International Journal of Agriculture Innovations and Cutting-Edge Research (HEC Recognised)

Integrating New Frontier Digital Twins Technology in Smart Agriculture Revolution

Tue, 07 Apr 2026 00:00:00 +0000

The effects of climate change on agriculture are very profound, including food security and the financial stability of developing countries. Thus, Artificial Intelligence (AI), Internet of Things (IoT), and Digital Twins (DTs) are significant in changing agriculture to a data-enabling, real-time system to develop crop management, high productivity, and climate mitigation. Such technologies are useful in predicting the time of droughts and scheduling the irrigation timetable based on climatic changes, and also in deciding on the appropriate crop rotation within a particular area. AI and IoT may be combined to create DTs to facilitate climate-resilient precision farming. This technology embraces agricultural workplaces, livestock surveillance, crop harvesting, crop protection, and predictive maintenance systems. It also changes how agriculture is practised by examining huge amounts of information to predict the impact of climate change. Precision agriculture is an AI-driven technology that uses micro-localised applications, which are informed by synthetic sensory data, drones, and satellite data. Whereas Smart agriculture combines AI, Big Data Analytics, IoT, and DT to collect, unite, and interpret information from many sources. With AI-powered models, future weather conditions, insects, and disease outbreaks are predictable, allowing for early intervention and increased crop production. Such insights culminate in better allocation of resources, optimisation of agricultural activities, and high farm productivity amidst climate change. As a consequence, the DT technology can be a game-changer in the field of agriculture in the future. In this study, DT in conjunction with IoT sensors and AI models has been explained conceptually and potentially as useful in precision agriculture to adjust to the rise in climate change by anticipating droughts, optimising irrigation, and enhancing crop control through real-time data analysis.

Evaluating the Effects of Potassium and Naphthalene Acetic Acid on Drought Tolerance of Wheat

Mon, 06 Apr 2026 00:00:00 +0000

Wheat productivity in semi-arid and rainfed regions is severely affected by drought stress, which limits growth, physiological efficiency, and yield. “A field study was conducted during the Rabi season 2024–2025 at the University Research Farm, Koont (Pothowar region, Punjab, Pakistan) to evaluate the effects of foliar-applied Naphthalene Acetic Acid (NAA) and Potassium Nitrate (KNO₃) on growth, physiological traits, drought tolerance, and yield performance of wheat. The experiment was conducted using a Randomised Complete Block Design (RCBD) with eight treatments and three replications. Treatments comprised: T₁ (Control), T₂ (NAA 50 ppm), T₃ (NAA 100 ppm), T₄ (NAA 150 ppm), T₅ (KNO₃ 2%), T₆ (NAA 150 ppm + KNO₃ 2%), T₇ (NAA 100 ppm + KNO₃ 2%), and T₈ (NAA 50 ppm + KNO₃ 2%). Foliar applications were applied at the tillering and booting stages. Data were recorded for chlorophyll content, crop growth rate, plant height, spike length, number of grains per spike, 1000-grain weight, and grain yield. The results showed that the combined application of NAA and potassium significantly improved physiological and yield parameters compared to individual applications and control treatment. The highest grain yield was recorded in treatment T6 (NAA 150 ppm + KNO₃ 2%), which produced significantly higher chlorophyll content and crop growth rate. Chlorophyll content ranged from 34.06 (T₁) to 41.09 (T6). Values for T₂, T₃, T₄, T₅, T₇, and T₈ were 35.93, 37.02, 38.48, 40.11, 38.22, and 38.98, respectively. Crop growth rate increased from 8.51 g m ² day⁻¹ in control to 13.64 g m⁻² day^-1in T₆, while T₂, T₃, T₄, T₅, T₇, and T₈ recorded 10.05, 10.50, 11.86, 12.87, 11.88, and 12.45 g m⁻² day^-1, respectively. Plant height improved from 85.16 cm (T₁) to 102.81 cm (T₆). Other treatments produced heights of 89.88 cm (T₂), 93.13 cm (T₃), 95.82 cm (T₄), 98.57 cm (T₅), 95.34 cm (T₇), and 98.66 cm (T₈). Spike length plant^-1 increased from 8.33 cm (T₁) to 11.02 cm (T₆), with intermediate values of 9.07, 9.19, 9.85, 10.56, 10.01, and 10.21 cm under T₂, T₃, T₄, T₅, T₇, and T₈, respectively. Spikelets spike^-1 ranged from 13.94 (T₁) to 18.68 (T₆), while T₂, T₃, T₄, T₅, T₇, and T₈ recorded 15.00, 15.96, 17.01, 18.06, 16.97, and 17.96 spikelets spike^-1, respectively. The highest 1000-grain weight (43.40 g), grain yield (4655.85 kg ha⁻¹), biological yield (10327.94 kg ha⁻¹), and harvest index (44.84%) were obtained in T6, whereas control plots recorded 36.83 g, 3216.27 kg ha⁻¹, 8210.08 kg ha⁻¹, and 38.77%, respectively. Grain yield under T₂, T₃, T₄, T₅, T₇, and T₈ was 3459.97, 3701.05, 4195.93, 4506.53, 4351.55, and 4506.53 kg ha⁻¹, respectively. The results were statistically significant at P ≤ 0.05. The improvement in yield may be attributed to enhanced stomatal regulation, osmotic adjustment, and improved nutrient uptake under drought conditions. The improvement in growth and yield parameters under integrated treatment may be attributed to improved photosynthetic efficiency, osmotic adjustment, water use efficiency, and better assimilate partitioning under drought stress conditions. The study concluded that the integrated application of potassium and auxin is an effective strategy for improving wheat productivity under drought stress conditions and can be recommended for semi-arid and rainfed agricultural systems.

