Tomato Crops Research Articles

Color Dominance-Based Polynomial Optimization Segmentation for Identifying Tomato Leaves and Fruits

Optimization processes or methods play an essential role in the continuous improvement of various human activities, particularly in agriculture, given its vital role in food production. In precision agriculture, which utilizes technology to optimize food production, a primary goal is to minimize the consumption of resources like water, fertilizers, and the detection of pests and diseases. In the fertilization process, it is essential to identify any deficiencies or excesses of chemical elements. Nutrient deficiencies, which are essential for plant development, are typically detected in the leaves of crops. This paper proposes a methodology for optimizing the color threshold dominance factors employed in the segmentation process for tomato crop leaves and fruits. The optimization is performed using an interpolation method to find the values that maximize the segmentation of leaves and fruits used by the color dominance segmentation method. A comparison of the interpolation method results with those obtained using a greedy algorithm, which iteratively finds the optimal segmentation values, shows nearly identical outcomes. Similarly, a UNetmodel is used for semantic segmentation, the results of which are inferior to those obtained by the proposed interpolation optimization method. The most significant contribution of the interpolation method is that it requires only a single iteration to generate the initial data, in contrast to the iterative search required by the greedy algorithm and the lengthy training process and video card dependency of the UNet model. This results in an 80% reduction in computation time.

Occurrence of Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) Middle East–Asia Minor 1 (MEAM1) and Mediterranean (MED) in Commercial Fields of Solanum lycopersicum in Brazil

The tomato (Solanum lycopersicum) is an important crop to the economy of Brazil, and the whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is one of the limiting factors responsible for reducing its yields. These insects are part of a cryptic species group present across almost the entire globe. The most relevant cryptic species in the world are B. tabaci MEAM1 and MED due to their capability to adapt and cause damage to vegetables, grain, and ornamental crops. The arrival of MED in Brazil through the state of São Paulo represents risks to farmers in the region due to the difficulty in managing these insects. This study assessed the occurrence of both species in tomato crops in the southeastern region of Brazil in 2020 and 2021. An amount of 79 samples containing 767 insects were collected throughout the study period, and in the cities of Sumaré (SP) and Monte Mor (SP), several samples were collected from the same location throughout the year. The insects were stored and sent for molecular analysis. The results showed an increase in MED compared to MEAM1. The presence of MED in the Minas Gerais samples was not recorded. However, a higher percentage of MED was observed in the state of São Paulo, which was detected in the municipalities of Sumaré and Monte Mor. These results possibly indicate that MED could be starting to stabilize in open tomato fields in Brazil.

Effect of Different Intensities of Leaf Removal on Tomato Development and Yield

Defoliation (leaf removal or pruning) is a common practice in tomato production that makes crops more manageable, prevents conditions conducive to fungal attack and increases the exposure of the fruit to light, especially in winter conditions. The intensity and frequency of leaf removal on commercial farms often vary according to workforce availability criteria, which makes it difficult to determine their effect on tomato crop yields. It would be reasonable to think that a reduction in leaf area influences radiation interception and, therefore, the production of assimilates and biomass. However, in intensive production systems with a high leaf area index (LAI), leaf pruning can increase radiation interception, either by reducing competition between productive and vegetative organs or by increasing radiation use efficiency. This study was therefore designed to assess the effect of different intensities and frequencies of basal leaf removal on dry matter production and partitioning between the different organs of the plant, and thus on tomato crop productivity. A series of trials were conducted over three consecutive seasons, with a trial conducted per season: (a) Trial 1: leaf removal control—LRC (with leaves removed from the base to two leaves below the truss close to harvest, T0) was compared with LR1 (leaf removal from the base to two leaves below the truss above T0, i.e., T1) and LR2 (two trusses above T0 (T2)); (b) Trial 2: LRC compared with LR2 and LR4 (four trusses above T0 (T4)), carried out at two frequencies; and (c) Trial 3: LRC compared with an intense leaf removal treatment (LRI) whereby between 10 and 12 leaves were left on each stem. LAI saturation values under our conditions were found to be around 2.0. No significant differences in yield were found between the control and treatments LR1, LR2 and LR4, with a reduction in the number of leaves of up to 35% and LAI values during harvest above 2.0. The intense leaf removal treatment (LRI), which reduced the number of leaves by 47% and the LAI value from 2.8 to 1.5 compared to the control, resulted in a 15% reduction in dry biomass and a 17% decrease in fruit yield.

