile Delta, Egypt. The fuzzy inference system used was Mamdani type. The membership functions used in this work are sigmoidal, Gaussian, and zmf membership. The inputs in this research are chemical, physical, and fertility soil indices. To predict the final soil suitability using FIS, it is required to implement 81 IF-THEN rules that were written by some experts. The obtained results show the effectiveness of FIS in predicting the wheat crop’s suitability compared to conventional methods. The research region is split into four classes: around 241.3 km² is highly suitable for wheat growth, and 224 km² is defined as having moderate suitability. The third soil suitability class (low), which comprises 252.73 km², is larger than the unsuitable class, which comprises 40 km². The method given here can be easily applied again in an arid region. Decision-makers may benefit from the research’s quantitative findings.

Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive soil data, achieving high accuracy with the artificial neural networks (ANN) model. However, the efficiency of ANN depends on the accuracy of the data that is prepared for training. For this purpose, MATLAB programming language was used to enable the calculation, classification, and compilation of the results into databases within a few minutes. The proposed MATLAB program was highly efficient, accurate, and quick in calculating soil big data for training the machine compared with traditional methods. The database contains 306 vector sets, 80% of them are used for training and the remaining 20% are reserved for testing. The optimal model obtained comprises one hidden layer with 250 neurons and one output layer with a sigmoid function. The ANN achieved a high coefficient of determination (R²) values for SQI estimation, with around 0.97 and 0.98 for training and testing, respectively. The results indicate that 36.93% of the total soil samples belonged to the very high quality class (C1). In contrast, the high quality (C2), moderate quality (C3), low quality (C4), and very low quality (C5) classes accounted for 10.46%, 31.37%, 20.92%, and 0.33% of the samples, respectively. The high contents of CaCO₃, pH, sodium saturation, salinity, and clay content were identified as limiting factors in certain areas. The results of this study indicated high accuracy of soil quality assessment using physical, chemical, and fertility soil features in regression analysis with ANN. This method, which is suitable for arid zones, enhances agricultural productivity and decision-making by identifying critical soil quality categories and constraints.

Evaluating soil quality is crucial for ensuring the sustainable use of agricultural lands. This review examines the definition, evaluation methods, indicator selection, and relevant case studies. The concept of soil quality supplements soil science research by deepening our understanding of soils and aiding in the allocation of resources as agriculture intensifies to meet rising global demand. Soil quality provides a framework for educating stakeholders about the essential functions of soils and offers a tool for assessing and comparing different management techniques. Regular evaluation of soil quality is vital for maintaining high crop yields and addressing the gap between production and consumption. Nowadays, many researchers have explored machine learning (ML) and deep learning (DL) techniques and various algorithms to model and predict soil quality with satisfactory results. These chosen indicators can be influenced by chemical, biological, or physical features. This paper compares ML and DL with traditional methods, examining their features, limitations, different categories of machine learning, and their applications in soil quality assessment. Finally, we show that predicting soil quality has the potential to be extremely accurate and efficient with ML and DL. This distinguishes the application of DL and ML from other approaches since they can anticipate the soil quality index without the need for more intricate computations. Our suggestion for future studies is to evaluate soil quality over broader regions and predict it by using more accurate, modern, and faster methods, using a variety of activation functions and algorithms.

The issue of agricultural soil pollution is especially important as it directly affects the quality of food and the lives of humans and animals. Soil pollution is linked to human activities and agricultural practices. The main objective of this study is to assess and predict soil contamination by heavy metals utilizing an innovative method based on the adaptive neuro-fuzzy inference system (ANFIS), an effective artificial intelligence technology, and GIS in a semiarid and dry environment. A total of 150 soil samples were randomly collected in the neighboring area of the Bahr El-Baqar drain. Ordinary kriging (OK) was employed to generate spatial pattern maps for the following heavy metals: chromium (Cr), iron (Fe), cadmium (Cd), and nickel (Ni). The adaptive neuro-fuzzy inference system (ANFIS), known as one of the most effective applications of artificial intelligence (AI), was utilized to predict soil contamination by the selected heavy metals (Cr, Fe, Cd, and Ni). In total 150 samples were used, 136 soil samples were used for training and 14 for testing. The ANFIS predicting results were compared with the experimental results; this comparison proved its effectiveness, as a root mean square error (RMSE) was 0.048594 in training, and 0.0687 in testing, which is an acceptable result. The results showed that both the exponential and spherical models were quite suitable for Cr, Fe, and Ni. The correlation values (R²) were close to one in training and test; however, the stable model performed well with Cd. The high concentration of heavy metals was the most prevalent, encompassing approximately 51.6% of the study area. Furthermore, the average concentration of heavy metals in this degree was 82.86 ± 15.59 mg kg⁻¹ for Cr, 20,963.84 ± 4447.83 mg kg⁻¹ for Fe, 1.46 ± 0.42 mg kg⁻¹ for Cd, and 48.71 ± 11.88 mg kg⁻¹ for Ni. The comparison clearly demonstrates that utilizing the ANFIS model is a superior option for predicting the level of soil pollution. Ultimately, these findings can serve as a foundation for decision-makers to develop acceptable measures for mitigating heavy metal contamination.

Water desalination plants are significant consumers of electric power, making them some of the largest energy users in power grids. Their electricity consumption presents an urgent challenge for efficient and sustainable operation, and they are among the most power‐quality‐threatening customers for utilities. This study presents three distinct strategies to enhance prediction accuracy and extend forecasting horizons, aiming to reduce algorithmic and hardware delays. Additionally, it suggests effective methods to compensate for voltage fluctuations, voltage flicker, and dips arising from desalination plants. The paper also discusses forecasting harmonics and interharmonics in current signals. Furthermore, it integrates the above techniques into a comprehensive computing system, along with an active power filter (APF) scheme, within the Simulink framework. A comparison is drawn between the performance of predictive techniques in an APF and a conventional, non‐predictive APF. The proposed data augmentation method successfully increases prediction accuracy. By effectively forecasting upcoming waveforms, it reduces algorithmic and hardware delays. These techniques are designed to address multiple power quality issues simultaneously, including harmonics, interharmonics, flicker, and voltage dips, which often coexist in the spectrum as interharmonics. The suggested approach employs Long Short‐Term Memory (LSTM) networks combined with the Jetson TX2 embedded artificial intelligence computer to accelerate machine learning applications. This method has proven effective in predicting and classifying time series data, including harmonics, interharmonics, and raw current signals, achieving 100% accuracy. This eliminates the need for designing specific low‐pass filters. The evaluation results for this time‐domain deep learning‐based technique will be reported in the subsections below. The implementation is conducted in Python using the KERAS deep learning framework and TensorFlow backend, and it is evaluated on a workstation equipped with an Intel i7 processor running at 4.0 GHz and 48 GB of RAM.