Abstract: Progress in agricultural productivity and sustainability hinges on strategic investments in technological research. Evolving technologies such as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing are propelling the agricultural sector towards the transformative Agriculture 4.0 paradigm. The present systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore the usage of Machine Learning in agriculture. The study investigates the foremost applications of Machine Learning, including crop, water, soil, and animal management, revealing its important role in revolutionising traditional agricultural practices. Furthermore, it assesses the substantial impacts and outcomes of Machine Learning adoption and highlights some challenges associated with its integration in agricultural systems. This review not only provides valuable insights into the current landscape of Machine Learning applications in agriculture, but it also outlines promising directions for future research and innovation in this rapidly evolving field.

Abstract: Global drylands, covering about 41% of Earth’s surface and inhabited by 38% of the world’s population, are facing the stark challenges of water scarcity, low water productivity, and food insecurity. This paper highlights the major constraints to agricultural productivity, traditional irrigation scheduling methods, and associated challenges, efforts, and progress to enhance water use efficiency (WUE), conserve water, and guarantee food security by overviewing different smart irrigation approaches. Widely used traditional irrigation scheduling methods (based on weather, plant, and soil moisture conditions) usually lack important information needed for precise irrigation, which leads to over- or under-irrigation of fields. On the other hand, by using several factors, including soil and climate variation, soil properties, plant responses to water deficits, and changes in weather factors, smart irrigation can drive better irrigation decisions that can help save water and increase yields. Various smart irrigation approaches, such as artificial intelligence and deep learning (artificial neural network, fuzzy logic, expert system, hybrid intelligent system, and deep learning), model predictive irrigation systems, variable rate irrigation (VRI) technology, and unmanned aerial vehicles (UAVs) could ensure high water use efficiency in water-scarce regions. These smart irrigation technologies can improve water management and accelerate the progress in achieving multiple Sustainable Development Goals (SDGs), where no one gets left behind.

Abstract: The potential benefits of applying information and communication technology (ICT) in precision agriculture to enhance sustainable agricultural growth were discussed in this review article. The current technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), as well as their applications, must be integrated into the agricultural sector to ensure long-term agricultural productivity. These technologies have the potential to improve global food security by reducing crop output gaps, decreasing food waste, and minimizing resource use inefficiencies. The importance of collecting and analyzing big data from multiple sources, particularly in situ and on-the-go sensors, is also highlighted as an important component of achieving predictive decision making capabilities in precision agriculture and forecasting yields using advanced yield prediction models developed through machine learning. Finally, we cover the replacement of wired-based, complicated systems in infield monitoring with wireless sensor networks (WSN), particularly in the agricultural sector, and emphasize the necessity of knowing the radio frequency (RF) contributing aspects that influence signal intensity, interference, system model, bandwidth, and transmission range when creating a successful Agricultural Internet of Thing Ag-IoT system. The relevance of communication protocols and interfaces for presenting agricultural data acquired from sensors in various formats is also emphasized in the paper, as is the function of 4G, 3G, and 5G technologies in IoT-based smart farming. Overall, these research sheds light on the significance of wireless sensor networks and big data in the future of precision crop production

Abstract: Litchi is a highly favored fruit with high economic value. Mechanical automation of litchi picking is a key link for improving the quality and efficiency of litchi harvesting. Our research team has been conducting experiments to develop a visual-based litchi picking robot. However, in the early physical prototype experiments, we found that, although picking points were successfully located, litchi picking failed due to random obstructions of the picking points. In this study, the physical prototype of the litchi picking robot previously developed by our research team was upgraded by integrating a visual system for actively removing obstructions. A framework for an artificial intelligence algorithm was proposed for a robot vision system to locate picking points and to identify obstruction situations at picking points. An intelligent control algorithm was developed to control the obstruction removal device to implement obstruction removal operations by combining with the obstruction situation at the picking point. Based on the spatial redundancy of a picking point and the obstruction, the feeding posture of the robot was determined. The experiment showed that the precision of segmenting litchi fruits and branches was 88.1%, the recognition success rate of picking point recognition was 88%, the average error of picking point localization was 2.8511 mm, and an overall success rate of end-effector feeding was 81.3%. These results showed that the developed litchi picking robot could effectively implement obstruction removal.