Silicon Application Contributes to Efficient Gas Exchange in Tomato Plants

The study aimed to analyze the effect of silicon application on tomato crops subjected to different water management conditions. The experiment was conducted in a completely randomized design with two water replacement conditions (60 and 100% of crop evapotranspiration - ETc), four forms of silicon application (no application, full-dose soil application, split soil application and foliar application) with four replicates. When the plants were in the reproductive stage, gas exchange analysis was performed in two periods (critical period before water replacement and after water replacement). The photosynthesis rates, internal CO2, stomatal conductance, transpiration, and instantaneous water use efficiency were determined. The data were subjected to analysis of variance and the means compared by the Tukey test with 5% significance. The application of silicon caused significant variation in gas exchange in tomato leaves, with an increase in rates and an increase in instantaneous water use efficiency. The application of silicon promotes an increase in gas exchange in tomato plants, whether in adequate water conditions or with a deficit in the crop. The application of the element, via soil and foliar, showed a significant increase in gas exchange in tomato plants. The best results were obtained for application via soil, regardless of the water condition or time of evaluation.

NeRF-based 3D reconstruction pipeline for acquisition and analysis of tomato crop morphology

NeRF-based 3D reconstruction pipeline for acquisition and analysis of tomato crop morphology

Effects of green seaweed (<i>Ulva onhoi</i>) on the reproductive development of tomato (<i>Solanum lycopersicum)</i> plants

Algae-derived products have great potential as crop biostimulants due to their multiple beneficial effects at different stages of plant development. Green seaweeds of the genus <i>Ulva</i> are well suited for this purpose because they are widely distributed and grow rapidly in a wide range of conditions. In this study, a greenhouse experiment was conducted to evaluate the effects of dry seaweed powder (DSP) and liquid seaweed extract (LSE) of <i>Ulva ohnoi</i> on the reproductive development of tomato plants. The experiment included three treatments: 1) plants treated with 5 g of DSP, 2) plants treated with 250 mL of LSE, and (3) control plants (without algae). The reproductive parameters, chlorophyll content, and mineral composition were measured during the flowering and early fruiting periods. The application of DSP was the most effective treatment in promoting early flowering and significantly increased the number of buds (103%), flower clusters (55%), flowers (61%), and fruits (45%) per plant. Furthermore, the DSP-treated plants exhibited an enhancement in the levels of chlorophyll and nutrients in the plants and fruits. The results of the current work show that the application of <i>U. ohnoi</i> in its natural form (dry powder) stimulates the reproductive development of crop tomato. This represents a sustainable and natural alternative to synthetic inputs that growers can incorporate into horticultural production to improve yield attributes.

Modified Surface Drip Irrigation and Hydraulic Barrier Impacts on Soil Moisture and Water Productivity for Tomatoes in a Greenhouse

Considerable amounts of irrigation water of vegetable crops grown in homogenous sandy soil profiles could be subjected to deep percolation water losses due to inappropriately designed surface or subsurface drip irrigation methods. This study aimed to investigate the combined influence of implementing clay soil layer in homogenous sandy soil profile of low-tech greenhouse ridges and using modified surface drip irrigation (M-DI) on soil moisture distribution and water productivity of tomatoes. In the greenhouse, a 7.5 cm thick clay soil layer was implemented 15 cm from the soil surface of each ridge as a hydraulic barrier. Three irrigation regimes (100%, 70% and 50% of ETo) were imposed with the M-DI on tomato plants and 100%ETo with surface drip irrigation (DI) as control. Regarding economic valuation, viability was preserved for the M-DI and DI methods. The outcome indicated that soil moisture spreads more horizontally than vertically on the sandy soil above the clay soil layer. The combined effect of the homogenous sandy soil profile amendment and full irrigation (100%ETo) with the M-DI irrigation method increased the tomato fruit yield by 64.5%. Furthermore, the combined influence enhanced water productivity by the M-DI to 54.7 kg/m3 compared to 32 kg/m3 by the DI. However, M-DI demonstrated dominance over DI regarding returns, yield, and profit. Economic-wise, the M-DI requires 50% less of the lateral pipelines needed by the DI in low-tech greenhouses. Adopting the M-DI with a hydraulic barrier can improve soil moisture, water productivity, yield, and returns for tomato crops in low-tech greenhouses under sandy soil conditions. Also, the M-DI with the hydraulic clay barriers was an economically viable investment compared to the DI without clay barriers for growing tomatoes in low-tech greenhouses.

