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The foundation of effectively predicting plant disease in the early stage using deep learning algorithms is ideal for addressing food insecurity, inevitably drawing researchers and agricultural specialists to contribute to its effectiveness. The input preprocessor, abnormalities of the data (i.e., incomplete and nonexistent features, class imbalance), classifier, and decision explanation are typical components of a plant disease detection pipeline based on deep learning that accepts an image as input and outputs a diagnosis. Data sets related to plant diseases frequently display a magnitude imbalance due to the scarcity of disease outbreaks in real field conditions. This study examines the effects of several preprocessing methods and class imbalance approaches and deep learning classifiers steps in the pipeline for detecting plant diseases on our data set. We notably want to evaluate if additional preprocessing and effective handling of data inconsistencies in the plant disease pipeline may considerably assist deep learning classifiers. The evaluation's findings indicate that contrast limited adaptive histogram equalization (CLAHE) combined with image sharpening and generative adversarial networks (GANs)-based approach for resampling performed the best among the preprocessing and resampling techniques, with an average classification accuracy of 97.69% and an average F1-score of 97.62% when fed through a ResNet-50 as the deep learning classifier. Lastly, this study provides a general workflow of a disease detection system that allows each component to be individually focused on depending on necessity. [ABSTRACT FROM AUTHOR]

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production. [ABSTRACT FROM AUTHOR]

The airflow discharged from orchard airblast sprayers is a primary component for successfully carrying spray droplets to the target trees. Because of the variation in orchard tree canopies, control of the airflow to minimize off-target loss during spray application is essential. An automatic airflow control system for precision sprayers was developed to maximize spray droplet coverage on targets and minimize off-target loss while considering the tree canopy densities. The primary component of the system was an iris damper, which was designed as a retrofit attachment on the fan inlet of a threepoint airblast intelligent sprayer. A 3D light detection and ranging (LiDAR) sensor was installed at the top of the sprayer to acquire the tree canopy data. A motor was employed to control the damper opening with a micro-controller. To develop the models required for automatic airflow control, field experiments were conducted at three canopy density orchards with different cultivars (GoldRush, Gala, and Fuji). A total of 15 trees (five trees from each cultivar) were randomly selected, and five different damper openings (openings 1, 2, 3, 4, and 5) were tested for each tree. Opening 1 represented the same air inlet as a traditional precision airblast sprayer, while openings 2, 3, 4, and 5 were the sequentially reduced air inlets of the sprayer. A canopy density measurement algorithm was scripted to measure the canopy point density of individual trees. Three models were built to show relationships between (1) tree canopy point densities and airflows; (2) canopy densities and damper openings; and (3) damper opening and motor steps. The combination of the two models (2 & 3) was used to assess the amount of airflow required for a specific canopy density. Field validations for medium and high-density trees showed that the system achieved adequate spray penetration at the top, middle, bottom, back-left, and back-right positions of the tree sections and reduced off-target loss at the ground and edge of next row sections using openings 4 and 2, respectively. However, the mechanical motion of the damper required 3 s to move from minimum to maximum opening, so the average canopy density was recommended to control the airflow. The overall results suggested that the automatic airflow control system could reduce spray drift and off-target losses and improve spray application efficiency in orchards. [ABSTRACT FROM AUTHOR]

Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL's state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain. [ABSTRACT FROM AUTHOR]

