Abstract Pesticides are contributing in current agriculture to fulfil the need of raising population. Uses of pesticides are not limited to agriculture, but they are also used to control over domestic pests, disease insect vectors and home gardening. But they are very toxic in nature and pose acute risks on the human health and the environment. They negatively affected the agricultural workers and trigger social conflicts when employed extensively and without safety measures. Further, they also have adverse effects on the neighboring communities. Chiefly, agriculture workers meet with both direct and indirect exposure with these chemicals. Common man comes in contact with these chemicals by skin contacting which is due to leaking and drifting of pesticides during mixing and causing serious threat to human health such as diabetes, reproductive disorders, neurological dysfunction, cancer and respiratory disorders. In this review, we discussed classification, mechanisms, benefits and adverse effects of the pesticides on both human beings and the environment. We had also discussed some remedial measures to mitigate their toxicity. In future, research is needed to develop innovative ideas in current farming which are able to decrease the application of chemical pesticides.
A. Boursianis, Maria Papadopoulou, Panagiotis D. Diamantoulakis
et al.
Abstract Internet of Things (IoT) and Unmanned Aerial Vehicles (UAVs) are two hot technologies utilized in cultivation fields, which transform traditional farming practices into a new era of precision agriculture. In this paper, we perform a survey of the last research on IoT and UAV technology applied in agriculture. We describe the main principles of IoT technology, including intelligent sensors, IoT sensor types, networks and protocols used in agriculture, as well as IoT applications and solutions in smart farming. Moreover, we present the role of UAV technology in smart agriculture, by analyzing the applications of UAVs in various scenarios, including irrigation, fertilization, use of pesticides, weed management, plant growth monitoring, crop disease management, and field-level phenotyping. Furthermore, the utilization of UAV systems in complex agricultural environments is also analyzed. Our conclusion is that IoT and UAV are two of the most important technologies that transform traditional cultivation practices into a new perspective of intelligence in precision agriculture.
The use of bioeffectors, formally known as plant biostimulants, has become common practice in agriculture and provides a number of benefits in stimulating growth and protecting against stress. A biostimulant is loosely defined as an organic material and/or microorganism that is applied to enhance nutrient uptake, stimulate growth, enhance stress tolerance or crop quality. This review is intended to provide a broad overview of known effects of biostimulants and their ability to improve tolerance to abiotic stresses. Inoculation or application of extracts from algae or other plants have beneficial effects on growth and stress adaptation. Algal extracts, protein hydrolysates, humic and fulvic acids, and other compounded mixtures have properties beyond basic nutrition, often enhancing growth and stress tolerance. Non-pathogenic bacteria capable of colonizing roots and the rhizosphere also have a number of positive effects. These effects include higher yield, enhanced nutrient uptake and utilization, increased photosynthetic activity, and resistance to both biotic and abiotic stresses. While most biostimulants have numerous and diverse effects on plant growth, this review focuses on the bioprotective effects against abiotic stress. Agricultural biostimulants may contribute to make agriculture more sustainable and resilient and offer an alternative to synthetic protectants which have increasingly falling out of favour with consumers. An extensive review of the literature shows a clear role for a diverse number of biostimulants that have protective effects against abiotic stress but also reveals the urgent need to address the underlying mechanisms responsible for these effects.Graphical abstractBiostimulants have protective effects against abiotic stress.
Significance Overuse of agricultural chemicals has resulted in enormous damages to environmental quality and human health in China. Reducing the use of agricultural chemicals to an optimal level is a crucial challenge for the sustainable development of agriculture. We demonstrate that small farm size (in China, typically ∼0.1 ha for each parcel) is strongly related to overuse of agricultural chemicals. Farm size increases with economic development in many other countries, but this is not observed in China due to national policies. Increasing farm size by removing policy distortions would substantially decrease both the use of agricultural chemicals and their environmental impact, while increasing rural income in China. Understanding the reasons for overuse of agricultural chemicals is critical to the sustainable development of Chinese agriculture. Using a nationally representative rural household survey from China, we found that farm size is a strong factor that affects the use intensity of agricultural chemicals across farms in China. Statistically, a 1% increase in farm size is associated with a 0.3% and 0.5% decrease in fertilizer and pesticide use per hectare (P < 0.001), respectively, and an almost 1% increase in agricultural labor productivity, while it only leads to a statistically insignificant 0.02% decrease in crop yields. The same pattern was also found using other independently collected data sources from China and an international panel analysis of 74 countries from the 1960s to the 2000s. While economic growth has been associated with increasing farm size in many other countries, in China this relationship has been distorted by land and migration policies, leading to the persistence of small farm size in China. Removing these distortions would decrease agricultural chemical use by 30–50% and the environmental impact of those chemicals by 50% while doubling the total income of all farmers including those who move to urban areas. Removing policy distortions is also likely to complement other remedies to the overuse problem, such as easing farmer’s access to modern technologies and knowledge, and improving environmental regulation and enforcement.
