Precision Agriculture Techniques Research Articles

Challenges in the Planning, Construction and Farming Practices in Agrivoltaic Systems With Vertically Mounted Panels

Several challenges in planning, construction, and farming practices hinder the optimization of agrivoltaic systems (AS) and the achievement of optimal crop production. This paper identifies and addresses these issues while presenting initial solutions. One specific type of AS involves vertically mounted panels on arable or grassland sites. The installation of panel rows divides large fields into narrow units, restricting the use of farming implements with different working widths. Implement widths must align with the spacing between panel rows, which often results in residual strips or overlapping issues when field operations are carried out. Furthermore, boundary effects in AS are more pronounced, impacting yield along field borders. The presence of panel rows also complicates driving operations, requiring reduced speeds and posing collision risks between implements and panels. Soil compaction during AS construction, microclimate variations, and panel contamination by dust, or spray drift deposits further affect plant growth and solar system performance. Initial solutions are proposed to address these challenges. These include careful planning of row spacing based on the working widths of critical implements such as combines, adoption of field sprayers with foldable booms, consideration of pneumatic fertilizer spreaders, and integration of precision farming techniques to manage variability within AS. Additionally, the use of construction machinery with low soil pressure, employment of steering technologies based on global navigation satellite systems, and research on panel cleaning devices are suggested. Overall, this paper highlights the need for further research and development to overcome farming challenges in agrivoltaic systems with vertically mounted panels.

Selection of suitable fusional band combination from Sentinel-2A and UAV imagery for agricultural applications

ABSTRACT This study explores the fusion of Sentinel-2A (S2) and different bands of UAV imagery (Blue, Green, Red and Panchromatic (PAN)) to enhance spatial and spectral information for crop health monitoring. It assesses the quality of fused images, accuracy of the classified fused images and statistical analysis between the fused and S2 vegetation indices. Various fusion techniques are examined to select an optimal combination of bands. The quality of the fused images is evaluated using image quality assessment metrics and classification employs the Random Forest algorithm, assessing accuracy with metrics like F-score and Kappa Coefficient. Statistical analysis involves comparing vegetation indices from fused and S2 imagery. Notably, the fusion of UAV Green and Red bands with S2 imagery, using BT and PCA techniques, emerges as an effective combination for plant-level agricultural health monitoring. This research contributes to advancing precision agriculture techniques by leveraging multispectral imaging fusion for enhanced crop monitoring and management.

Efficiency and sustainability: Evaluating and optimizing energy use and environmental impact in cucumber production

This study assesses the energy-economic-environmental aspects of cucumber production in the face of environmental issues and resource scarcity, which threaten global food security and demand sustainable agriculture solutions. Data were collected by questionnaire and analyzed for inputs from planting to harvesting. The results indicated that diesel fuel (37.41%) and nitrate fertilizer (37.22%) constitute the primary inputs. The energy ratio, energy efficiency and net energy gain were 0.43, 0.54 kgMJ−1 and -30,153.31 MJha−1, respectively. Input optimization by Data Envelopment Analysis (DEA) reduced the carbon footprint from 178.433 to 168.900 kg CO2 per ton, the abiotic depletion from 1228.291 to 964.305 MJ and the acidification from 0.838 to 0.749 kg SO2 eq. Also, the results of DEA showed that 52.5% and 35% of the units were fully efficient under Variable Return to Scale (VRS) and Constant Return to Scale (CRS) models, respectively, while the others were inefficient to varying degrees. The average of technical, pure technical and scale efficiencies were 92.88%, 97.64%, and 95.07%, respectively. The results of economic evaluation showed that production cost is 6058.84 $ha−1 with a benefit-cost ratio of 1.71. Based on the primary results, it is suggested to use precision agriculture techniques and the exploration of alternative energy sources, such as biofuel for machinery. Additionally, using slow-release fertilizers or organic substitutes can decrease nitrogen fertilizer dependency while maintaining crop yields and improve the sustainability in this production.