Keywords: Wheat, Potassium, Naphthalene Acetic Acid, Foliar application, Grain yield, Growth attributes, Rainfed agriculture, Drought stress, Water Use Efficiency, Crop Growth Rate.

Auxin-Induced Rooting and Seedling Quality Enhancement in Seedless Lemon (Citrus limon L.) under Nursery Conditions

Mon, 06 Apr 2026 00:00:00 +0000

Seedless lemons (Citrus limon L.) are valued at the global level for the character of seedless. However, these cultivars are often characterised by poor rooting capability and low survival rates. In order to resolve this issue, the present study was conducted during the spring season of 2024 at the Nursery of Sindh Agriculture University (SAU), Tandojam, to evaluate the influence of auxin treatments on the sprouting and seedling growth of seedless lemon cuttings. A factorial experiment in a Completely Randomised Design (CRD) was laid out with four replications, comprising two varieties (Persian lime and Malaysian lemon) and four treatments: IBA gel dip, NAA powder dip, IBA gel + NAA powder dip, and control. All the seedling-related parameters, such as sprouting percentage, number of sprouts, days to sprouting, rooting percentage, root depth, seedling quality index, sturdiness quotient, shoot biomass and root biomass, were significantly affected by the auxin treatments. However, varieties and their interactive effect with auxin treatments were only significant for a few sprouting and rooting-related parameters. The statistical results revealed that the maximum sprouting percentage, number of sprouts per cutting, rooting percentage and minimum days to sprouting were observed in Persian lemon with the NAA powder dip method. The Dickson Quality Index, Sturdiness quotient, and biomass of shoot and root were not significantly affected by the varieties or their interactive effect with auxin treatments. However, Auxin as an independent factor had significant effects on all these parameters. The highest DQI, SQ, and biomass of shoot and root were observed with NAA powder dip treatments. However, the Number of sprouts and root depth traits were significantly influenced by treatments alone, with IBA gel dip resulting in more sprouts and the deepest roots. The findings suggest that NAA powder dip is the most effective treatment for enhancing rooting and overall seedling vigour in seedless lemon cuttings, particularly in the Persian variety.