Abstract: The invasion of agricultural diseases and insect pests is a huge difficulty for the growth of crops. The detection of diseases and pests is a very challenging task. The diversity of diseases and pests in terms of shapes, colors, and sizes, as well as changes in the lighting environment, have a massive impact on the accuracy of the detection results. We improved the C2F module based on DenseBlock and proposed DCF to extract low-level features such as the edge texture of pests and diseases. Through the sensitivity of low-level features to the diversity of pests and diseases, the DCF module can better cope with complex detection tasks and improve the accuracy and robustness of the detection. The complex background environment of pests and diseases and different lighting conditions make the IP102 data set have strong nonlinear characteristics. The Mish activation function is selected to replace the CBS module with the CBM, which can better learn the nonlinear characteristics of the data and effectively solve the problems of gradient disappearance in the algorithm training process. Experiments show that the advanced Yolov8 algorithm has improved. Comparing with Yolov8, our algorithm improves the MAP50 index, Precision index, and Recall index by 2%, 1.3%, and 3.7%. The model in this paper has higher accuracy and versatility.

Abstract: Biochar is commonly used to improve acidic soil and reduce nitrogen loss. However, the impact of biochar on soil nitrogen retention, especially at varying pH levels, is not fully understood. Soil samples were obtained from an acidic red soil citrus orchard. The soil pH was adjusted using CaO, with five levels (4.0, 5.1, 5.8, 6.6, and 7.2), and two biochar doses (0% and 1%) were applied. The study used 15N-Tracer and Ntrace to investigate biochar’s influence on soil nitrogen retention at different pH levels. The results showed that soil amendment with biochar improved gross mineralization rates (TM) and gross NH4+ immobilization rates (TI), except at pH 4.0 for TI. Biochar enhanced heterotrophic nitrification (ONrec) within pH 4.0–7.4, with a threshold for autotrophic nitrification (ONH4) at pH 6.4. The findings revealed biochar’s positive effect on soil nitrogen retention within pH 4.5–6.4. Biochar had a greater impact on TI than TM and inhibited ONH4, potentially enhancing nitrogen retention in this pH range. These results highlight the significance of considering biochar incorporation for improving nitrogen use efficiency and reducing NO3−-N loss in subtropical pomelo orchards.

Abstract: With the global population continuing to increase, the demand for food and energy has escalated, resulting in severe environmental pressures. Traditional methods of food and energy production have left a significant footprint on the environment, primarily due to the emission of greenhouse gases and a notable surge in waste production. Nevertheless, scientists have recently focused on developing sustainable solutions by managing biomass waste and converting it into useful products. Various biomass conversion technologies, including pyrolysis, gasification, and fermentation, have emerged to transform waste materials into valuable commodities like biofuels, fertilizers, and chemicals. These technologies present an alternative to conventional energy production methods and decrease reliance on non-renewable resources. Furthermore, the by-products generated through biomass conversion, such as biochar, possess utility as valuable soil amendments. This review emphasizes the potential of biomass conversion technologies in providing sustainable solutions for waste management, food and energy production, and reducing negative environmental impacts while providing valuable by-products for agricultural use. The focus is on Lebanon, which is facing a waste and energy crisis, with an aim to encourage and promote sustainable practices by highlighting different green waste management technologies. Focusing on the application of biochar in soil, our goal is to provide cost-effective and eco-friendly solutions to various agricultural and environmental challenges in Lebanon. This includes using biochar from biomass waste as a soil amendment to boost crop yields, remediate soil pollution, reduce soil drought stress, and address other related issues.

