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The loss of fresh fruits after harvesting is not new since it has constantly been a challenge for humankind. The growing population in developing countries, where food shortages exist, require serious food security measures to address hunger and malnutrition. Present research focused on the development, fabrication and testing of mango pulp extractor to assist small-scale fruit farmers in the countryside with a view to minimizing fruit spoilage. The unit, whose major material was food grade stainless steel (SS-304), consists of major components such as teflon brushes mounted shaft, motor, main frame, hopper, extraction compartment, pulp outlet, fruit residue outlet, perforated sieve and bearings. After construction, the machine was tested at three feed rate (2.0, 2.5, 3.0 kg/min) and extraction speed levels (500, 900 and 1400 rpm). Each of these factors was replicated three times, which resulted into 3 × 3 × 3 factorial experimental design. The optimum operating parameters for maximum pulp yield, maximum extraction efficiency and minimum extraction losses were determined. The physicochemical analysis of the extracted pulp was also carried out. Results revealed a maximum pulp yield of 77.9%, highest extraction efficiency of 96.03% and highest extraction loss of 9.3%. The mango pulp extraction machine was found to be affordable, easy to operate and maintain. The breakeven point of the machine was found to be 40 h if the machine is operated at its peak capacity. Therefore, it is recommended for small-scale farmers and for cottage industry.

All tropical and subtropical areas of the world are suitable for citrus cultivation. In managing fertilizer application efficiency in orchards, variable-rate technology (VRT) has been demonstrated to be an important element. This article aims to study the significance of variable-rate fertilization for citrus in the arid region of Pakistan. The NPK was calculated before the application of the variable-rate fertilizer. The plant height and stem girth were determined before and after fertilizer application. The preliminary results revealed that the stem girth performed significantly better than the plant height after applying fertilizer by VRT. The preliminary results showed a significant difference in the fruit yield between the VRT and uniform-rate fertilizer application.

Technology integration between the farm and the consumer is at the heart of digital agriculture. New technologies for developing countries, such as vertical indoor farming that uses automation and robots, might speed up the elimination of rural poverty and hunger worldwide. Significant technical developments include state-of-the-art greenhouse techniques, laser-guided precision farming, AI, and blockchain. Connected farm machinery collects data that may then be used to research the soil and climate of a given area, allowing experts to offer advice on seed selection and the optimal timing for applying pesticides and fertilizers. One of the most widely used innovations of the previous century was the mobile phone. The use of digital technology will improve communication between consumers and farmers. The public now has more accessible access to information on farming because of the wealth of data collected on crops and livestock. Smart farming’s impact on crop yields will be seen in the long term.

Crop growth and yield are influenced by the genetic potential of the cultivar, soil, weather, and cultivation practices, i.e., sowing date, irrigation and fertilizer amount, and biotic stresses. Temporal variation in yield and growth has been largely forecasted using climate as a predictor, which can be achieved by using either an empirical or crop simulation approach for a given location. Climate and soil data collected over agricultural land regularly aid in crop growth monitoring, as well as crop vitality assessment. Crop simulation models (CSM) that have been successful in field-scale applications are now being implemented in GIS framework to simulate and monitor crop growth with remote sensing inputs, allowing for sensitive evaluations of seasonal weather conditions, local variability, and crop management signals. This research was designed to monitor the growth of three varieties of kiwifruit, i.e., Hayward grafted, Green-flesh, and Hayward, in four different localities: Hazaro (Attock), Simli Dam (Pind Begwal), GPU (germ plasm unit) Arid Agriculture University Rawalpindi and ZTBL (Zarai Taraqiati Bank Limited) Farm Islamabad, each of which has different soil and weather conditions. Soil proximal sensors were used to measure soil characteristics, and data loggers were installed in each field to monitor the weather parameters to collect data that influences crops. In this study, we used a quantitative method and GIS-integrated data to assess the impact of soil and climate on kiwifruit growth. It can help policy makers and researchers to identify new agro-climatic zones in Pakistan’s semi-arid regions for kiwifruit farming based on this data. In this study, we found that kiwi is very susceptible to temperatures above 40 °C, which cause mortality in kiwifruits plants. Morphological data with respect to soil and climate results showed that green-fleshed and Hayward varieties performed slightly better than Hayward grafted, which was most susceptible to diseases and heat damage.