Determination of Phytochemicals in Medicinal Plants Collected from Adjoining Areas of Lahore through Fourier Transform Infrared Spectroscopy

Faiqa Ghaffar, Uzma Hanif, Romana Aziz, Sarosh Sohail, Adeel Mustafa — Thu, 09 Apr 2026 00:00:00 +0000

In the present research work, some medicinal plants from the adjoining areas of Lahore were investigated for their phytochemical screening and FTIR analysis. Four samples of medicinal plants Cichorium intybus (Kasni), Foeniculum vulgare (Sonf), Solanum nigrum (Makoh)and Polygonum aviculare(Anjbar)were selected. The main objective of the present study is to identify the phytochemicals through FTIR instrumentation, which encourages Sustainable Development Goals (SDGs) by minimising waste and chemical usage. Phytochemical testing of extracts of leaves of C. intybus, S. nigrum and roots of F. vulgare, and P. aviculare was carried out in four different solvents (Methanol, chloroform, n-hexane and aqueous solutions). The purpose of this study is to help in the formulation of herbal medicines and their quality assurance, and to make innovations to support industries in developing plant-based products contributing to Sustainable Development Goals (SDGs). Phytochemicals identified were steroids, alkaloids, carbohydrates, flavonoids, phenols, tannins, saponins, cardiac glycosides, proteins and reducing sugars. Functional groups were identified in the leaves and roots of medicinal plants like esters, alcohols, alkenes, nitrites, amino acids, carboxylic acids, ethers, aromatics, organic halogens and carbohydrates. In this study, an effort was also taken to understand the importance of functional groups as bioactive components for treating various illnesses, and which functional group is responsible for a certain medicinal property of a medicinal plant. This study investigates the application of phytochemical screening and Fourier Transform Infrared (FTIR) spectroscopy for the identification of phytochemicals in a selected medicinal plant, addressing the limited analytical data available for such species, and recommends further research to explore their therapeutic potential.

Pathobiome

Tue, 05 May 2026 00:00:00 +0000

The olive industry is facing considerable pressure from emerging diseases caused by various pathogens, including Xylella fastidiosa, Verticillium dahliae, and Fusarium oxysporum. This has led to increased interest in developing environmentally friendly, sustainable management strategies for these diseases through biological control and resistant rootstocks. The recent researches have emphasized the need to know the pathobiome, the complex community of microorganisms that coexists with the plant and affects its health and production. Pathobiome microorganisms may be beneficial and detrimental to the plant. In addition, the pathobiome is important to understand in order to create new varieties of olive, resistant to diseases. Although there are serious challenges, the current developments in the field of sequencing technologies and bioinformatics tools are aiding in overcoming these challenges. It is important to develop sustainable and environmentally friendly methods, which take into consideration both the plant and the associated microorganisms, so as to ensure effective disease control measures and the formulation of new disease-resistant olive varieties. The review paper will inform about the importance of the pathobiome in the establishment of effective disease management strategies and indicate challenges and opportunities in the field.

Integrative Genomic and Expression Analysis of Zinc Transporter (ZIP) Genes in Sunflower (Helianthus annuus L.) under Drought Stress

Sidra Zaman, Zamarud Shah, Zeeshan Khan, Iqra Shah, Kashf Tanveer — Wed, 06 May 2026 00:00:00 +0000

Among micronutrients, zinc serves predominantly as a cofactor of many enzymes involved in important biochemical reactions. Vigorous sunflower growth in response to zinc application points towards the existence of an efficient zinc transportation system in this crop. ZIP protein is responsible for the availability of zinc and iron in all plant cells. The absence of comprehensive ZIP gene characterization in sunflower was the driving force for conducting this research work. The objective of the current study was to explore and characterise all ZIP genes across the sunflower genome. A total of 19 ZIP genes were identified and designated as HaZIPs in ascending order. All 19 HaZIP proteins were predicted to exist in the plasma membrane. The HaZIPs family was clustered into 3 groups based on phylogenetic assessment. Not much diversity in structural features of genes belonging to the same group was observed; the genes belonging to different groups exhibited variations in motif configuration. The genes were unevenly mapped on 9 chromosomes, with the maximum genes (7) found on chromosome 15. Two paralog pairs showed segmental duplication, while tandem duplication was witnessed in 4 paralog pairs. Sunflower exhibited no phylogenetic association with other crops, except Arabidopsis thaliana, where a single ortholog was witnessed. Significant increase in expression of HaZIP-1, HaZIP-3, HaZIP-5 and HaZIP-19 was recorded upon exposure of sunflower to drought stress, compared to control, for all 1h, 3h, 6h and 9h. Overall, maximum expression of all 4 genes was witnessed after 3h treatment, while minimum expression was recorded after 9h exposure to drought stress. The cis-acting ABRE could be involved in higher expression of HaZIP genes under drought stress.