Whole-farm yield map datasets – Data validation for exploring spatiotemporal yield and economic stability

CONTEXTStatistical methods used for delineation of field management zones and yield stability are frequently only applied to relatively small areas, with few studies performing rotational, whole-farm economic spatiotemporal appraisals. To enable accurate economic analysis, yield map datasets must contain minimal errors while cleaning procedures are often used to remove errors, it is rare that cleaned data is validated before its application. OBJECTIVEThe objective of this study was to process, validate and combine spatial statistical approaches for a rotational yield map dataset from a whole-farm across 7 crops in a winter wheat based rotation. Developing a framework for using validated yield map datasets to support precision agriculture techniques that are applicable for farm-level decision making. METHODSThe rotational completeness of a 10 year combine yield map dataset for a 435 ha farm in Eastern England was assessed. The dataset was cleaned statistically, and its accuracy assessed by comparison with recorded yields from trailer weigh cells. The cleaned, validated, and corrected yield map dataset was used to identify management zones across the whole farm using fuzzy clustering. The temporal stability of management zones and economic performance across the rotation was also assessed. RESULTS AND DISSCUSIONData cleaning methods removed 16% of data points, improving the degree of spatial correlation within the individual yield maps. Independent validation demonstrated varied accuracy of yield maps from combine harvester data and errors in wheat ranged from 0.53 to 1.53 t/ha RMSE. These errors have implications for researchers using combine yield data to develop and validate precision agriculture technologies. This data set required correction before yield data can be applied with confidence for on-farm decision making. Compared to the zones with the highest margin in each field, 34% of zones had an average annual margin loss of >£100 ha. The temporal stability of the resulting management zones also varied. Areas with the lowest economic performance and greatest yield stability across years will potentially see the greatest economic and environmental benefits from precision agriculture techniques. SIGNIFICANCEThe accuracy of combine yield map data should not be assumed. The application of these datasets, including for the identification of management zones or in developing precision agriculture techniques should attempt to address this through data cleaning and validation procedures. Only then should it be used for on farm decision making, such as identifying areas with the most economic benefit by applying precision agriculture tools such as variable rate nutrient applications.

Статистически групировки при изследване нагласите на фермерите за внедряване практиките на прецизното земеделие

The application of precision farming techniques leads to significant improvements both in economical and environmental point of view, facilitates the optimization of resource use and reduces farmers’ risks in terms of natural and disease hazards but enhances the risk from technological faults. The whole article is to explore the attitudes of farmers regarding the possibilities and prospects for the implementation of precision agriculture technologies, identifying and establishing the most possible and unifying views and perceptions obtained from a field study. In this article, a statistical grouping is used to process surveys conducted in farms in Bulgaria. Given the growing role of technology and its importance in expanding, improving and stabilizing farms, the role of precision agriculture is becoming stronger in the agriculture industry.

CLIMATE CHANGE AND FOOD SUPPLY CHAIN ECONOMICS: A COMPREHENSIVE ANALYSIS OF IMPACTS, ADAPTATIONS, AND SUSTAINABILITY

Climate change poses significant challenges to global food supply chains, impacting various facets of production, distribution, and consumption. This review presents a comprehensive analysis of the intricate relationship between climate change and food supply chain economics, focusing on its impacts, adaptations, and sustainability measures. Firstly, climate change disrupts agricultural production through extreme weather events, shifts in precipitation patterns, and changes in temperature, leading to reduced crop yields and quality. These impacts ripple through the entire food supply chain, affecting input costs, market prices, and food security. Consequently, stakeholders across the supply chain face heightened risks and uncertainties, necessitating adaptive strategies to mitigate losses and maintain resilience. Secondly, adaptation measures within food supply chains encompass a range of strategies aimed at minimizing climate-related risks and optimizing resource utilization. These may include adopting climate-resilient crop varieties, implementing precision agriculture techniques, enhancing water management practices, and diversifying sourcing and distribution channels. Moreover, investments in technology, infrastructure, and knowledge transfer play pivotal roles in enhancing adaptive capacities and fostering sustainability in the face of climate variability. Furthermore, ensuring the sustainability of food supply chains amidst climate change requires a multifaceted approach that integrates economic, social, and environmental considerations. Sustainable practices such as organic farming, agroforestry, and regenerative agriculture not only mitigate greenhouse gas emissions but also enhance ecosystem resilience and promote equitable access to resources. Moreover, fostering collaboration and knowledge exchange among stakeholders, including farmers, policymakers, researchers, and consumers, is essential for promoting innovation and driving transformative change towards more resilient and sustainable food systems. In conclusion, understanding the complex interplay between climate change and food supply chain economics is essential for developing effective strategies to mitigate risks, enhance adaptation, and promote sustainability. By adopting a holistic approach that integrates scientific insights, technological innovations, and stakeholder engagement, the global community can work towards building more resilient and equitable food supply chains in a changing climate.
 Keywords: Climate Change, Food, Supply, Chain, Economics, Review.