Water saving based on moisture observations: scheduling drip irrigation regimes for tomatoes under greenhouse conditions in Tajikistan

This study aimed to examine the efficiency of different drip irrigation regimes in spring film greenhouses for early tomato harvesting. The field (small-plot) experiment included designing and testing the watering technology suitable for rural Tajikistan. Irrigation, soil moisture, and tomato growth were followed for several seasons under four irrigation pilots. The experiments showed that the most effective drip irrigation regime for Elpida hybrid tomato was carrying it out while regulating soil moisture within 75-85%, with the irrigation demand of 4,978 m3/ha and irrigation norm of 99.0 m3/ha, also contributing to better water efficiency. The volume of irrigation water for the production of one unit of tomato crop in the second experiment was 5.73 m3/ha, i.e. 14.18% less than in the control plot. The study showed that the tomato evapotranspiration coefficient tends to increase as the threshold of soil mois-ture before and after irrigation grows. The maximum net yield amounted to 1,342 thou. Somoni/ha – 1.9 times or 52.7% higher compared to the control plot. The research findings can guide individual farmers and production facilities, as well as the overall development of agrarian economies like Tajikistan.

Characterization and Evaluation of an Electrostatic Knapsack Sprayer Prototype for Agricultural Crops

Pesticide application development has grown exponentially in recent decades thanks to the implementation of new technologies and improved quality of spray input application. Electrostatic technology for increasing deposition has proven to be a suitable tool under specific study conditions, such as when working with very small droplet sizes, with air assistance, or typically in greenhouse environments. However, its effectiveness in hydraulic spraying, as well as its application from a commercial point of view in agriculture, is still challenging. The aim of this study was to evaluate the performance of this technology by implementing a modified lance on a small commercial knapsack sprayer, equipped with a hydraulic nozzle providing a range of droplet size values (Dv50) from 136 μm to 386 μm in the pressure range between 2 and 6 bar. This setup allowed operation under normal conditions (disconnected electrostatic system: NES) or with the connected electrostatic system (ES), with both configurations being tested in this study. Liquid distribution profiling as well as qualitative and quantitative evaluation of deposition were carried out both under laboratory conditions and in tomato crops under greenhouse conditions. The results showed no differences between the ES and NES in terms of flow rate (L min−1) characterization or in the total accumulated volume collected with the vertical bench. The impact of the electrostatic system connection was clearly observed in laboratory trials, with total deposition increases of up to 66%. In field trials, this effect decreased in unexposed areas and in denser sections of the crop. However, the overall increase in deposition, mainly associated with the exposed side, continued to be significant.

First report of Sclerotinia minor causing soft rot of Scabiosa atropurpurea in California.