Comparative Performance Analysis of Machine Learning and Deep Learning Models for IoT-Based Crop Yield Prediction

Azeem Ayaz Mirani, Nimra Memon, MR. Imran Khan Jatoi, Shahid Iqbal — Wed, 06 May 2026 00:00:00 +0000

The environmental uncertainty and the nonlinear behaviour of IoT-sensor data nature influence the crop yield prediction. In Nawab Shah, this region requires a model which behaves capturing complex patterns. This study provides a deep learning and machine learning comparative analysis, whereas data is obtained from the IoT-based sensor data; the dataset parameters include temperature, humidity, smoke, and light intensity. The dataset has more than 35000 time-stamped samples, collected with the five seconds delay in each data reading. These observations are processed by applying data normalisation, outlier detection and cleaning, and min-max normalisation. The statistical data validation is obtained by applying RMSE, MAE, MSE, Pearson’s correlation and confusion matrix. The Bayesian-optimised random forest consistently achieved outstanding performance with the highest accuracy, recall, and precision with 0.33 F1-Score. The smoke and humidity are the significance factor from the obtained results analysis for the yield prediction. The classification ability is confirmed by the confusion matrix with the ability as average, good and poor classes of the yield. Furthermore, the finding shows that the optimised random forest performed better than all in the environmental data for the prediction of yield. This is also based on the same features as smoke and humidity; this methodology and approach provide a reliable and low-complex framework with a real-time precision agriculture system for decision-making. LSTM models, along with a variant of Random Forest, give results of 0.27 intermediate range value, which recommends that the very important patterns are captured, but are not effective for the top models.

A Comparative Review of Boosting Algorithms for Agricultural Applications

Assadullah Soomro, Mushtaque Ahmed Rahu, Sayed Mazhar Ali, Sarang Karim — Tue, 19 May 2026 00:00:00 +0000

The flexibility of Machine Learning algorithms, their automation, and the capability to address big data have been heavily exploited in agricultural research. The most notable Machine Learning algorithms are the Boosting Algorithms, "Gathering wisdom in a group of Fools", thus turning weak learners into strong learners. Both high flexibility and interpretability are key features of Boosting algorithms. Through this work, we give insights into the characteristics of Boosting Algorithms to enable them to better exploit their strengths in agricultural research. This paper summarises recent developments in boosting algorithms, relevant applications in agriculture, and how the implementation of boosting algorithms and their use are related to their properties. This study demonstrates that great progress in the sphere of agriculture can be achieved in terms of explanation and interpretation, as well as in terms of predictive performance of the Boosting. This paper provides a detailed overview of the significant Boosting algorithms used in agriculture, like AdaBoost, Gradient Boosting Machines (GBM), XGBoost, LightGBM, CatBoost, and other successful variants. After analysing 45 peer-reviewed publications from 2015 to 2025, we compared the different algorithms in terms of their predictive accuracy, training speed, ability to deal with categorical data, overfitting control, and scalability and present a decision matrix for choosing the algorithms for specific agricultural applications, such as crop yield prediction, disease detection, and soil analysis. This study also gives a comparative summary to advise practitioners on the best algorithm to use in various applications, especially in agriculture. The paper has ended with unrestricted research direction and valuable suggestions to practitioners in the agricultural sector.