Bioenergy and sustainable agriculture: A review of synergies and potential conflicts

The intersection of bioenergy and sustainable agriculture presents a dynamic landscape with both promising synergies and potential conflicts that demand careful consideration. This comprehensive review explores the intricate relationship between bioenergy production and sustainable agricultural practices, aiming to elucidate the opportunities and challenges inherent in their integration. Synergies between bioenergy and sustainable agriculture are evident in their shared goal of mitigating climate change. Bioenergy crops, such as perennial grasses and woody biomass, can sequester carbon and contribute to greenhouse gas reduction, aligning with the principles of sustainable agriculture that emphasize environmental stewardship. Additionally, the integration of bioenergy systems with agricultural practices can foster resource efficiency, as crop residues and organic waste become valuable feedstocks for biofuel production. However, potential conflicts arise when land-use competition intensifies. The expansion of bioenergy crops may encroach upon land designated for food production, leading to concerns about food security and biodiversity loss. Striking a balance requires careful planning, considering regional variations in land availability, climate, and socio-economic factors. Furthermore, the review delves into the technological advancements driving the coexistence of bioenergy and sustainable agriculture. Precision farming techniques, agroforestry, and innovative cropping systems emerge as tools to optimize land use, enhance resource efficiency, and minimize environmental impacts. In conclusion, the intertwining realms of bioenergy and sustainable agriculture offer a complex tapestry of synergies and potential conflicts. A nuanced and context-specific approach is essential for maximizing the benefits while minimizing adverse impacts. This review provides insights into the current state of knowledge, highlighting the need for interdisciplinary collaboration, policy frameworks, and technological innovations to ensure a harmonious coexistence between bioenergy production and sustainable agriculture.

Horticultural Innovations Elevating Crop Yields and Agricultural Sustainability for a Flourishing Future

Advancements in horticultural technologies and practices are critical for meeting global food security needs while ensuring environmental sustainability. This paper examines recent innovations that enhance crop yields and resource efficiency in horticulture. Key areas explored include high-tech greenhouse agriculture, precision agriculture techniques, improved irrigation systems, biofertilizers and biopesticides, breeding of resilient cultivars, and vertical farming. Intensive greenhouse production with supplemental lighting, climate control, hydroponics and automation enables year-round vegetable and fruit yields up to 20 times higher than open-field methods. Precision agriculture leverages data analytics, sensors and AI for optimized inputs and cultivation decisions per sub-field zone. Upgraded irrigation systems like drip lines and computerized scheduling curtail water usage. Organic biofertilizers and biopesticides derived from microbes, plants and minerals boost soil health and impede pests while avoiding chemical residues. Newly bred cultivars feature accelerated growth, improved taste and nutrition, and resistance to biotic and abiotic stresses. Meanwhile, vertical farms multilayer indoor cropping to magnify yields in small footprints. Further integration of these and other cutting-edge horticultural technologies can increase global food production without environmental sacrifice. This necessitates greater public and private sector investments paired with institutional support.