Abstract: Biotic and abiotic stress factors are pivotal considerations in agriculture due to their potential to cause crop losses, food insecurity, and economic repercussions. Zinc oxide nanoparticles (ZnO nanoparticles) have gained substantial attention from researchers worldwide for their capacity to alleviate the detrimental impacts of both biotic and abiotic stress on plants, concurrently reducing dependence on environmentally harmful chemicals. This article provides an overview of methods for synthesizing ZnO nanoparticles, encompassing physical vapor deposition, ball milling, hydrothermal methods, solvothermal methods, precipitation methods, microwave methods, microbial synthesis, and plant-mediated synthesis. Additionally, it delves into the absorption, translocation, and biotransformation pathways of ZnO nanoparticles within plants. The emphasis lies in elucidating the potential of ZnO nanoparticles to safeguard plants against biotic and abiotic stress, enhance plant performance, and modulate various plant processes. The article also offers a preliminary exploration of the mechanisms underlying plant stress tolerance mediated by ZnO nanoparticles. In conclusion, ZnO nanoparticles present an environmentally friendly and cost-effective strategy for plant stress management, paving the way for the integration of nanotechnology in sustainable agriculture. This opens new possibilities for leveraging nanotechnology to bolster plant resilience against stress in the ever-changing climate conditions, ensuring global food security.

Abstract: Salinity is one of the most threatening abiotic stresses to agricultural production, alarmingly expanding both through natural salinization phenomena and anthropogenic activities in recent times. The exploration of sustainable and eco-friendly strategic approaches for mitigating the negative impact of salinity on food crops is of vital importance for future food security. Therefore, our study aimed to evaluate zinc oxide nanoparticles (ZnO-NPs) as potent salinity mitigators in maize (Zea mays L.). Three ZnO-NPs foliar treatments (i.e., 0, 50, and 100 mg/L) were applied 40, 55, and 70 days after sowing on maize plants exposed to continuous salinities of 0 mM NaCl (S0), 60 mM NaCl (S1), and 120 mM NaCl (S3) in a semi-automated greenhouse facility. Results showed that the highest salinity (i.e., 120 mM NaCl) significantly affected plant growth attributes, physiological performance, nutrient profiles, antioxidant activity, plant yield, and yield-contributing characteristics of maize plants. Thus, 120 mM NaCl resulted in −53% number of grains per cob (NG), −67% grains weight per cob (GW), −36% 100-grains weight (HGW), and −72% grain yield per plant (GY) compared to controls. However, foliar treatment of maize plants with ZnO-NPs successfully mitigated salinity and significantly improved all studied parameters, except transpiration rate (TR) and intrinsic water use efficiency (iWUE). Foliar application of 100 mg/L of ZnO-NPs alleviated NG, GW, HGW, and GY by 31%, 51%, 13%, and 53%, respectively. Furthermore, principal component analysis (PCA) and Pearson’s correlation further strengthened the significance of ZnO-NP application as salinity mitigators.

Abstract: Unmanned farms employ a variety of sensors, automated systems, and data analysis techniques to enable fully automated and intelligent management. This not only heightens agricultural production efficiency but also reduces the costs associated with human resources. As integral components of unmanned farms’ automation systems, agricultural UAVs have been widely adopted across various operational stages due to their precision, high efficiency, environmental sustainability, and simplicity of operation. However, present-day technological advancement levels and relevant policy regulations pose significant restrictions on UAVs in terms of payload and endurance, leading to diminished task efficiency when a single UAV is deployed over large areas. Accordingly, this paper aggregates and analyzes research pertaining to UAV swarms from databases such as Google Scholar, ScienceDirect, Scopus, IEEE Xplorer, and Wiley over the past decade. An initial overview presents the current control methods for UAV swarms, incorporating a summary and analysis of the features, merits, and drawbacks of diverse control techniques. Subsequently, drawing from the four main stages of agricultural production (cultivation, planting, management, and harvesting), we evaluate the application of UAV swarms in each stage and provide an overview of the most advanced UAV swarm technologies utilized therein. Finally, we scrutinize and analyze the challenges and concerns associated with UAV swarm applications on unmanned farms and provide forward-looking insights into the future developmental trajectory of UAV swarm technology in unmanned farming, with the objective of bolstering swarm performance, scalability, and adoption rates in such settings.