Seasonal Population Dynamics of Red Palm Weevil (Rhynchophorus ferrugineus Olivier) in Relation to Climatic Factors and Date Palm Varietal Susceptibility in Panjgur, Balochistan, Pakistan

Mon, 01 Jun 2026 00:00:00 +0000

The red palm weevil (Rhynchophorus ferrugineus Olivier) is a serious pest of date palm (Phoenix dactylifera L.) worldwide and has become a major threat to date growers in the Makeran Division, Balochistan, especially in the Panjgur district. This study monitored RPW population dynamics and seasonal activity over 26 weeks (April–September 2025) in the district of Panjgur, using pheromone-baited plastic bucket traps. Weekly adult counts, sex ratio, and climatic data (temperature and relative humidity) were recorded. RPW adults were present throughout the observation period, showing two population peaks: an early-season peak and a late peak around weeks 24–26, with the lowest activity recorded at week 18. Mean adult catches were 7.65 ± 0.38 weevils per trap. Females were captured in greater numbers, 4.60 ± 0.25, than males, 3.04 ± 0.15, weevils per trap, indicating strong pheromone attraction. The mean temperature in district Panjgur was 27.25 ± 0.36°C, and the mean relative humidity was 26.73 ± 0.49%. Variety-wise infestation showed the Mozawati with the highest infestation, 11.33 ± 1.33, and Shakar with 6.67 ± 0.66. The infestation rate of RPW adults and larvae varies in different sample varieties; the average adult catches in all varieties were 3.02 ± 0.35, and the mean larval catches were 4.16 ± 0.44. These results suggest that pheromone trapping, combined with other control measures and timed to climatic windows, can improve sustainable RPW management and date palm production in Makeran Division.

Urbanization and Anthropogenic Impacts on Fungal Diversity

Adeel Mustafa, Uzma Hanif, Nayyab Munir, Mehar Un Nisa, Hafiza Mehak Munawar — Tue, 02 Jun 2026 00:00:00 +0000

Macroscopic fungal species forming fruiting bodies are vital for the proper functioning of ecosystems due to their roles in decomposition and nutrient cycling, soil formation, and plant fungal symbiosis. Urbanization and human intervention lead to significant alterations in fungal communities in soil, water, atmosphere, and artificial ecosystems through changes in diversity, ecological functional groups, and ecosystem stability. This review presents a critical synthesis of the available information on the influence of urbanization processes and anthropogenic impacts on mushroom diversity and examines the importance of fungi as ecological bioindicators. The review was carried out via a comprehensive literature analysis focusing on urban mycology, fungal ecology, environmental monitoring, and biodiversity conservation. Key topics covered in this paper include habitat fragmentation, pollution-induced stress, climate change-related impacts, changes in fungal community structure, and the ecological value of mycoindicators of environmental conditions. In addition, special attention is paid to the relevance of fungal conservation in light of global environmental challenges such as the implementation of the UN Sustainable Development Goals. The study recommends establishing a comprehensive system of monitoring the mycobiome in urban areas, developing strong conservation policies for fungi, and raising public awareness regarding the importance of fungi for urban sustainability.

An explainable AI-based cotton leaf disease classification using EfficientNet, Grad-Cam, Lime, and Shap

Sumera Nazim, Abdul Samad, Nisha Tanwani, Jalal Bhayo, Mushtaque Ahmed Rahu — Tue, 02 Jun 2026 00:00:00 +0000

The quality of the cotton crop is reduced in terms of fibre, yield quality, and economic standard, especially due to leaf infections and disease symptoms, which spread in all field areas. The automated visualisation of disease screening from the infected images of leaves, due to limitations of the black box nature of the deep learning models, creates the less agriculture developments. This research provides a framework with integration of the deep learning and explainable AI-based approach for the detection and classification with the EfficientNet approach for the cotton crop disease, along with an explanation by adopting the Grad-CAM and SHAP discussion and explanation. The experimentation is performed using 800 Images with labelled leaf images of cotton crop disease obtained from Kaggle. The data preprocessing is used for the image resized as 224 x 224 pixels, augmented and normalised, with spilt into validation, training, and testing data subsets. The results show that after 20 epochs, the EfficientNet Model provides subtle results and is stable with 92% accuracy for image disease detection. The confusion matrix shows the 45 correctly healthy classified images, the disease leaves 43, false positives 5, and false negatives 7 by providing the 88.00% yielding test accuracy and 89.58% the precision of the disease class, recall 86.00%, f1 score 87.76%. The spatial heatmap, highlighted by the Grad-CAM, provides the symptoms of the leaf region. Whereas the pixel-level explanation is obtained by the LIME and summarises the visual contextual explanation of the feature from the image. The predictive performance is focused in this framework with transparency, reliability, and interpretability for the cotton crop diseases.