Microcontroller Implementation in Precision Planting: A Review

Precision farming, also known as precision agriculture, is a crop management method that optimizes agricultural production by utilizing technology and data for precise placement of seed, fertilizer and pesticides. It enhances increase crop yields, reduce waste, and reduce environmental impact by applying the appropriate amount of inputs, such as seed, water, fertilizer, and pesticides for each crop. Farmers can reduce expenses by reducing the usage of expensive inputs while increasing yields and crop quality by employing precision farming techniques. Commercially available planters normally use mechanical metering systems which have many limitations including, skidding of the wheel, non-uniform seed rate, wastage of the seed and higher maintenance cost. It is evident from recent researchers that irregularly in seed placement, which is generally caused due to wheel slippage and seed metering systems powered by electric or hydraulic motor can maintain accurate seed placement and can work at higher speeds compared to traditional planters.

Development of a gridded yield data archive for farm management and research at the USDA Beltsville Agricultural Research Center

AbstractHigh‐quality, gridded maps of crop yield, which discern the spatial variability in productivity across individual farm fields, are extremely valuable in numerous agricultural and remote sensing applications. The availability of these data was greatly facilitated by the development and adoption of grain yield monitors starting in the early 1990s. However, the utility of raw yield monitor data is limited if additional processing has not been carried out to ensure data quality. In addition, a readily accessible data repository that allows for examining long‐term yield variability in response to climate and management is often not available. We present a protocol for producing a multi‐year (7 years currently processed), gridded yield data archive derived from yield monitor data acquired from over 40 production fields at the Beltsville Agricultural Research Center (BARC) in Beltsville, MD. Production of the archive involved the projection of the yield monitor data into a geographic coordinate system suitable for mapping, the identification and removal of data outliers, and the production of gridded, spatially interpolated yield maps using block Kriging. All map products were produced in common, nonproprietary file formats for easy access. Preliminary assessments of the processed yield monitor data have underscored the necessity of data filtering and have shown the influence of previous land use practices and biophysical properties, such as topography and soil moisture availability, on yield response. We discuss the use of spatially interpolated, archival yield maps in the implementation of precision farming techniques such as the development of site‐specific variable rate prescriptions as well as remote sensing applications, including the selection of field sites for long‐term agricultural experiments and the assessment of crop modeling approaches. The gridded yield data archive will be housed at the National Agricultural Library as a dynamic dataset that will continue to expand in breadth and depth. It will be a part of and provide support to internal USDA yield monitoring investigations as well as complementary external efforts. All geospatial data delivered to NAL will adhere to the ISO 19115 Geospatial Metadata Standards to provide the user with a full understanding of the yield monitor and associated datasets.

The Role of Smart Farming Technologies in Enhancing Crop Yields and Resource Efficiency: A Global Perspective

This research article explores the role of smart farming technologies in improving crop yields and resource efficiency from a global perspective. Utilizing a qualitative methodology involving literature review and library research, the study aims to examine the various smart farming technologies and their impact on agricultural productivity and sustainability.
 The literature review encompasses a wide range of sources, including scholarly articles, reports, and case studies, to provide a comprehensive understanding of smart farming technologies. By synthesizing existing research, the article identifies key technologies such as precision agriculture, Internet of Things (IoT) sensors, drones, and data analytics, and examines their applications in enhancing crop yields and resource management.
 The findings highlight the significant contributions of smart farming technologies in optimizing agricultural practices, minimizing resource wastage, and maximizing efficiency. Through precision farming techniques and real-time monitoring systems, farmers can make data-driven decisions regarding irrigation, fertilization, and pest control, leading to improved crop quality and higher yields.
 Moreover, the study discusses the potential challenges and barriers to the adoption of smart farming technologies, including cost, infrastructure, and digital literacy issues, particularly in developing countries. It also explores the importance of supportive policies, capacity building initiatives, and public-private partnerships in facilitating the widespread adoption of these technologies.

Tree-based algorithms for spatial modeling of soil particle distribution in arid and semi-arid region.

