Background: Mango leaf diseases reduce fruit yield and quality, requiring early detection for effective management. Traditional methods rely on manual inspection, which is slow, subjective and error-prone. Deep learning, especially Convolutional Neural Networks (CNNs), offers automation but faces challenges. These include class imbalance, poor dataset generalization and limited real-world scalability. This study develops a robust CNN model to improve mango leaf disease classification. Methods: A dataset of 2,494 mango leaf images from the Mendeley database was used. Images were categorized into anthracnose, bacterial canker, cutting weevil, dieback and healthy. Preprocessing involved image resizing, normalization and data augmentation to enhance model performance. The dataset was split into 80% training, 10% validation and 10% testing. A six-layer CNN with ReLU activation, max-pooling, dropout (0.5) and fully connected layers was trained for 25 epochs. The model used Adam optimizer and categorical cross-entropy loss. Result: The model achieved 98.03% training accuracy and 97.77% validation accuracy over 25 epochs. It had a low validation loss (0.0485), indicating good generalization. The confusion matrix showed high precision and recall across all classes. The overall classification accuracy was 96.53%, with a macro-average F1-score of 96.57%. Anthracnose and Dieback were perfectly classified. Bacterial canker had a lower precision (0.8500), suggesting minor misclassifications. AUC analysis showed good disease separation, with Cutting Weevil achieving the highest AUC (0.72). This CNN model can automate mango disease detection, reducing reliance on manual inspections. It can be useful for smart farming systems and mobile applications for real-time disease diagnosis. Future work will focus on expanding the dataset, optimizing for mobile use and integrating environmental factors for better disease prediction.

Agroforestry, the practice of integrating trees with agricultural crops and livestock, has emerged as a sustainable land-use approach with multiple ecological, economic, and social benefits. This review explores the role of agroforestry in enhancing biodiversity and ecosystem services such as soil fertility, water regulation, carbon sequestration, and habitat provision. Despite its potential, agroforestry faces challenges related to knowledge gaps, economic barriers, and insufficient policy support. This paper synthesizes current research on the contributions of agroforestry to biodiversity conservation and ecosystem services, emphasizing its importance in achieving sustainable agricultural practices and mitigating climate change.

Integrated Plant Protection (IPP) represents a holistic and sustainable framework that merges biological, cultural, physical, and chemical methods to manage plant health threats while minimizing ecological disruption. In response to increasing concerns over pesticide resistance, environmental degradation, and consumer demand for safer food, IPP has seen significant innovation. This review elaborates on recent advances in biological control, selective chemical interventions, precision technologies, agroecological approaches, and regulatory frameworks. It also projects future directions, including gene editing, synthetic biology, and participatory governance. A multidisciplinary outlook is crucial to navigate the complexities of plant protection in a changing climate.

The soil microbiome, comprising bacteria, fungi, archaea, viruses, and protozoa, plays a pivotal role in maintaining soil health, nutrient cycling, and plant productivity. With increasing global food demand and environmental degradation, understanding soil microbiome dynamics is essential for sustainable agricultural practices. This review explores the composition, functions, and interactions of soil microbial communities, emphasizing their role in nutrient cycling, disease suppression, and plant growth promotion. Recent advances in metagenomics, transcriptomics, and bioinformatics have enhanced our understanding of microbial diversity and functionality. Additionally, we discuss how agricultural practices such as crop rotation, organic amendments, and reduced tillage influence microbial communities. Finally, we highlight emerging strategies for microbiome engineering to enhance agricultural sustainability, including microbial inoculants and synthetic microbial consortia.

Soil degradation is a critical global issue affecting food security, biodiversity, and ecosystem resilience. Degraded soils often result from unsustainable land use practices, deforestation, overgrazing, industrial activities, and climate change-induced stresses. This review synthesizes recent advancements in ecological, biological, and engineering-based restoration strategies. By integrating multidisciplinary approaches, it is possible to rehabilitate degraded lands, enhance soil health, and improve agricultural productivity. We explore key techniques, case studies, and emerging tools, emphasizing nature-based solutions, microbiome manipulation, and geotechnical interventions. The review highlights the importance of site-specific strategies, community involvement, and policy support in driving effective soil restoration efforts.