Enhancing Citrus Yield and Water Use Efficiency in Pakistan’s Arid Zones

Sat, 06 Jun 2026 00:00:00 +0000

Water scarcity limits citrus production in arid areas of Pakistan. This study evaluated five irrigation techniques – basin irrigation (BI, control), surface drip irrigation (SDI), plastic bottle irrigation (BTI), pitcher irrigation (PI), and perforated plastic sleeves irrigation (PPSI) – for their effects on soil moisture, fruit yield, water productivity, and economic returns of five‑year‑old Kinnow mandarin trees over four growing seasons (2016–2019) at Fateh Jang. The experiment used a randomized complete block design with three replications. SDI significantly (p ≤ 0.05) increased fruit yield (16,416 kg ha^-¹) and water productivity (7.13 kg m^-³) compared to BI (9,732 kg ha^-¹ and 1.43 kg m^-³, respectively). SDI also achieved the highest net return (Rs. 2,087,400 ha^-¹) despite higher installation costs. PI and BTI produced intermediate yields (11,969–12,301 kg ha^-¹) with water productivity 4–5 times higher than BI. PPSI showed the lowest performance among the water‑saving techniques. We conclude that surface drip irrigation is the most effective technique for enhancing water productivity (83%) and citrus yield (51%) under arid conditions, though pitcher and bottle irrigation offer affordable alternatives for resource‑limited farmers.

Mitigation of Cigarette Filter Residue Toxicity in Maize Through Organic Amendments Under Controlled Soil Conditions

Sat, 06 Jun 2026 00:00:00 +0000

Cigarette filter residues are emerging soil pollutants that may adversely affect crop growth and soil health due to the release of toxic compounds and non-biodegradable materials. The objective of this study was to evaluate the effects of cigarette filter residues and organic amendments on the growth, yield, and toxicity mitigation potential in maize (Zea mays L.) grown under contaminated soil conditions. A pot experiment was carried out at PMAS-Arid Agriculture University using a Completely Randomized Design (CRD) consisting of six treatments with three replications each. The treatments included control (T1), cigarette filters alone (T2), farmyard manure (T3), poultry manure (T4), farmyard manure + cigarette filters (T5), and poultry manure + cigarette filters (T6). Data regarding plant height, stem diameter, number of leaves, shoot and root biomass, total leaf area, leaf area index, and yield parameters were recorded and analyzed statistically value through analysis of variance (ANOVA), and treatment means were compared using the Least Significant Difference (LSD) test at a 5% probability level.

The results revealed that organic amendments significantly improved maize growth and yield attributes compared with control and cigarette filter treatment alone. The combined application of poultry manure and cigarette filters (T6) produced the highest plant height (194 cm), total leaf area (5903 cm²), grain yield per plant (141 g), biological yield per plant (267 g), and harvest index (52.8%), representing substantial improvements over the control treatment. Poultry manure performed better than farmyard manure due to its higher nutrient availability and rapid mineralization. The combined application of poultry manure and cigarette filters effectively minimized the negative effects of cigarette filter residues by improving soil fertility, nutrient uptake, and biomass accumulation. These findings demonstrate that organic amendments can serve as an environmentally sustainable strategy for mitigating cigarette filter residue toxicity in agricultural soils. In particular, poultry manure showed strong potential for improving soil quality and enhancing maize productivity, offering a practical waste-management and soil-remediation approach for contaminated environments. These findings were obtained under controlled pot conditions and should be validated through field-based investigations before broader agricultural recommendations are made.