In May of 2019, Scabiosa atropurpurea samples with brown discoloration, soft rot of the crown and lower stem, with presence of white mycelium and black sclerotia (Supp. Fig. 1A, B) were collected from a 0.10 ha open field diversified cut flower production in San Luis Obispo County, CA. Approximately 30 to 40% of the scabiosa crop planted in a quarter of the field, exhibited symptoms. Symptomatic crowns and lower stems from five plants were surface disinfested by rinsing in 0.1% Tween 20, soaking in 70% ethanol for 30 s, 1% sodium hypochlorite for 2 min and sterile water. Disinfested tissue was placed in 1/10 potato dextrose agar (PDA) and incubated at 20°C (12 h photoperiod). Resulting colonies (n = 5) formed abundant white mycelia, with black sclerotia formed on the outer edge of the plates after two weeks (Suppl. Fig. 1C). Sclerotia (n = 50) had an average size of 1.6 (± 0.19) mm in diameter. Morphological identification resulted in Sclerotinia sp. (Hao et al., 2003). The pathogen was further identified by DNA extraction of two hyphal tipped isolates, followed by amplification and sequencing of the rDNA internal transcribed spacer (ITS) region, ITS1/ITS4 (White et al. 1990), calmodulin (CaM), CAL-228F/CAL-737R (Carbone and Kohn, 1999), and DNA replication licensing factor Mcm7 Mcm7-709for/Mcm7-1348rev (Schmitt et al., 2009). NCBI BLAST searches with consensus sequences for each maker revealed 99 to 100% identity with S. minor ex-types for all loci (Supp. Table 1). A maximum parsimony multilocus phylogenetic analysis clustered Californian isolates with reference strains of S. minor (Supp. Fig. 1F). Sequences were deposited in GenBank (Supp. Table 1). Pathogenicity tests were conducted with isolate CS435, which was transferred onto PDA plates and incubated at 20°C for one week. Inoculum consisted of CS435 infested PDA plugs (1 cm3). In the greenhouse, the experiment was set as a complete randomized design and observed for six weeks. Fourteen-week-old scabiosa 'Merlot Red' grown in 3.78 L pots (n = 6), were inoculated by wounding plants at 0.5 cm above the crowns with a 1 mm probe. Inoculum was placed directly on top of the wound and was secured with parafilm. Negative control plants (n = 6) were wounded as above and inoculated with PDA plugs. In experiment 1(19.4 (± 3) °C, RH 46.9), 83% of plants exhibited yellowing of the lower leaves and wilting at one week post inoculation (wpi). Symptoms progressed over time until wilting, major leaf and stem necrosis, was observed in all inoculated plants (Supp. Fig 1E, D). Plant mortality incidence at five wpi was 83%. Pathogen signs including white mycelia and black sclerotia were also observed. In experiment 2 (20.0 (± 10) °C, RH 39.6), 66% of the total plants were symptomatic at five wpi: 33% exhibited yellowing of the lower leaves and wilting, and 33% of plants died. Disease did not develop in non-inoculated plants in either experiment. S. minor was successfully reisolated from surface disinfested tissue of at least 50% (Exp. 1) and 100% (Exp. 2) of inoculated plants, yielding identical sequences to those of the inoculated isolate. S. minor is a soil-borne pathogen that infects tomato, lettuce, brassica, and sunflower crops in California. This study stablishes S. minor as a new soil-borne pathogen of Scabiosa in California. Soil-borne pathogens are a recurrent issue in the cut flower industry, characterizing new pathogens in these crops can inform crop advisors and disease diagnostician to improve disease management in the ornamental industry.

Reverse-transcription recombinase-aided amplification and CRISPR/Cas12a-based universal detection for fast screening and accurate identification of six pospiviroids infecting Solanaceae crops.

Pospiviroids, members of the genus Pospiviroid, can cause severe diseases in tomato and other Solanaceae crops, causing considerable economic losses worldwide. Six pospiviroids including potato spindle tuber viroid (PSTVd), tomato chlorotic dwarf viroid (TCDVd), tomato planta macho viroid (TPMVd), Columnea latent viroid (CLVd), pepper chat fruit viroid (PCFVd), and tomato apical stunt viroid (TASVd) are regulated in many countries and organizations. Rapid, accurate detection is thus crucial for controlling the spread of these pospiviroids. For simultaneous detection of these six pospiviroids, we developed a rapid, visual method that uses a reverse transcription recombinase-aided amplification (RT-RAA) assay coupled with a clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 12a (CRISPR/Cas12a) system. In particular, this technique could achieve both universal detection and specific identification of the six target pospiviroids within 40 min. The universal detection could diagnose the six target pospiviroids in a single reaction, and the specific identification could identify each target pospiviroid without cross-reactivity of other pospiviroids. The sensitivity limits for the target pospiviroids detection with the proposed detection method were higher than those of the conventional reverse transcription-polymerase chain reaction (RT-PCR) method. We designed an RT-RAA-CRISPR/Cas12a-based universal detection method for both large-scale screening and accurate identification of the six target pospiviroids, which is appropriate for on-site detection. Our study results can aid in performing rapid, large-scale screening of multiple pests simultaneously. © 2024 Society of Chemical Industry.