The diagnosis and recommendation integrated system (DRIS) approach assesses plants’ nutrient status by comparing crop nutrient ratios with optimum values from a high-yielding group (DRIS norms). Furthermore, in this study, twenty-one grids were selected using GPS for soil sampling from a 1-hectare olive orchard field with a grid size of 20 × 20 m. Using standard laboratory procedures, soil samples were analyzed for macronutrients (N, P, and K ranging from 0.06–0.17%, 1.44–5.56%, and 31.94–120.32 mg kg−1) and micronutrients (Fe, Cu, Mn, Zn and B ranging from 25.16–82.3, 1.09–1.8, 26.96–65.69, 0.01–4.5 and 0.48–1.06 mg kg−1), respectively, and in plant samples for macronutrients (N, P and K ranging from 0.63–1.93, 0.01–0.16 and 0.75–1.37%) and micronutrients (Fe, Cu, Zn, Mn and B ranging from 152.5–621.5, 8.5–17, 18–34 and 43.5–113 mg kg−1), respectively. The critical ranges were investigated in terms of their relationship to the population’s yield level. DRIS norms derived from olives were able to detect nutrient deficiency and excess.

Horticultural crops have a special impact on a nation’s economy due to their significance in raising the living standards of farmers. The traditional method for crop protection is becoming ineffective with an increase in climate change effects. Precision Agriculture (PA) presents a solution to this issue through the precise monitoring and forecasting of pests, which improves productivity and guarantees environmental sustainability. The productivity of the existing plant production system can be increased by using precision agriculture techniques. Various PA technologies that enable farmers to monitor the pest include remote sensing, the Internet of Things, geographical information systems, and artificial intelligence. The PA technique assists with pest forecasting and the management of pests and diseases in plants. The content of this article was gathered through a literature review of recent research. The approaches used in PA for pest forecasting, monitoring, and management are the main topic of this study. In the long term, this study will help farmers to manage insect pests in a way that is both affordable and environmentally beneficial.

The number of spray deposits plays an important role in effective and efficient spraying. The spraying equipment is one of the most significant factors that affect the number of spray deposits. Therefore, the study was focused on the parameter optimization of a newly developed self-propelled variable height crop sprayer. Response surface methodology (RSM) along with Box–Behnken design (BBD) was used to study the effect of the independent variables (forward speed, spray height, and spray pressure) on response variables such as droplet density, coverage per-centage, and Volume Median Diameter (VMD). The experiment was conducted in the cotton field. Additionally, the RSM model was validated in this research. The results revealed that the coefficient of determination (R2) values was good for all response variables in the quadratic polynomial model. The optimized parameters were 6.5 km/h, 60 cm, 4 bar for fungicide application, and 8 km/h, 70 cm, 3 bar for insecticide and herbicide application. The predicted response variable values at the optimal conditions were 60.4 droplet/cm2, 27%, 230 µm for fungicides and 37.8 droplet/cm2, 19.1%, 225.4 µm for insecticide and herbicides application. The model validation is confirmed by the mean of actual response variable values at the optimal condition for insecticide and herbicides application, which was 41.35 ± 3.67 droplet/cm2, 21.10 ± 1.72%, 227.43 ± 1.22 µm, and the prediction error was 8.46%, 9.2%, and 0.9% for droplet density, coverage percentage, and VMD, respectively. This study can provide support for further optimizing the parameters of the sprayer.