Over the years, the household sector has become an important energy consumer and the main source of greenhouse gas (GHG) emissions. The rural household sector has significant potential for emission reduction due to its heavy reliance on traditional fuels and technologies. A great number of academic studies have been undertaken to analyze patterns of household energy and their determinants around the globe, particularly in developing countries. However, little is known about the association between household dynamics and patterns of energy (biomass vs. non-renewable) use. This study aims to analyze the relationship between different household dynamics, such as household size, income, climate, availability of resources, markets, awareness, consumption of energy, and carbon emissions. The study uses the STIRPAT model to investigate the impact of income, household size, housing dimensions, clean energy, and market accessibility on energy consumption. The findings of the study reveal that biomass energy accounts for the majority of household energy consumption and dung has the highest share in total household energy consumption (39.11%) The consumption of biomass increased with the size of the household and decreased with the level of income. A 1 kgoe increase in biomass consumption resulted in a 15.355 kg increase in CO2 emissions; on the other hand, a 1 kgoe increase in non-renewable-energy consumption resulted in just a 0.8675 kg increase in CO2 emissions. The coefficients of housing unit size, distance from the LPG market, and livestock were the primary determinants for choosing any fuel. Having knowledge of modern cookstoves, clean energy, and the environmental impact of fuels reduced the consumption of both energy sources. Furthermore, it was found that households with a greater reliance on biomass emitted higher quantities of carbon compared to those with a low reliance on biomass. Based on the results of the study, it can be stated that a reduction in the use of biomass and non-renewable energy is possible with adequate interventions and knowledge. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Robotic pruning is a potential solution to reduce orchard labor and associated costs. Collision-free path planning of the manipulator is essential for successful robotic pruning. This simulation study investigated the collision-free branch accessibility of a six rotational (6R) degrees of freedom (DoF) robotic manipulator with a shear cutter end-effector. A virtual environment with a simplified tall spindle tree canopy was established in MATLAB. An obstacle-avoidance algorithm, rapidly-exploring random tree (RRT), was implemented for establishing collision-free paths to reach the target pruning points. In addition, path smoothing and optimization algorithms were used to reduce the path length and calculate the optimized path. Two series of simulations were conducted: (1) performance and comparison of the RRT algorithm with and without smoothing and optimization, and (2) performance of collision-free path planning considering different approach poses of the end-effector relative to the target branch. The simulations showed that the RRT algorithm successfully avoided obstacles and allowed the manipulator to reach the target point with 23 s average path finding time. The RRT path length was reduced by about 28% with smoothing and by 25% with optimization. The RRT smoothing algorithm generated the shortest path lengths but required about 1 to 3 s of additional computation time. The lowest coefficient of variation and standard deviation values were found for the optimization method, which confirmed the repeatability of the method. Considering the different end-effector approach poses, the simulations suggested that successfully finding a collision-free path was possible for branches with no existing path using the ideal (perpendicular cutter) approach pose. This study provides a foundation for future work on the development of robotic pruning systems. [ABSTRACT FROM AUTHOR]

The water resources are depleting rapidly due to the increasing population of the world. Thus, it has become a big challenge to provide sustainable quality water for future generations. Unfortunately, due to decreasing rivers/canals water supply, the trend of groundwater pumping has increased for agricultural purposes during the last few decades especially in developing countries of Africa and Asia like Pakistan. The freshwater-scarce areas in the world are now relying on water desalination processes. In the recent era, the desalination has emerged as a potential method to produce freshwater. Although the process of desalination is an acceptable way to convert saline to freshwater, this is a highly electrical energy-intensive process. At the same time, the availability of enormous daily solar energy in many parts of the world provides an excellent opportunity to operate reverse osmosis (RO) systems. The hybrid solar system seems to be a feasible solution for continuous water desalination throughout the day. Thus, this study was carried out for the development and experimental investigation of a 500 L h–1 decentralized photovoltaic (PV-RO) system. Based on the running load of the RO system, a 2 kWp PV system was coupled with RO plant through 5 kVA hybrid inverter. The experiments were conducted in terms of no tracking and three-point manual PV tracking, cooling, and no-cooling of the PV system. The results showed 18% higher daily PV energy using PV tracking and 10% higher PV energy by cooling of PV panels. The sizing and development process of this system is helpful for easier selection and installation of a decentralized hybrid PV-RO system to perform environmental friendly desalination of water by the community to meet the future challenges of water scarcity. [ABSTRACT FROM AUTHOR]