Abstract: To enhance the efficiency of mechanical automatic picking of cherry tomatoes in a precision agriculture environment, this study proposes an improved target detection algorithm based on YOLOv5n. The improvement steps are as follows: First, the K-means++ clustering algorithm is utilized to update the scale and aspect ratio of the anchor box, adapting it to the shape characteristics of cherry tomatoes. Secondly, the coordinate attention (CA) mechanism is introduced to expand the receptive field range and reduce interference from branches, dead leaves, and other backgrounds in the recognition of cherry tomato maturity. Next, the traditional loss function is replaced by the bounding box regression loss with dynamic focusing mechanism (WIoU) loss function. The outlier degree and dynamic nonmonotonic focusing mechanism are introduced to address the boundary box regression balance problem between high-quality and low-quality data. This research employs a self-built cherry tomato dataset to train the target detection algorithms before and after the improvements. Comparative experiments are conducted with YOLO series algorithms. The experimental results indicate that the improved model has achieved a 1.4% increase in both precision and recall compared to the previous model. It achieves an average accuracy mAP of 95.2%, an average detection time of 5.3 ms, and a weight file size of only 4.4 MB. These results demonstrate that the model fulfills the requirements for real-time detection and lightweight applications. It is highly suitable for deployment in embedded systems and mobile devices. The improved model presented in this paper enables real-time target recognition and maturity detection for cherry tomatoes. It provides rapid and accurate target recognition guidance for achieving mechanical automatic picking of cherry tomatoes.

Abstract: Soil heavy metal pollution caused by human activities has become one of the most critical environmental issues with a global concern. Phytoremediation is widely used due to its low cost and environmental friendliness. However, the impact of heavy metals on plant growth remains unclear. This study investigated the effects on the growth and photosynthetic activity of Picris divaricata Vant. under different cadmium concentrations using a hydroponics cultivation system. The results showed that the growth and photosynthetic processes of P. divaricata exhibited a phenomenon of promotion in low Cd concentrations and inhibition in high Cd concentrations. Under a low to medium Cd concentration (≤25 μM), there was no Cd toxicity in terms of plant growth, but high concentrations of Cd inhibited plant growth. The Fe content of leaves gradually increased as the Cd concentration increased; it reached 201.8 mg kg−1 in 75 μM Cd. However, there was no significant difference in Mn between the 75 μM Cd treatment and the control (p > 0.05). The contents of carotenoid ranged between 3.06 and 3.26 mg/g across the different Cd treatments, showing no significant differences. The treatment with 5–75 μM Cd did not directly affect the photosynthesis of P. divaricata. Higher Cd concentrations reduced the stomatal density on the of P. divaricata leaves, resulting in stomatal and mesophyll conductance limitations, indirectly affecting P. divaricata photosynthesis. These research results provide a reference for evaluating and selecting heavy metal tolerant plants and provide environmentally friendly approaches to remediate heavy metal pollution.

Abstract: Motivated by the ongoing debate on food security and the global trend of adopting new emerging technologies in the aftermath of COVID-19, this research focuses on the challenges and opportunities of agriculture digitalization in Spain. This process of digital transformation of the agricultural sector is expected to significantly affect productivity, product quality, production costs, sustainability and environmental protection. For this reason, our study reviews the legal, technical, infrastructural, educational, financial and market challenges that can hinder or impose barriers to the digitalization of agriculture in Spain. In addition, the opportunities that digitalization can bring are identified, with the intention of contributing to provide insights that helps strengthen the Spanish agricultural model and make the necessary decision so that professionals in the sector are prepared to adapt to this intense change.

Abstract: Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispectral data, thermal infrared data, fluorescence, and multi-source data fusion) in remote sensing monitoring of rice diseases and pests. Secondly, we summarize current methods for monitoring rice diseases and pests, including statistical discriminant type, machine learning, and deep learning algorithm. Finally, we provide a general framework to facilitate the monitoring of rice diseases or pests, which provides ideas and technical guidance for remote sensing monitoring of unknown diseases and pests, and we point out the challenges and future development directions of rice disease and pest remote sensing monitoring. This work provides new ideas and references for the subsequent monitoring of rice diseases and pests using remote sensing.