Estimating Energy Needs for Climate-Controlled Greenhouses in Syria with a Software Tool

Amid the current conditions in Syria, the study of energy consumption within plastic greenhouses emerges as a fundamental element in the agricultural economy, especially in areas subject to extreme climate variations. With many thermal power stations ceasing operation due to conflicts and the diminishing sources of energy, understanding energy consumption becomes more urgent to enhance productivity and reduce costs. Successful management of protected agriculture requires in-depth knowledge of weather dynamics and the optimal environmental conditions for crops. To implement effective management of plastic greenhouses, it is essential to recognize how climatic fluctuations affect plant growth and production throughout the various seasons. Heating systems form a significant part of the costs in constructing plastic greenhouses, and deficiencies in these systems can lead to negative impacts on quality, quantity, duration of cultivation, and production volume. Therefore, accurately calculating heating costs is crucial for reducing operational expenses. This study included the development of a computer program to determine the heating needs of plastic greenhouses, considering various factors such as the geographical location of the greenhouse, crop type, covering materials, heating system used, and land area. The results showed that Syria needs 4.56 megawatts of energy for the greenhouses, with the Tartus Governorate consuming the largest share, with energy consumption rates in Tartus, Latakia, Homs, and Damascus countryside amounting to 3.6, 0.3, 0.51, and 0.19 megawatts, respectively. The crops of tomatoes, vegetables, strawberries, and tropical plants consumed 2.2, 1.66, 2.21, and 0.244 megawatts of energy, respectively. This study is an important step towards achieving sustainable and efficient agriculture that contributes to supporting the economy and protecting the environment in Syria.

Remote sensing detection of plastic-mulched farmland using a temporal approach in machine learning: case study in tomato crops.

The increasing use of plastics in rural environments has led to concerns about agricultural plastic waste (APW). However, the plasticulture information gap hinders waste management planning and may lead to plastic residue leakage into the environment with consequent microplastic formation. The location and estimated quantity of the APW are crucial for territorial planning and public policies regarding land use and waste management. Agri-plastic remote detection has attracted increased attention but requires a consensus approach, particularly for mapping plastic-mulched farmlands (PMFs) scattered across vast areas. This article tests whether a streamlined time-series approach minimizes PMF confusion with the background using less processing. Based on the literature, we performed a vast assessment of machine learning techniques and investigated the importance of features in mapping tomato PMF. We evaluated pixel-based and object-based classifications in harmonized Sentinel-2 level-2A images, added plastic indices, and compared six classifiers. The best result showed an overall accuracy of 99.7% through pixel-based using the multilayer perceptron (MLP) classifier. The 3-time series with a 30-day composite exhibited increased accuracy, a decrease in background confusion, and was a viable alternative for overcoming the impact of cloud cover on images at certain times of the year in our study area, which leads to a potentially reliable methodology for APW mapping for future studies. To our knowledge, the presented PMF map is the first for Latin America. This represents a first step toward promoting the circularity of all agricultural plastic in the region, minimizing the impacts of degradation on the environment.