Accurate estimation of particle size distribution across a large area is crucial for proper soil management and conservation, ensuring compatibility with capabilities and enabling better selection and adaptation of precision agricultural techniques. The study investigated the performance of tree-based models, ranging from simpler options like CART to sophisticated ones like XGBoost, in predicting soil texture over a wide geographic region. Models were constructed using remotely sensed plant and soil indexes as covariates. Variable selection employed the Boruta approach. Training and testing data for machine learning models consisted of particle size distribution results from 622 surface soil samples collected in southeastern Turkey. The XGBoostClay model emerged as the most accurate predictor, with an R2 value of 0.74. Its superiority was further underlined by a 21.36% relative improvement in XGBoostClay RMSE compared to RFClay and 44.5% compared to CARTClay. Similarly, the R2 values for XGBoostSilt and XGBoostSand models reached 0.71 and 0.75 in predicting sand and silt content, respectively. Among the considered covariates, the normalized ratio vegetation index and slope angle had the highest impact on clay content (21%), followed by topographic position index and simple ratio clay index (20%), while terrain ruggedness index had the least impact (18%). These results highlight the effectiveness of Boruta approach in selecting an adequate number of variables for digital mapping, suggesting its potential as a viable option in this field. Furthermore, the findings of this study suggest that remote sensing data can effectively contribute to digital soil mapping, with tree-based model development leading to improved prediction performance.

Digital agriculture for sustainable development in China: The promise of computerization

The pivotal role of computer technology in augmenting crop productivity, optimizing supply chain management in food production, and reducing wastage through precision agriculture techniques is undeniable. The utilization of computers has significantly transformed the field of food production, enhancing effectiveness, environmental sustainability, and economic viability. This study examines the asymmetric and long-term effects of computer usage on grain output in the selected Chinese provinces. Additionally, it investigates the impact of other variables, such as energy use and the application of pesticides and fertilizers on grain yields. To analyze the dataset spanning from 2002 to 2020, the researchers employed the FGLS, D-K, and Dumitrescu-Hurlin causality methods. The study's findings underscore the substantial positive impact of computer utilization on grain production. Besides, fertilizer application and adoption of irrigation practices positively influence grain yields. Conversely, the utilization of pesticides, power consumption, and rural labor engagement exhibit a negative impact on grain output. Therefore, evaluating the potential of investing in sophisticated technologies, considering multiple elements including local conditions, the availability of financial resources, and the role of agricultural industrialization in regional development is imperative. We discuss the policy implications of these findings in detail in the Conclusion section.

Sustainable Farming and Soil Health Enhancement through Millet Cultivation: A Review

Historically integral to Indian agriculture, millets are experiencing resurgence, driven by their adaptability to harsh climatic conditions and minimal resource requirements. The article explores the significant benefits of millets in soil nutrient management, demonstrating their ability to thrive in nutrient-poor soils while contributing to soil fertility through organic matter addition and improved soil structure. The integration of millets in crop rotation and intercropping systems is highlighted as a sustainable practice that enhances soil biodiversity and reduces the reliance on chemical inputs. A key environmental benefit of millet cultivation is its low water requirement and drought resistance, crucial in water-scarce regions, making it a strategic crop for adapting to climate change. Also examines the socio-economic impacts of millet cultivation. In rural areas, millets play a vital role in livelihoods by providing a sustainable food source and income generation, particularly in marginal environments. However, challenges in market accessibility and supply chain inefficiencies pose significant hurdles. Increasing consumer awareness and acceptance of millets, once considered traditional or 'poor man’s food', is critical for reviving their cultivation. This is complemented by emerging research and technologies in millet cultivation, focused on developing improved varieties and precision agriculture techniques tailored to the needs of millet farming. The potential for scaling up millet cultivation is immense, particularly in regions facing environmental constraints. The article emphasizes the integration of millets into global sustainable farming strategies, aligning with several Sustainable Development Goals. This integration is supported by international organizations advocating for millets in agricultural policies and programs. In conclusion, the review underscores the importance of millets in the context of global food security and sustainable agriculture. The growing importance of millets backed by research, policy support, and market trends, positions them as a key crop in the quest for a sustainable and resilient agricultural future.