Abstract: Plastic film mulching has been widely used to improve crop yield and water use efficiency, although the effects of plastic film mulching on water, heat, nitrogen dynamics, and crop growth are rarely presented comprehensively. This study investigated a large number of studies in film mulching fields from the past 10 years (mostly from 2019 to 2023) and summarized the impact of plastic film mulching, progress in modeling with film mulching, and future research directions. The effects of plastic film mulching were intricate and were influenced by film mulching methods, irrigation systems, crop types, crop growth stages, etc. Overall, plastic film mulching showed a positive effect on improving soil water, temperature, and nitrogen status, enhancing crop transpiration and photosynthetic rates, and promoting crop growth and yield, although film mulching may have negative effects, such as increasing rainfall interception, blocking water entering the soil, and reducing net radiation income. The crop yield and water use efficiency could increase by 39.9–84.7% and 45.3–106.4% under various film mulching methods. Coupled models of soil water and heat transport and crop growth under plastic film mulching conditions have been established by considering the effects of plastic film mulching on the upper boundary conditions of soil water and heat, energy budget and distribution processes, and the exchange of latent and sensible heat between soil and atmosphere. The models have good applicability in film mulched farmland of maize, rice, and potato for different regions of China. Further development is needed for soil water, heat, nitrogen migration, and crop growth models under different plastic film mulching methods, and the acquisition of soil and crop indicators under plastic film mulching conditions based on big data support. The study will provide reference for the subsequent development and innovation of plastic film mulching technology.

Abstract: Biofertilizers and organic fertilizers are eco-friendly treatments that reduce the consumption and problems associated with chemical fertilizers. The aim of this research was to investigate the effects of biofertilizers and organic fertilizers on reducing consumption and improving the effectiveness of chemical fertilizer treatments by comparing the growth parameters, yield, quality criteria, and nutrient concentration in lettuce and broccoli grown under greenhouse conditions. The biofertilizer (BM-MegaFlu®) comprised Bacillus megaterium, Pseudomonas fluorescens, and Pantoea agglomerans bacteria. The experiment consisted of six treatments comprising (1) biofertilizer (BF), (2) chemical fertilizer + biofertilizer (CF + BF), (3) chemical fertilizer (CF), (4) CF (1/2 dose) + BF, (5) CF (1/3 dose) + BF, and (6) organic fertilizer (OF + BF). BF did not adversely affect the head height and root collar diameter of lettuce; on the contrary, it showed non-significant differences with CF + BF, BF, CF (1/2) + BF, and CF (1/3) + BF treatments and CF alone. The highest total and marketable yields were obtained from CF + BF, CF, CF (1/2) + BF treatments in lettuce. The total yield was the highest in the CF + BF, CF, CF (1/2) + BF, and CF (1/3) + BF treatments in broccoli. In conclusion, the biofertilizer had a supportive effect on the use of chemical fertilizers in lettuce and broccoli production, especially the CF (1/2) + BF treatment in lettuce. The CF (1/2) + BF and CF (1/3) + BF treatments in broccoli showed similar yields to CF. In both crops, BF could provide 50% chemical fertilizer savings.

Abstract: The maize field environment is complex. Weeds and maize have similar colors and may overlap, and lighting and weather conditions vary. Thus, many methods for the automated differentiation of maize and weeds achieve poor segmentation or cannot be used in real time. In this paper, a weed recognition model based on improved Swin-Unet is proposed. The model first performs semantic segmentation of maize seedlings and uses the resulting mask to identify weeds. U-Net acts as the semantic segmentation framework, and a Swin transformer module is introduced to improve performance. DropBlock regularization, which randomly hides some blocks in crop feature maps, is applied to enhance the generalization ability of the model. Finally, weed areas are identified and segmented with the aid of an improved morphological processing algorithm. The DeepLabv3+, PSANet, Mask R-CNN, original Swin-Unet, and proposed models are trained on a dataset of maize seedling images. The proposed Swin-Unet model outperforms the others, achieving a mean intersection over union of 92.75%, mean pixel accuracy of 95.57%, and inference speed of 15.1 FPS. Our model could be used for accurate, real-time segmentation of crops and weeds and as a reference for the development of intelligent agricultural equipment.