Nikolas Chatzis, Angeliki Tsinouka, Katerina Papadimitriou
et al.
Accurate 6D pose estimation for robotic harvesting is fundamentally hindered by the biological deformability and high intra-class shape variability of agricultural produce. Instance-level methods fail in this setting, as obtaining exact 3D models for every unique piece of produce is practically infeasible, while category-level approaches that rely on a fixed template suffer significant accuracy degradation when the prior deviates from the true instance geometry. To bridge such lack of robustness to deformation, we introduce PEAR (Pose and dEformation of Agricultural pRoduce), the first benchmark providing joint 6D pose and per-instance 3D deformation ground truth across 8 produce categories, acquired via a robotic manipulator for high annotation accuracy. Using PEAR, we show that state-of-the-art methods suffer up to 6x performance degradation when faced with the inherent geometric deviations of real-world produce. Motivated by this finding, we propose SEED (Simultaneous Estimation of posE and Deformation), a unified RGB-only framework that jointly predicts 6D pose and explicit lattice deformations from a single image across multiple produce categories. Trained entirely on synthetic data with generative texture augmentation applied at the UV level, SEED outperforms MegaPose on 6 out of 8 categories under identical RGB-only conditions, demonstrating that explicit shape modeling is a critical step toward reliable pose estimation in agricultural robotics.
The resources of the Sundarbans mangrove forest have provided livelihoods for communities in Bangladesh and India that depend on it. However, the role of the Sundarbans in ensuring the household dietary diversity of Sundarbans mangrove forest resource-dependent communities (SMFRDCs) remains unexplored. Considering the Sustainable Livelihood Approach, this cross-sectional survey study was conducted in three coastal districts (Khulna, Satkhira, and Bagerhat) to assess the prevalence and determinants of dietary diversity among SMFRDCs. Data were collected using a structured interview schedule from 782 households selected through a multistage stratified random sampling process over three months in 2023. Relevant statistical tests were conducted to assess the prevalence of dietary diversity and identify its determinants among households in the immediate vicinity of the Sundarbans. The one-sample binomial test showed that honey collectors and households in Shyamnagar Upazila had higher dietary diversity than those involved in other occupations and residing in other areas. The results of the binary logistic regression analysis indicated that individuals with higher education and those involved in multiple seasonal occupations were more likely to have diversified diets; however, spatial location had an inverse effect on the diets of SMFRDCs. Household assets, including domestic, transport, and livestock assets, as well as livelihood capitals such as social, natural, financial, and political, were positively associated with a diversified diet. In contrast, human and physical capital were negatively associated with household dietary diversity. The findings further show that physical vulnerability, along with household food insecurity, negatively affected dietary diversity among forest-proximate households. To ensure a sustainable, proper, and protein-enriched diet for forest-resource dependent people in the Sundarbans mangrove forest, context-specific, tailored, and well-integrated strategies are needed, including promoting alternative livelihoods, such as climate-smart agriculture, along with awareness regarding the consumption of locally available nutritious foods and government-aided food assistance programs, specifically during seasonal unemployment. Moreover, to improve access to diversified food items essential for the physical and mental development and well-being of forest-adjacent marginalized communities in coastal Bangladesh, certain factors suggested by the Sustainable Livelihood Approach, such as vulnerability, assets, policies and structures, and livelihood strategies, should be considered to ensure the sustainability of livelihood and resources, especially for climate-vulnerable communities.
Isaac Kazuo Uyehara, Heesup Yun, Earl Ranario
et al.