The present study was undertaken to investigate the effect of deficit irrigation (DI) on wheat grain yield under different sowing methods. The field experiment was layout using randomized complete block design (RCBD) with split plot arrangements by keeping four DI regimes (I1, I2, I3, I4 as 0%, 20%, 35% and 50% deficit respectively) and two sowing methods (bed sowing and drill sowing) in sub plots and main plots respectively with three repeats. Research also evaluated the ability of AquaCrop model to simulate wheat gain yield and biological yield under full and deficit water conditions in a semi-arid environment. The model was calibrated for the full irrigation treatments under both sowing method and remaining six treatments were used for validation purpose. The results showed that the maximum grain yield was recorded 5.724 tons/ha for 0% DI treatment under drill sowing method. The DI (I1, I2, I3, and I4) levels under drill sowing method gave 13.12%, 14.28%, 16.38%, and 19.59% more grain yield than the corresponding DI levels (I1, I2, I3, and I4) under bed sowing method respectively, whereas the average crop water use efficiency for I1, I2, I3, and I4 treatments under bed sowing was found higher than the corresponding treatments under drill sowing method by 17.94%, 14.34%, 9.96%, and 5.36% respectively. AquaCrop model simulated gain yields and biological yield showed a good agreement with measured values of both gain yield (RMSE=0.25ton ha-1, NRMSE= 5.41, d=0.96 and NSE=0.79) and biological yield (RMSE=0.59 ton ha-1, NRMSE= 5.36, d=0.94 and NSE=0.74). The high values of the statistical indicators confirmed that the AquaCrop model (v3.0) can simulate wheat yield under no to mild water stress conditions which makes it very useful for evaluating the deficit irrigation strategies under different cultural and management practices with minimal input data requirements and ease of use. [ABSTRACT FROM AUTHOR]

The article focuses on solar desalination system using evaporation condensation & heat recovery method, adopted in Pakistan, and talks of increase in daily specific fresh water productivity, water quality standards as per the organization World Health Organization (WHO), and cost of bottled water.

Reducing risk from pesticide applications has been gaining serious attention in the last few decades due to the significant damage to human health, environment, and ecosystems. Pesticide applications are an essential part of current agriculture, enhancing cultivated crop productivity and quality and preventing losses of up to 45% of the world food supply. However, inappropriate and excessive use of pesticides is a major rising concern. Precision spraying addresses these concerns by precisely and efficiently applying pesticides to the target area and substantially reducing pesticide usage while maintaining efficacy at preventing crop losses. This review provides a systematic summary of current technologies used for precision spraying in tree fruits and highlights their potential, briefly discusses factors affecting spraying parameters, and concludes with possible solutions to reduce excessive agrochemical uses. We conclude there is a critical need for appropriate sensing techniques that can accurately detect the target. In addition, air jet velocity, travel speed, wind speed and direction, droplet size, and canopy characteristics need to be considered for successful droplet deposition by the spraying system. Assessment of terrain is important when field elevation has significant variability. Control of airflow during spraying is another important parameter that needs to be considered. Incorporation of these variables in precision spraying systems will optimize spray decisions and help reduce excessive agrochemical applications. [ABSTRACT FROM AUTHOR]

Agriculture of Pakistan relies on the Indus basin, which is facing severe water scarcity conditions. Poor irrigation practices and lack of policy reforms are major threats for water and food security of the country. In this research, alternative water-saving strategies are evaluated through a high spatio-temporal water footprint (WF) assessment (1997–2016) for the Punjab and Sindh provinces, which cover an irrigated area of 17 million hectares in the Indus basin of Pakistan. The SPARE:WATER model is used as a spatial decision support tool to calculate the WF and establish alternative management plans for more sustainable water use. The average water consumption (WFarea) is estimated to 182 km3 yr−1, composed of 75% blue water (irrigation water from surface water and groundwater sources), 17% green water (precipitation) and 8% grey water (water used to remove soil salinity or dilute saline irrigation water). Sugarcane, cotton, and rice are highly water-intensive crops, which consume 57% of the annual water use. However, WFarea can be reduced by up to 35% through optimized cropping patterns of the existing crops with the current irrigation settings and even by up to 50% through the combined implementation of optimal cropping patterns and improved irrigation technologies, i.e., sprinkler and drip irrigation. We recommend that the economic impact of these water-saving strategies should be investigated in future studies to inform stakeholders and policymakers to achieve a more sustainable water policy for Pakistan. [ABSTRACT FROM AUTHOR]