Abstract: A three-year field experiment was carried out to assess the efficacy of various tillage and residue management practices, as well as weed management approaches, in a rice–wheat–green gram rotation. The treatments included: conventional till transplanted rice–conventional till wheat–fallow (T1); conventional till transplanted rice–zero-till wheat–zero-till green gram (T2); conventional till direct-seeded rice—conventional-till wheat—zero-till green gram (T3); zero-till direct-seeded rice—zero-till wheat—zero-till green gram (T4); zero-till direct-seeded rice + residue zero-till wheat + residue zero-till green gram (T5). In weed management, three treatments are as follows: recommended herbicides (W1); integrated weed management (W2); and unweeded (W3). The integrated weed management treatment had the lowest weed biomass, which was 44.3, 45.3, and 33.7% lower than the treatment W3 at 30 and 60 days after sowing and harvest, respectively. T1 grain and straw yielded more than T2 in the early years than in subsequent years. The conventional till transplanted rice–zero-till wheat–zero-till green gram system produced 33.6, 37.6, and 27.7% greater net returns than the zero-till direct-seeded rice—zero-till wheat—zero-till greengram system, respectively. Conventional till transplanted rice–conventional till wheat–fallow had the biggest reduction (0.41%) in soil organic carbon from the initial value. The findings of the study demonstrated that adopting the transplanting method for rice, followed by zero tillage for wheat and green gram, enhanced productivity and profitability, while simultaneously preserving soil health.

Abstract: To overcome food security, sustainable strategies for reclamation and the subsequent utilization of salt-affected soils for crop production are needed. The aim of the current study was to evaluate the impacts of compost and biochar addition on the growth and fruit quality of tomato under salinity stress. For this purpose, the soil was spiked with analytical grade sodium chloride to achieve a 6 dS m−1 salinity level for a pot experiment. After 30 days of spiking, the compost (2%) and biochar (2%) were added in selected pots. After the seedling transplant, recommended doses of NPK were added to fulfill nutrient requirements of tomato plants. Plants were harvested after 90 days of seedling transplantation. Results revealed that the salinity caused a significant reduction of 28.4% in SPAD value, 23.5% in Ft, 22.6% in MSI, 12.1% in RWC, 18.3% in Chl. a, 13.7% in Chl. b, and 16.5% in T. Chl. as compared to the un-amended non-saline control in physiological attributes of tomato plants. Similarly, a significant decrease of 26.9–44.1% was obtained in growth attributes of tomato as compared to the non-saline control. However, in saline soil, the addition of biochar and compost (alone or together) demonstrated a significant improvement in plant growth (i.e., up 45%) over the respective un-amended control. Moreover, the combined application of compost and biochar significantly reduced the sodium (Na+) in shoots and roots of tomato plants by 40% and 47%, respectively, over the respective control. Our findings suggest that the combined application of biochar and compost could be useful to reduce salinity, alleviate salinity-induced phytotoxicity, and subsequently improve plant growth and productivity in salt-affected soil.

Abstract: This study aimed to clarify the effect of phosphorus fertilizer on the senescence and yield of Tartary buckwheat under low-nitrogen treatment. A two-year field experiment to investigate the characteristics was conducted on Tartary buckwheat (Qianku 5) under four phosphorus fertilizer application rates, 0(CK), 40(LP), 80(MP), and 120 kg·ha−1 (HP), in the absence of nitrogen treatment. Compared with CK, MP treatment increased the plant height, node number of main stem, branch number of main stem, root-morphology items, root activity, enzyme activity related to root nitrogen metabolism, leaf chlorophyll content, and antioxidant enzyme activity by an average of 27.82%, 36.00%, 31.76%, 70.63%, 103.16%, 45.63%, 19.42%, and 45.48%, respectively. MP treatment significantly decreased the malondialdehyde content by 23.54% compared with that of CK. Among all treatments, the HP treatment had the highest content. The grain number per plant, grain weight per plant, and yield under MP treatment were 1.54, 1.65, and 1.53 times those of CK, respectively. In summary, the appropriate phosphate fertilizer treatment (80 kg·ha−1) can delay senescence, promote the growth, and increase the yield of Tartary buckwheat at low nitrogen levels. Such treatment is recommended for use in production to jointly achieve the high yield and high nitrogen conservation of Tartary buckwheat.

Abstract: In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing “crop management”, “soil management”, and “water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management.

