Volume 1 Issue 1 (2025)

Food security remains a critical global concern. The rising world population has led to a continuous increase in food demand. Wheat serves as a primary dietary component and its enhanced production is essential to mitigate the food availability challenge, especially in countries like Pakistan. The current study employs descriptive statistical analysis to explore and quantify the impact of various agronomic and input related factors on wheat production. The objective is to identify optimal levels of individual factors aiming to attain the intelligent agriculture practices that significantly contribute to yield improvement. Certified seed increases wheat yield by 25% compared to home-retained seed. A seed rate of 60 kg per acre, adopted by 48.3% of the farmers, is associated with improved productivity. Sowing wheat by mid-November ensures consistently higher yields. The use of 1 to 2 bags of DAP and 2 to 3 bags of urea per acre is associated with maximum yield gains. The use of other fertilizers contributes to a 12.02% increase in production. Pesticide applications for weed control are linked with a 17.11% enhancement in yield. Ploughing/rotavator operations demonstrate a positively increasing trend in yield. Wheat sown after cotton or sugarcane produced better wheat productivity. These findings highlight the critical role of agronomic practices and input management in achieving food security through increased wheat production. Policymakers and agricultural extension services should emphasize these statistically significant factors to support evidence based decision making among farmers. This study promotes intelligent agriculture practices and supports informed decisions for food sustainability.

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Food security remains a critical global concern. The rising world population has led to a continuous increase in foGlobal food security relies significantly on the agricultural sector, with tomatoes being a vital dietary component worldwide. However, various diseases pose an ongoing threat to tomato crop yield and quality. Prompt and accurate identification of these diseases is crucial for sustainable agriculture and effective management practices. This study introduces an innovative approach using Convolutional Neural Networks (CNNs) to enable rapid detection and classification of tomato leaf diseases through image analysis. The system utilizes a high-resolution dataset comprising images of tomato leaves showing symptoms of common diseases such as bacterial wilt, early blight, and late blight. Before training, the dataset undergoes preprocessing to enhance image clarity and eliminate noise, followed by division into training and testing subsets. A custom CNN architecture is developed and trained to automatically learn and extract hierarchical features from the images. Additionally, transfer learning methods are explored to improve the model’s efficiency and generalization. The model’s performance is evaluated using various metrics including accuracy, precision, recall, and F1 score. Results indicate that the CNN model demonstrates high accuracy and robustness in early disease detection. This approach holds substantial potential for practical implementation, offering farmers and agricultural professionals a powerful tool for timely and precise disease management. By enabling targeted responses and supporting precision agriculture, the proposed method represents a significant advancement in integrating modern technology with sustainable farming, ultimately contributing to agricultural stability and global food security.

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Climate change, which arises from the activities of humans, leads to increased temperature and irregular rainfall patterns, which pose a significant threat to food security, particularly in developing nations like Nigeria, where agriculture is critical to the economy and livelihood. Environmental degradation, exacerbated by climate change, further complicates this scenario by reducing arable land, depleting water resources, and altering weather patterns, all of which contribute to decreased agricultural productivity. The current study aims to assess the impacts of the dual challenges of climate change and environmental degradation on food security in Nigeria, using quarterly time series data from 1991 Q1 to 2023 Q4. The research employs multiple machine learning algorithms, including Multiple Linear Regression, K-Nearest Neighbor (K-NN), Support Vector Regression, and Random Forest, to model the complex relationships between climate variables (CO₂ emissions, temperature anomalies) and food production index (FPI), a proxy for food security. The results indicated that CO₂ emissions and temperature anomalies have a significant negative effect on agricultural productivity, while land use and fertilizer consumption positively influence food production. The study concluded that sustainable land management practices, climate-resilient agricultural methods, and investment in agricultural infrastructure are critical to mitigating the adverse effects of climate change on food security. Policy recommendations were made to enhance resilience in Nigeria’s agricultural sector.

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The presented article considers the problem of estimating the parameters of root crop biomass based on Earth remote sensing data. The underground commercial part of the biomass of this type of crops is inaccessible to optical remote sensing. The authors develop a classical approach to estimating the parameters of the state of dynamic systems based on mathematical models. In their previous works, this approach was implemented by the authors to assess crops with above-ground commercial biomass. Such crops are cereals and perennial grasses. To assess the biomass of crops with an underground commercial part, the authors proposed using three mathematical models. The first, main one, is the model of the dynamics of the biomass of a root crop, reflecting the relationship between the above-ground part of the biomass and the mass of root crops. The second is a dynamic model of the parameters of the soil environment, reflecting the removal of nutrients and moisture by the biomass of the root crop. The third is a model of optical remote sensing, reflecting the relationship between the reflectance parameters in the red and near infrared optical ranges with the parameters of the above-ground part of the biomass. Since underground biomass is inaccessible to Earth remote sensing, special requirements are imposed on the model of biomass parameter dynamics. This model must have the property of observability, which ensures the assessment of all components of the root crop biomass when probing its above-ground part. The presence of three mathematical models allows simultaneous assessment of the root crop biomass parameters and soil environment parameters with the closure of the assessment algorithm on real Earth remote sensing data. The proposed methodology and algorithms are quite applicable to other root crops, such as carrots, potatoes, etc.

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Plant diseases significantly affect agricultural productivity by reducing both the quality and quantity of crops. The necessity for automated image‑based solutions stems from the labor‑intensive and subjectively error‑prone nature of traditional inspection methods performed by farmers or agricultural specialists. To maintain sustainable agriculture and prevent the spread of infections, the detection of plant leaf diseases should be performed early and accurately. Early identification of infections can also significantly reduce yield losses and minimize the excessive use of pesticides. Since leaf diseases frequently manifest as uneven texture patterns, spots, or distortions on the leaf surface, local texture capturing mechanisms have proven to be remarkably effective among many computational approaches. This study proposes a novel Deep Convolutional Neural Network (DCNN) to extract high‑level hidden feature representations from leaf images. To enhance performance, the deep features are combined with traditional handcrafted texture features known as the Uniform Local Binary Pattern (uLBP). The proposed model was trained and tested using three well‑known publicly available datasets: Apple Leaf, Tomato Leaf, and Grape Leaf. The model achieved test accuracies of 96%, 91%, and 96% on these datasets, respectively. The experimental results demonstrate that the proposed approach is an effective and practical method for early diagnosis of plant diseases. This system has potential for real‑world application by farmers and agricultural experts to support disease management and contribute to the development of more resilient crops and a sustainable agricultural industry.

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