Growing Green: Sustainable Agriculture Meets Precision Farming: A Review

Precision farming involves the use of advanced technologies such as the global positioning system (GPS), sensors and data analytics to make informed decisions on crop management. The integration of sustainable agriculture principles with precision farming techniques offers a holistic approach to address the challenges faced by modern agriculture. This review explores the convergence of sustainable agriculture and precision farming practices, aiming to enhance the efficiency, productivity and environmental sustainability of modern farming systems. The review delves into the principles of precision farming, which involves the use of advanced technologies such as GPS, sensors and data analytics to optimize resource use and improve crop yields. The integration of sustainable practices within precision farming frameworks is a central focus, emphasizing the importance of environmental supervision, soil health and biodiversity conservation. This review also highlights the collaboration between cutting-edge agricultural technologies and environment friendly farming practices, illustrating a path forward for the agriculture industry towards a sustainable and resilient nutritional security.

Assessing the Influence of Climate Change on Agricultural Yields and Crafting Farmer-Centric Adaptation Strategies: A Policy-oriented Agricultural Analysis

This journal article critically examines the influence of climate change on agricultural yields and proposes farmer-centric adaptation strategies through a policy-oriented agricultural analysis. The research employs a comprehensive approach, integrating climate science, agricultural economics, and policy studies to assess the multifaceted impacts of climate change on global agricultural productivity.
 The study analyzes empirical data on changing climate patterns, extreme weather events, and temperature variations, emphasizing their significant consequences for agricultural yields. By utilizing statistical models and econometric techniques, the research quantifies the extent of yield variations across diverse crops, providing a nuanced understanding of the challenges faced by farmers.
 In response to these challenges, the research proposes adaptation strategies centered on the needs and capacities of farmers. These strategies encompass the development and dissemination of climate-resilient crop varieties, the enhancement of irrigation infrastructure, and the implementation of precision agriculture techniques. Moreover, the study investigates the role of agricultural policies in promoting adaptive practices and explores potential policy frameworks to incentivize climate-smart agriculture.
 The findings highlight the importance of tailoring adaptation strategies to local contexts and engaging farmers in the decision-making process. The research underscores the need for policies that empower farmers, providing them with the knowledge, resources, and incentives to adapt to changing climatic conditions effectively.
 In conclusion, this article contributes to the discourse on climate change adaptation in agriculture by offering a policy-oriented perspective. It advocates for a holistic approach that integrates scientific insights, economic considerations, and farmer participation, aiming to enhance the resilience of agricultural systems in the face of climate change.

A Systematic Review of Geographic Information Systems (GIS) in Agriculture for Evidence-Based Decision Making and Sustainability

The aim of this study was to consolidate current information on the utilization of Geographic Information Systems (GIS) and Remote Sensing (RS) in the agricultural sector, with a focus on their role in promoting evidence-based policies and practices to enhance agricultural sustainability. Additionally, this review sought to identify the challenges hindering the widespread adoption of GIS and RS applications, particularly in low- and middle-income nations. This study employed the methodology of systematic literature review. The findings indicate that the utilization of GIS technology in the agricultural sector has experienced a notable increase over the past few years. The primary areas of use for GIS that have been identified encompass crop yield estimation, assessment of soil fertility, monitoring of cropping patterns, evaluation of drought conditions, detection and management of pests and crop diseases, implementation of precision agriculture techniques, and management of fertilizer and weed control. GIS technology possesses the capacity to augment the sustainability of agriculture by incorporating the spatial aspect of agricultural practices into agricultural policies. Furthermore, the potential of GIS in facilitating evidence-based decision making is expanding. Given the escalating peril of climate change on agriculture and food security, there exists a heightened imperative to include GIS into policy formulation and decision-making processes to enhance the sustainability of agricultural practices. The findings of this study might be beneficial in informing the development of policies that effectively integrate sustainable and climate-smart practices in agriculture.

