Agriculture is pivotal for the economy of a country as it is a major source of food, employment and raw materials. However, challenges such as diseases, soil degradation, and water scarcity persist. Technology adoption can address these issues, improving production and quality. Machine learning, a subset of Artificial Intelligence (AI), enables prediction, classification, and automation in agriculture. It optimizes irrigation, fertilization, and crop selection, aiding decision-making for food security and crop management. This study proposes two robust machine learning architectures for classification and regression based on distinct datasets. Firstly, we delve into a crop recommendation dataset obtained from Kaggle, consisting of various input attributes such as the pH of the soil, temperature, humidity, and nutrient levels. Leveraging machine learning classification techniques such as Extra Tree Classifier (ETC), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbour (KNN), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM), we suggest twenty-two different crops founded on these inputs. Through the use of K-fold cross-validation, Explainable AI (XAI) and feature engineering, we identify the best-performing model, with Random Forest coming out on top scoring an accuracy of 99.7% with precision, recall, F1 score, and confusion matrix. Secondly, we investigate wheat yield prediction data snagged from the World Bank and Food and Agriculture Organization (FAO), covering the years 1992-2013 for Pakistan. Using Multivariate Imputation by Chained Equations (MICE) to tackle data restrictions, we gauge wheat production for 2014-2024 and forecast the 2025 yield using machine learning regression models. Once again, using hyper parameter tuning with K-fold cross-validation, Support Vector Regressor (SVR) stands out as the top-performing model, achieving an accuracy of 99.9% with R² Score. Transparency and confidence in agricultural decision-making are increased when machine learning decisions are made comprehensible using Explainable AI (XAI) approaches. Two widely used XAI approaches, namely Feature Importance and Local Interpretable Model-Agnostic Explanations (LIME) are used to interpret and explain outcomes of the proposed models. The study can increase agricultural productivity, minimize risks, enhance food security, and promote more environmentally friendly farming approaches.

Swarm intelligence algorithms are metaheuristics inspired by the collective behavior of species such as birds, fish, bees, and ants. They are used in many optimization problems due to their simplicity, flexibility, and scalability. These algorithms get the desired convergence during the search by balancing the exploration and exploitation processes. These metaheuristics have applications in various domains such as global optimization, bioinformatics, power engineering, networking, machine learning, image processing, and environmental applications. This paper presents a systematic literature review (SLR) on applications of four swarm intelligence algorithms i.e., grey wolf optimization (GWO), whale optimization algorithms (WOA), Harris hawks optimizer (HHO), and moth-flame optimizer (MFO) in the field of software engineering. It presents an in-depth study of these metaheuristics’ adoption in the field of software engineering. This SLR is mainly comprised of three phases such as planning, conducting, and reporting. This study covers all related studies published from 2014 up to 2022. The study shows that applications of the selected metaheuristics have been utilized in various fields of software engineering especially software testing, software defect prediction, and software reliability. The study also points out some of the areas where applications of these swarm intelligence algorithms can be utilized. This study may act as a guideline for researchers in improving the current state-of-the-art on generally adopting these metaheuristics in software engineering.