Pepper is one of the most important vegetables and spices in the world. Principal pungency is contributed by secondary metabolites called capsaicinoids, mainly synthesized in the placenta of pepper fruit. Various factors, including drought, limit pepper production. Flowering is one of the most sensitive stages affected by drought stress. The current study was conducted to determine the effect of drought on different pepper genotypes at the flowering and pod formation stages. Hot pepper (Pusajuala and Ghotki) and Bell pepper (Green Wonder and PPE-311) genotypes were subjected to drought (35% field capacity) at two different stages (flowering (DF) and pod formation (DP) stage). In comparison, control plants were maintained at 65% field capacity. The data regarding flowering survival rates, antioxidant protein activity, and proline content, were collected. Results indicated that parameters like flower survival percentage, number of fruits per plant, and fruit weight had significant differences among the genotypes in both treatments. A high proline level was observed in Green Wonder at the pod formation stage compared to other genotypes. Capsaicin contents of hot pepper genotypes were affected at the pod formation stage. Antioxidants like GPX were highly active (190 units) in Ghotki at pod formation. Bell pepper genotypes had a high APX activity, highly observed (100 units) in PPE-311 at pod formation, and significantly differ from hot pepper genotypes. In the catalase case, all the genotypes had the highest values in DP compared to control and DF, but Pusajuala (91 units) and Green Wonder (83 units) performed best compared to other genotypes. Overall, the results indicate that drought stress decreased reproductive growth parameters and pungency of pepper fruit as most of the plant energy was consumed in defense molecules (antioxidants). Therefore, water availability at the flowering and pod formation stage is critical to ensure good yield and pepper quality.

Resources are limited, thus improving resource use efficiency is a key objective for cereal-based cropping systems. This field study was carried out to quantify resource use efficiencies in selected C3 and C4 cereals under split nitrogen (N) application regimes. The study included the following treatments: six cereals (three C3: wheat, oat, and barley; and three C4: maize, millet, and sorghum) and four split N application regimes (NS1 = full amount of N at sowing; NS2 = half N at sowing + half N at first irrigation; NS3 = ⅓ N at sowing + ⅓ N at first irrigation + ⅓ N at second irrigation; NS4 = ¼ N at sowing + ¼ N at first irrigation + ¼ N at second irrigation + ¼ N at third irrigation). Results revealed that C4 cereals out-yielded C3 cereals in terms of biomass production, grain yield, and resource use efficiencies (i.e., radiation use efficiency (RUE) and nitrogen use efficiency (NUE)), while splitting N into three applications proved to be a better strategy for all of the selected winter and summer cereals. The results suggest that C4 cereals should be added into existing cereal-based cropping systems and N application done in three installments to boost productivity and higher resource use efficiency to ensure food security for the burgeoning population.

Understanding the impact of the warming trend on phenological stages and phases of cotton (Gossypium hirsutum L.) in central and lower Punjab, Pakistan, may assist in optimizing crop management practices to enhance production. This study determined the influence of the thermal trend on cotton phenology from 1980–2015 in 15 selected locations. The results demonstrated that observed phenological stages including sowing (S), emergence (E), anthesis (A) and physiological maturity (M) occurred earlier by, on average, 5.35, 5.08, 2.87 and 1.12 days decade−1, respectively. Phenological phases, sowing anthesis (S-A), anthesis to maturity (A-M) and sowing to maturity (S-M) were reduced by, on average, 2.45, 1.76 and 4.23 days decade−1, respectively. Observed sowing, emergence, anthesis and maturity were negatively correlated with air temperature by, on average, −2.03, −1.93, −1.09 and −0.42 days °C−1, respectively. Observed sowing-anthesis, anthesis to maturity and sowing-maturity were also negatively correlated with temperature by, on average, −0.94, −0.67 and −1.61 days °C−1, respectively. Applying the cropping system model CSM-CROPGRO-Cotton model using a standard variety in all locations indicated that the model-predicted phenology accelerated more due to warming trends than field-observed phenology. However, 30.21% of the harmful influence of the thermal trend was compensated as a result of introducing new cotton cultivars with higher growing degree day (thermal time) requirements. Therefore, new cotton cultivars which have higher thermal times and are high temperature tolerant should be evolved.