Designing a Precision Seed Sowing Machine for Enhanced Crop Productivity

A seed sowing machine is a valuable agricultural device that facilitates the precise and efficient sowing of seeds in fields. When designing and optimizing such a machine, several crucial factors need consideration including seed size, seed rate, soil type, and field conditions. The primary objective is to achieve uniform seed distribution and optimal seed-to-soil contact, which can be accomplished by incorporating a seed metering mechanism to control the seed rate accurately. Versatility is another important aspect of the machine's design, as it should be able to handle different seed sizes, types, soil conditions, and field variations. To achieve this, utilizing advanced technologies such as sensors, automation, and precision farming techniques can significantly enhance the machine's performance and efficiency while also reducing costs and minimizing environmental impact. The optimization of a seed sowing machine plays a crucial role in ensuring successful crop production. By implementing cutting-edge technologies and precision farming techniques, farmers can increase their yields and decrease the amount of seed and fertilizer needed for a specific area. Ultimately, this leads to improved productivity, increased profitability, and a more sustainable approach to agriculture.

Enhancing Plant Disease Classification through Manual CNN Hyperparameter Tuning

Diagnosing plant diseases is a challenging task due to the complex nature of plants and the visual similarities among different species. Timely identification and classification of these diseases are crucial to prevent their spread in crops. Convolutional Neural Networks (CNN) have emerged as an advanced technology for image identification in this domain. This study explores deep neural networks and machine learning techniques to diagnose plant diseases using images of affected plants, with a specific emphasis on developing a CNN model and highlighting the importance of hyperparameters for precise results. The research involves processes such as image preprocessing, feature extraction, and classification, along with a manual exploration of diverse hyperparameter settings to evaluate the performance of the proposed CNN model trained on an openly accessible dataset. The study compares customized CNN models for the classification of plant diseases, demonstrating the feasibility of disease classification and automatic identification through machine learning-based approaches. It specifically presents a CNN model and traditional machine learning methodologies for categorizing diseases in apple and maize leaves, utilizing a dataset comprising 7023 images divided into 8 categories. The evaluation criteria indicate that the CNN achieves an impressive accuracy of approximately 98.02%.

Pasture Biomass Estimation Using Ultra-High-Resolution RGB UAVs Images and Deep Learning

The continuous assessment of grassland biomass during the growth season plays a vital role in making informed, location-specific management choices. The implementation of precision agriculture techniques can facilitate and enhance these decision-making processes. Nonetheless, precision agriculture depends on the availability of prompt and precise data pertaining to plant characteristics, necessitating both high spatial and temporal resolutions. Utilizing structural and spectral attributes extracted from low-cost sensors on unmanned aerial vehicles (UAVs) presents a promising non-invasive method to evaluate plant traits, including above-ground biomass and plant height. Therefore, the main objective was to develop an artificial neural network capable of estimating pasture biomass by using UAV RGB images and the canopy height models (CHM) during the growing season over three common types of paddocks: Rest, bale grazing, and sacrifice. Subsequently, this study first explored the variation of structural and color-related features derived from statistics of CHM and RGB image values under different levels of plant growth. Then, an ANN model was trained for accurate biomass volume estimation based on a rigorous assessment employing statistical criteria and ground observations. The model demonstrated a high level of precision, yielding a coefficient of determination (R2) of 0.94 and a root mean square error (RMSE) of 62 (g/m2). The evaluation underscores the critical role of ultra-high-resolution photogrammetric CHMs and red, green, and blue (RGB) values in capturing meaningful variations and enhancing the model’s accuracy across diverse paddock types, including bale grazing, rest, and sacrifice paddocks. Furthermore, the model’s sensitivity to areas with minimal or virtually absent biomass during the plant growth period is visually demonstrated in the generated maps. Notably, it effectively discerned low-biomass regions in bale grazing paddocks and areas with reduced biomass impact in sacrifice paddocks compared to other types. These findings highlight the model’s versatility in estimating biomass across a range of scenarios, making it well suited for deployment across various paddock types and environmental conditions.