The environmental impacts caused by the use of mineral fertilizers contribute significantly to the loss of quality of soil, water resources and the atmosphere. At a global level, there are increasing investments in the search for alternatives that reduce the use of chemical inputs in agriculture and contribute to sustainable production. In this sense, the use of growth-promoting microorganisms is one of the most promising techniques. In soybean cultivation, for example, inoculation makes it possible to eliminate the use of nitrogen fertilizers. The objective of the present work was to evaluate the effects of inoculating cotton plants with Azospirillum brasilense and Pseudomonas fluorescens. The experiment was conducted under greenhouse conditions in a completely randomized design, with five treatments and five replications, as follows: T1: 100% N; T2: 75% N; T3: 75% N + Azospirillum brasilense; T4: 75% N + Pseudomonas fluorescens; T5: 75% N + Azospirillum brasilense + Pseudomonas fluorescens. Inoculation was performed at the time of transplanting. After five months, the growth and mass production of roots and shoots were evaluated. The data were submitted to analysis of variance and the means were separated by the Scott–Knott test at 5% probability of error. The use of Pseudomonas fluorescens with 75% nitrogen fertilization increased the mass of fresh roots by 150% and the volume of roots by 167% in relation to 100% fertilization. Inoculation with P. fluorescens appears to be a promising tool for better rooting of cotton seedlings and reducing fertilization costs, and should be better explored to understand its benefits in field conditions, as well its effects on the productivity of this crop.

Plants account for over 80% of the human diet, being essential for food security and worldwide feeding. In this context, it is important to maintain
high productivity, avoid crop losses and preserve the environment. Plant leaf diseases are a drawback in this reality, where they are responsible for
depreciation in food quality, directly causing economic losses in agricultural production and great environmental impact if pesticides are used
indiscriminately. Therefore, the classification of diseases is of fundamental importance and is a great challenge since many leaf diseases present
similarities, inducing misidentification. Also, manual classification is an exhaustive task and gives subjective results, causing misidentification in
addition to being economically unfeasible. To solve this problem, a new arrangement of deep convolutional neural networks, activation
functions and optimizers for plant leaf disease classification is proposed. In the proposed method, we tested different EfficientNet Convolutional Neural Network models, scaling the model size and number of parameters alongside with the Mish activation function and Ranger Optimizer in the task of plant leaf disease classification. Compared with previous work applied into the same dataset, the
proposed arrangement achieved better performance with 94,0% accuracy for EfficientNetB0, achieving state-of-the art results for the tested
dataset. The model with best performance also has less parameters, therefore being effective and demonstrating potential for portability.

Telemetry systems are widely used in the latest agricultural machinery, both in tractors and combines. There is still a problem with managing the data recorded and stored in the telemetry systems and their purposeful use. Ways to correctly interpret, analyze, and use as much as possible of the information obtained during various agricultural technological processes has been studied.

The purpose of this work is to analyze the harvesting data collected and stored in the telematics system in order to optimize labor costs. The research was carried out during grain harvesting using a CLAAS Lexion combine harvester. By using the data of the telematics system, the working time structure and fuel consumption of the technological processes of spring wheat harvesting for one year were analyzed. During this study, the components of the harvesting time, which have an influence on the harvesting speed and the performance of the harvester, were examined. The results showed that 10% more time was spent when grain unloading stopped when harvesting smaller fields than larger fields (more than 50 ha). Fuel consumption when unloading grain after the harvester stopped amounted to 2% of the total fuel used for harvesting one ton of grain. After analyzing the efficiency of the use of the harvester, it was found that a considerable part of the working time and fuel consumption is devoted to the inevitable inefficient technological processes.

Pasture growth and quality are optimised when pasture is grazed at the point at which it achieves 95% light interception (LI), that is, when the canopy is capable of intercepting 95% of the incident solar radiation with its leaves. Thus, measurements of LI provide a good indicator of the ideal time to graze a pasture. However, measurements of LI are difficult since they require sensors to be placed in and above a pasture canopy throughout a pasture regrowth period and are affected by daily weather patterns. Therefore, an alternative tool for predicting pasture LI is desired. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge (NDRE) may provide useful alternatives to LI, since their measurement is less labour-intensive and is not affected by weather. In a mixed perennial ryegrass (Lolium perenne L.) + plantain (Plantago lanceolata L.) pasture at Massey University, New Zealand, the relationship between NDVI, NDRE and LI was investigated between early spring and late summer. NDVI and NDRE were measured by scanning eight plots (182m²) with a Rapid Scan(R) CS-45 canopy analyser (~300 readings), while LI was measured with a Spectrosense 2⁺(R) device, at three fixed locations within each plot. Positive logarithmic relationships were found between NDVI and LI (R² = 0.35) and NDRE and LI (R² = 0.30). These results suggest that NDVI and NDRE may be useful tools for predicting pasture LI, therefore indicating the most effective time to graze a pasture. However, future research is required to determine the limitations of their use for predicting optimal grazing times for different pasture species.