Abstract: Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in apple trees and can be applied to assess their growth status. Hyperspectral data can provide an important means for detecting the LCC in apple trees. In this study, hyperspectral data and the measured LCC were obtained. The original spectrum (OR) was pretreated using some spectral transformations. Feature bands were selected based on the competitive adaptive reweighted sampling (CARS) algorithm, random frog (RF) algorithm, elastic net (EN) algorithm, and the EN-RF and EN-CARS algorithms. Partial least squares regression (PLSR), random forest regression (RFR), and the CatBoost algorithm were used before and after grid search parameter optimization to estimate the LCC. The results revealed the following: (1) The spectrum after second derivative (SD) transformation had the highest correlation with LCC (–0.929); moreover, the SD-based model produced the highest accuracy, making SD an effective spectrum pretreatment method for apple tree LCC estimation. (2) Compared with the single band selection algorithm, the EN-RF algorithm had a better dimension reduction effect, and the modeling accuracy was generally higher. (3) CatBoost after grid search optimization had the best estimation effect, and the validation set of the SD-EN-CARS-CatBoost model after parameter optimization had the highest estimation accuracy, with the determination coefficient (R2), root mean square error (RMSE), and relative prediction deviation (RPD) reaching 0.923, 2.472, and 3.64, respectively. As such, the optimized SD-EN-CARS-CatBoost model, with its high accuracy and reliability, can be used to monitor the growth of apple trees, support the intelligent management of apple orchards, and facilitate the economic development of the fruit industry.

Abstract: The non-judicious use of water at the farm level in traditional irrigation application methods is a present-day concern across the world that can be resolved by enhancing application efficiency through the adoption of advanced irrigation techniques. Sprinkler irrigation is a method that has high application efficiency, which can be further increased when coupled with automation toward precision irrigation. The objectives of this review are to summarize the main aspects of sprinkler and precision irrigation and their development, scope, and future prospects specifically in Asian countries. In this paper, a modified methodology, inspired by PRISMA guidelines, was used to explore the available literature to summarize the existing knowledge in the field. Regarding the technological aspects of the analyzed works, it became evident that sprinkler systems are an efficient method to not only irrigate crops (with 39% water saving) but also for the application of fertilizers with higher efficiency (>35%) and water productivity (>14.1%) compared with gravity irrigation systems. Moreover, this paper highlights the prominent features of precision irrigation for maximizing agricultural productivity. The use of sprinkler irrigation with precision applications using automation with a sensor-based mechanism for field data collection, data transformation, data analysis, and operation of IoT-based automatic solenoid valves can save 20–30% more irrigation water and increase crop yield by 20–27%. An analytical understanding and knowledge of the field were used to draw conclusions that are thought-provoking for scientists, researchers, and other stakeholders.

Abstract: Intensive irrigation and nutrient management practices in agriculture have given rise to serious issues in aquifer water depletion and groundwater quality. This review discusses the effects of irrigation and nitrogen management practices on potato growth, yield, and quality, and their impacts on water and nitrogen use efficiencies. This review also highlights the economics and consequences of applying deficit irrigation strategies in potato production. Many researchers have demonstrated that excessive irrigation and nitrogen application rates negatively impact potato tuber yield and quality while also increasing nitrate leaching, energy consumption, and the overall costs of production. An application of light-to-moderate deficit irrigation (10–30% of full irrigation) together with reduced nitrogen rates (60–170 kg/ha) has a great potential to improve water and nitrogen use efficiencies while obtaining optimum yield and quality in potato production, depending on the climate, variety, soil type, and water availability. There is an opportunity to reduce N application rates in potato production through deficit irrigation practices by minimizing nitrate leaching beyond the crop root zone. The best irrigation and nitrogen management techniques for potato production, as discussed in this review, include using sprinkle and drip irrigation techniques, irrigation scheduling based on local crop coefficients, soil moisture content, and crop modeling techniques, applying slow-release nitrogenous fertilizers, split nitrogen application, and applying water and nitrogenous fertilizers in accordance with crop growth stage requirements.