Agricultural imaging often requires individual images to be stitched together into a final mosaic for analysis. However, agricultural images can be particularly challenging to stitch because feature matching across images is difficult due to repeated textures, plants are non-planar, and mosaics built from many images can accumulate errors that cause drift. Although these issues can be mitigated by using georeferenced images or taking images at high altitude, there is no general solution for images taken close to the crop. To address this, we created a user-friendly and open source pipeline for stitching ground-based images of a linear row of crops that does not rely on additional data. First, we use SuperPoint and LightGlue to extract and match features within small batches of images. Then we stitch the images in each batch in series while imposing constraints on the camera movement. After straightening and rescaling each batch mosaic, all batch mosaics are stitched together in series and then straightened into a final mosaic. We tested the pipeline on images collected along 72 m long rows of crops using two different agricultural robots and a camera manually carried over the row. In all three cases, the pipeline produced high-quality mosaics that could be used to georeference real world positions with a mean absolute error of 20 cm. This approach provides accessible leaf-scale stitching to users who need to coarsely georeference positions within a row, but do not have access to accurate positional data or sophisticated imaging systems.
This paper presents the challenges agricultural robotic harvesters face in detecting and localising fruits under various environmental disturbances. In controlled laboratory settings, both the traditional HSV (Hue Saturation Value) transformation and the YOLOv8 (You Only Look Once) deep learning model were employed. However, only YOLOv8 was utilised in outdoor experiments, as the HSV transformation was not capable of accurately drawing fruit contours. Experiments include ten distinct fruit patterns with six apples and six oranges. A grid structure for homography (perspective) transformation was employed to convert detected midpoints into 3D world coordinates. The experiments evaluated detection and localisation under varying lighting and background disturbances, revealing accurate performance indoors, but significant challenges outdoors. Our results show that indoor experiments using YOLOv8 achieved 100% detection accuracy, while outdoor conditions decreased performance, with an average accuracy of 69.15% for YOLOv8 under direct sunlight. The study demonstrates that real-world applications reveal significant limitations due to changing lighting, background disturbances, and colour and shape variability. These findings underscore the need for further refinement of algorithms and sensors to enhance the robustness of robotic harvesters for agricultural use.
The addition of phosphorus, in the form of mineral fertilizer, becomes necessary in most agricultural soils in order to achieve consistent high yield levels of intensive farming and maintain soil fertility. Recent consolidation of phosphate fertilizer industry has transformed fragmented trade into a single integrated global network, where a small group of large-scale companies dominates the international market for phosphate commodity fertilizers. To assess the impact of new trade structure on future region-level phosphorus supply, we simulate behavior of markets for ammonium phosphates in the FAO scenarios of global intensive farming evolution. Details of market microstructure are represented here by a many-to-many matching market. Current spatial distribution of global demand in ammonium phosphates is projected to strengthen by 2030. Bootstrap simulations produce similar network structures for both scenarios showing reduction in the density of the distributed market. In response to the non-uniform demand growth across regions, market concentration is expected to increase for small-scale markets, and to remain predominantly stable for large-scale markets; on the supply side, simulated equilibria point out large-scale multi-market suppliers concentrating on fewer markets than before. A high rate of import substitution by local suppliers in some markets indicate the need of additional region-level capital investment.
Athanasios Bacharis, Konstantinos D. Polyzos, Georgios B. Giannakis
et al.
Active vision (AV) has been in the spotlight of robotics research due to its emergence in numerous applications including agricultural tasks such as precision crop monitoring and autonomous harvesting to list a few. A major AV problem that gained popularity is the 3D reconstruction of targeted environments using 2D images from diverse viewpoints. While collecting and processing a large number of arbitrarily captured 2D images can be arduous in many practical scenarios, a more efficient solution involves optimizing the placement of available cameras in 3D space to capture fewer, yet more informative, images that provide sufficient visual information for effective reconstruction of the environment of interest. This process termed as view planning (VP), can be markedly challenged (i) by noise emerging in the location of the cameras and/or in the extracted images, and (ii) by the need to generalize well in other unknown similar agricultural environments without need for re-optimizing or re-training. To cope with these challenges, the present work presents a novel VP framework that considers a reconstruction quality-based optimization formulation that relies on the notion of `structure-from-motion' to reconstruct the 3D structure of the sought environment from the selected 2D images. With no analytic expression of the optimization function and with costly function evaluations, a Bayesian optimization approach is proposed to efficiently carry out the VP process using only a few function evaluations, while accounting for different noise cases. Numerical tests on both simulated and real agricultural settings signify the benefits of the advocated VP approach in efficiently estimating the optimal camera placement to accurately reconstruct 3D environments of interest, and generalize well on similar unknown environments.