Since the 1990s, precision farming has introduced a revolution in agricultural practices and has evolved significantly in terms of the application of GPS, GIS, and yield monitors. Such technologies have substantially contributed to the Indian agriculture sector over the past two decades by increasing productivity, resource management, and decision-making along with reducing environmental impact. The current global precision farming market was valued at USD 10.50 billion in 2023, and is expected to grow at a compound annual growth rate (CAGR) of 12.8% between 2024 and 2030. The analysis of data that has already been acquired by others is referred to as secondary data analysis. Developing advanced technologies such as AI, ML, IoT, and agricultural robotics plays a crucial role in data-driven digital farming by improving efficiency and sustainability. Precision seeding, utilizing variable-rate technologies, has shown 10% to 30% greater efficiency compared to conventional methods. IoT has increased agricultural productivity by 70%, aligning with the future scope for 2050. The Digital Agriculture Mission 2021–2025 and the 'India Digital Ecosystem of Agriculture,' centered on 'AgriStack,' are pivotal in driving sector digitalization and increasing farmers’ income. Several modern applications, including Soil Health Card, Plantix, Meghdoot, and mKisan, are enhancing areas like soil health, fertilizer recommendations, irrigation scheduling, and pest management. Future developments like Deep Leaf, which uses deep learning to enhance measurement accuracy with average errors as low as 4.6% for leaf length and 5.7% for leaf width, will further streamline agricultural processes. However, the adoption rate of precision agriculture is expected to stabilize post-2030. Continuous advancements in AI, ML, and IoT are anticipated to further propel productivity, profitability, and sustainability in agriculture, ensuring effective land resource protection and minimizing environmental impact.

The increasing global need for food necessitates a shift towards more profitable and efficient farming methods since chemical fertilizers pose hazards to both human health and the planet's ecosystem. Microorganisms that may serve as PGPRs (plant growth-promoting rhizobacteria) for Zea mays optimization were the focus of this study's isolation and identification efforts. A preliminary screening of five isolates was conducted using growth stimulation, nutrient solubilization, and enzyme synthesis in vitro from indigenous soil at the Federal University Oye Ekiti field. By comparing each trait's maximum and minimum, the strain pool was reduced to two. The 16S rDNA sequencing method was used to confirm the identification of two promising isolates, Enterobacter hormaechei OYA S29 and Acinetobacter sp. OYA S30. The effect of Enterobacter hormaechei OYA S29 on soil phosphorus and maize development was examined in a pot experiment. The findings showed that soil phosphorus solubilization was increased from 0.4905ppm (Control group) to 0.4934ppm by (Enterobacter hormaechei OYA S29), and Zea mays development much improved, with greater plant height (45.722cm) and plant girth (4.20cm), longer leaves (35.500cm), longer roots (23.433cm), and higher biomass (7.4850g), when compared to the control group (36.250cm, 3.225cm, 28.450cm, 16.750cm and 4.7220g). These results demonstrate the discovered strains' potential as efficient PGPRs for maximizing maize growth and nutrient availability.

Bacillus thuringiensis is a Gram-positive bacterium known for its insecticidal activity against various insect species, making it an excellent biocontrol agent. Although B. thuringiensis is regarded as an opportunistic entomopathogen due to the naturally low incidence of epizootics, certain strains, such as B. thuringiensis serovar aizawaii, have shown the potential to induce epizootics in Plodia interpunctella (Lepidoptera: Pyralidae). In this study, we isolated the strain INTA L404-1 from the hemolymph of a dead P. interpunctella larva collected from artificial rearing at CEPAVE (La Plata, Argentina), where a high mortality rate was observed. DNA was extracted from the purified bacterium using a commercial kit, and Illumina sequencing was performed for strain identification. Nine insecticidal protein genes were identified, including cry1Aa, cry1Ab, cry1Ca, cry1Da, cry1Ia, cry2Ab, cry9Ea, spp1Aa, and vip3Aa, with amino acid sequence identities ranging from 80% to 100%. Additionally, the proteins from the genes, similar to cry9Ec, mpp3Aa, and tpp80Aa, exhibited sequence identities below 34%. These results suggest the potential of INTA L404-1 as a biocontrol agent. Bipyramidal crystals were observed in INTA L404-1, and an SDS-PAGE analysis of parasporal crystals revealed a unique ca. 130 kDa protein. This strain demonstrated significant insecticidal activity, causing 100% mortality in Cydia pomonella (Lepidoptera: Tortricidae) when incorporated into the larvae's diet. B. thuringiensis INTA L404-1 may induce epizootics in the artificial rearing of P. interpunctella, as evidenced by the observed spread of infection among pest offspring. To confirm this, it is necessary to assess the strain INTA L404-1 in bioassays with P. interpunctella to demonstrate that it causes mortality like that observed in the original host. The insecticidal protein gene profile of INTA L404-1 is comparable to that of other B. thuringiensis serovar aizawaii strains.