Abstract: Global rice crop production is being threatened by a frequent rise in high temperatures and drought. Drought and heat stresses adversely affect the morphological, physiological, and biochemical characteristics of rice, resulting in reduced crop productivity. Heat and drought stresses entail physiological changes in rice plants, such as stomata closure, reduced photosynthesis, loss of turgor adjustment, and reduction in crop productivity. These stresses also cause metabolic changes by increasing the activities of antioxidative enzymes, phytohormones, abscisic acid, reactive oxygen species, and reactive stress metabolites. Among the different growth stages of rice, the reproductive stage is the most sensitive stage to high temperature and drought, resulting in low seed setting and grain yield. Genetic improvement and development of drought and heat-stress-tolerant rice varieties increase seed setting and enhance yield production even under stress conditions. Because of the multigenic nature of traits, the development of drought and high-temperature-tolerant varieties through genetic improvement is the best approach. Here, we summarized the effects of heat and drought stresses on the physiological traits of rice. We focused on different approaches to managing high-temperature and drought stresses, such as an adjustment in cultural practices, genetic improvement through molecular breeding, and the development of transgenics and chemical spray from an agricultural practice perspective.

Abstract: As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0.

Abstract: Agrivoltaic systems, which combine the cultivation of crops with solar panel installations, offer a novel solution to the dual challenges of energy production and agricultural productivity. This research verifies the impact of agrivoltaic (APV) conditions on the growth and quality of garlic and kimchi cabbage over two consecutive years in Naju-si, Jeollanam Province, Republic of Korea. In the 2019–2020 cultivation season, both kimchi cabbage and garlic grown under APV conditions experienced weight reductions of 18% and 15%, respectively, when compared to those grown in conventional settings. Intriguingly, despite the altered light conditions of APV leading to microenvironmental changes (mainly 41% light reduction), the quality of these crops, particularly in terms of their sulfur compound concentrations, remained consistent. This suggests that there was no discernible difference in the sensory quality of APV-grown kimchi cabbage and garlic compared to their traditionally grown counterparts. These findings highlight the potential of APV systems in promoting sustainable agriculture by balancing both crop yield and quality. Based on these results, the study suggests three innovative cultivation techniques to enhance crop growth in APV environments.

Abstract: The smart farm is currently a hot topic in the agricultural industry. Due to the complex field environment, the intelligent monitoring model applicable to this environment requires high hardware performance, and there are difficulties in realizing real-time detection of ripe strawberries on a small automatic picking robot, etc. This research proposes a real-time multistage strawberry detection algorithm YOLOv5-ASFF based on improved YOLOv5. Through the introduction of the ASFF (adaptive spatial feature fusion) module into YOLOv5, the network can adaptively learn the fused spatial weights of strawberry feature maps at each scale as a way to fully obtain the image feature information of strawberries. To verify the superiority and availability of YOLOv5-ASFF, a strawberry dataset containing a variety of complex scenarios, including leaf shading, overlapping fruit, and dense fruit, was constructed in this experiment. The method achieved 91.86% and 88.03% for mAP and F1, respectively, and 98.77% for AP of mature-stage strawberries, showing strong robustness and generalization ability, better than SSD, YOLOv3, YOLOv4, and YOLOv5s. The YOLOv5-ASFF algorithm can overcome the influence of complex field environments and improve the detection of strawberries under dense distribution and shading conditions, and the method can provide technical support for monitoring yield estimation and harvest planning in intelligent strawberry field management.

Abstract: The guava plant is widely cultivated in various regions of the Sub-Continent and Asian countries, including Bangladesh, due to its adaptability to different soil conditions and climate environments. The fruit plays a crucial role in providing food security and nutrition for the human body. However, guava plants are susceptible to various infectious leaf diseases, leading to significant crop losses. To address this issue, several heavyweight deep learning models have been developed in precision agriculture. This research proposes a transfer learning-based model named GLD-Det, which is designed to be both lightweight and robust, enabling real-time detection of guava leaf disease using two benchmark datasets. GLD-Det is a modified version of MobileNet, featuring additional components with two pooling layers such as max and global average, three batch normalisation layers, three dropout layers, ReLU as an activation function with four dense layers, and SoftMax as a classification layer with the last lighter dense layer. The proposed GLD-Det model outperforms all existing models with impressive accuracy, precision, recall, and AUC score with values of 0.98, 0.98, 0.97, and 0.99 on one dataset, and with values of 0.97, 0.97, 0.96, and 0.99 for the other dataset, respectively. Furthermore, to enhance trust and transparency, the proposed model has been explained using the Grad-CAM technique, a class-discriminative localisation approach.