The nematophagous fungus Arthrobotrys oligospora (Leotiomycetes: Heliotales) has for a long time incited interest inits potential as a biological control agent of plant-parasitic nematodes in agriculture. However, efforts to materialise these aspirations have been hindered by inconsistent results. Many research papers on the subject deal with one particular strain of A. oligospora, but their conclusions are often assumed to be valid for the whole species. We hypothesize that one of the causes for the inconsistences is that the natural variation in the nematicidal response between different strains of A. oligospora, as well as how prey availability in the experimental design can affect results, is often overlooked. In this work, we evaluate the in vitro nematicidal response of 11 strains of A. oligospora over the first 12 hours of contact with the model nematode Panagrellus redivivus. Strain INTA10 had significantly higher and faster nematicidal activity than the rest (LT50 after 247 minutes, Mean=0.55 n=6 SD=0.24 Tukey post-hoc: F=13.20, p<0.0001; LT100 after 719 minutes, Mean=1 n=6 SD=0 Tukey post-hoc: F=18.29, p<0.0001). Additionally, we studied how in vitro nematicidal activity is affected by the availability of prey, using strain INTA10 with four prey densities, 75, 25, 10, and 3 P. redivivus/cm², over the first 11 hours of contact. Although mortality over time curves followed a similar pattern across treatments, we found significant differences between them, with intermediate prey densities eliciting a faster response, and higher final mortalities. Our results stress the importance of evaluating A. oligospora as individual strains, show how different experimental designs can affect results, and highlight the risks of assuming that results obtained in one strain in certain particular conditions apply to the whole species.

Introduction: Phytopathogens affect all types of agricultural plants in all regions of the world. The annual losses from these pathogens in the global economy amount to tens of billions of dollars. All over the world, great importance is attached to environmentally friendly means of protecting plants from pathogens, so a promising direction is the use of green technologies for the development of biological products based on plant extracts and their natural compounds. The purpose of this study was to evaluate the antifungal activity of Achilléa millefólium extract of the Compósitae family against pathogens that cause plant root rot and to determine their component composition.

Methods: An aqueous-alcoholic extract of the plant Achilléa millefólium was obtained by grinding the leaves, washing with running water and then with sterile water, drying, grinding into powder, and extraction with 70% ethanol, followed by settling and filtering. The antifungal activity of the extract was determined by the agar diffusion method. Fusarium oxysporum, F. solani, and Penicillium notatum, previously isolated from soybean plants affected by root rot, were used as test microorganisms. The component composition was determined using HPLC and gas chromatographic analysis.

Results: The Achilléa millefólium extract was shown to have high antifungal activity; the growth inhibition zones of F. oxysporum, F. solani, and P. notatum were 36.3 mm, 39.6 mm, and 31.0 mm, respectively. The component composition of the extract revealed derivatives of flavones, quercetin, kaempferol, phenolic acids, decanoic acid, etc.

Conclusions: The Achilléa millefólium plant extract and the complex of its natural compounds can be further used as a basis for the development of environmentally friendly drugs against plant root rot pathogens.

Introduction: The field of experimental microbiology allows for the creation of new avenues of biotechnology with the aim of facilitating the extensive utilization of microbial synthesis products in agriculture as plant protection agents. The most prevalent biological substances are antibiotics, alkaloids, peptides, phenolic compounds, lipids, terpenoids, polysaccharides, organic acids, and others. The objective of this study was to assess the fungicidal, bactericidal, and nematocidal activity of carboxylic acids produced by the yeast Yarrowia lipolytica.

Methods: Succinic acid (SA), isocitric acid monopotassium salt (ICA), and α-ketoglutaric acid monopotassium salt (KGA) were synthesized under specific conditions using Y. lipolytica, and subsequently isolated from the culture liquid in crystalline form with 99.9% purity. The fungicidal and bactericidal activities of carboxylic acids were determined by the agar diffusion method. The phytopathogenic bacteria Erwinia carotovora and the fungi Trichothecium roseum, Cylindrocarpon, Aspergillus flavus, Fusarium oxysporum, F. napiforme, and Penicillium casei were employed as test microorganisms. The nematocidal activity of carboxylic acids was investigated against the phytoparasitic stem nematode Ditylenchus destructor.

Results: The selective antimicrobial activity of carboxylic acids produced by Y. lipolytica has been demonstrated. The acids were subjected to testing against six test microorganisms, with four being inhibited. Moreover, it was demonstrated that all acids were capable of inactivating the nematode D. destructor. The nematostatic activity ranged from 13 to 37.6%, while the nematocidal activity ranged from 22.4 to 58.3%. The highest nematocidal activity was observed with SA.

Conclusions: The utilization of SA, ICA, and KGA produced by Y. lipolytica for the purpose of plant protection may be regarded as a highly promising avenue of research, given that pests have not yet developed resistance to these acids. Furthermore, the acids produced by microorganisms are of a higher purity than those that are chemically synthesized.