Enhancing tomato crop resilience to water stress: the role of Trichoderma afroharzianum T22 and Nesidiocoris tenuis management

Enhancing tomato crop resilience to water stress: the role of Trichoderma afroharzianum T22 and Nesidiocoris tenuis management

Agriculture Robot Using Image Processing

Background: Agriculture is a cornerstone of the economy, but traditional farming is labor-intensive and time-consuming. Advances in technology, such as Convolutional Neural Networks (CNNs) and robotics, can help address challenges like disease detection, weed control, and labor shortages. Tomatoes, a key crop, benefit from proper management using these innovations. Objective: This project aims to design and develop an agriculture robot using IoT and Machine Learning to automate farming tasks, detect tomato plant diseases, and manage weeds more efficiently. Methods: The robot is controlled by an ESP32 microcontroller connected via Wi-Fi for real-time monitoring. The system uses CNNs for disease detection in tomato plants, along with a custom-trained dataset using the TensorFlow framework. Mechanical arms perform tasks like seeding, watering, spraying fertilizers, and harvesting. A user interface for disease detection was designed for real time disease detection. Results: The model achieved an accuracy of 95.0% for disease detection, 95.56% for fruit detection, and 95.14% for plant detection. These results highlight the effectiveness of the robot in managing tomato crops. Conclusion: The agriculture robot provides a comprehensive solution to key farming challenges, enhancing efficiency through automation and improving crop health monitoring. The integration of IoT and ML offers a pathway toward sustainable and profitable agriculture, particularly in managing tomatoes. Further improvements and scaling could expand its application to other crops and farming tasks. Keywords: Machine Learning, Internet of Things, Agricultural Robot, Disease Detection, Convolutional Neural Networks, Mechanical Tasks.

Tomato Pedicel Physical Characterization for Fruit-Pedicel Separation Tomato Harvesting Robot

To solve the problem of the lack of physical properties of pedicels and the changing pattern for designing the end-effector of tomato harvesting robot and different harvesting modes, research was conducted on the physical properties of tomato pedicels and their change patterns. Using a Universal TA texture analyzer, tensile, three-point bending, and shearing tests were performed on tomato pedicels in the early firm-ripening stage. The tomato variety used was Syngenta Spectrum, cultivated seasonally with two crops per year. Spring crop tomatoes were used in this study. The experimental results provide a theoretical basis for designing tomato harvesting robots across three harvesting modes. Tensile tests measured the pull-off force and tensile strength of the abscission zone with varying diameters. These results are crucial for designing robots using a tensile harvesting mode. The location of the tomato pedicel significantly affects the shearing force. A one-way test was conducted on the shearing part. The results showed that the shearing force and energy required for the proximal pedicel are significantly greater than for the distal pedicel. To reduce the shearing force and energy needed by the end-effector’s shearing mechanism on distal pedicels, a response surface test was conducted. Three factors were examined: shearing speed, angle, and distal pedicel diameter. Design–Expert software optimized these factors to minimize shearing energy and force, leading to the best shearing parameters for different distal pedicel diameters. From the three-point bending tests, the average maximum bending breaking force, bending modulus, and bending strength of the tomato abscission zone were determined. These findings offer a theoretical basis for designing tomato harvesting robots with a bending-type harvesting mode.

Multi-Objective Optimization based decision-making process using Dombi Logarithmic aggregation operator in CNN environment and its application to optimally select suitable greenhouse site for tomato crops

In this research article, we have defined Dombi Logarithmic aggregation operational laws in the framework of Cylindrical Neutrosophic Numbers (CNN) and utilized these laws to establish a new aggregation operator namely Cylindrical Neutrosophic Dombi Weighted Logarithmic Aggregation Operator(C N DW L A ). The said aggregation operational laws & aggregation operator have been used to present a new and novel decision making process where Full Consistency Method (FUCOM) and Multi-Objective Optimization(MOO) have been inte- grated and embedded fruitfully. Here the objective functions were formulated using the concept of a single-layer neural network and the FUCOM method to assess criterion weights. We have resolved the most favorable Pareto optimal solution derived from Multi-Objective Optimization by employing simulation and the Technique for Or- der of Preference by Similarity to Ideal Solution (TOPSIS) approach. Finally, we have applied our proposed decision making method along with MARCOS and MOORA techniques to determine the best greenhouse site for cultivating tomato crops. An exhaustive sensitivity and comparative analysis has been conducted to assess the robustness and stability of our Multi Criteria Group Decision Making (MCGDM) techniques. It is observed that our proposed method has some advantages in cylindrical neutrosophic environment and it produces stable robust results with the variation of underlying model parameters.

Estimación de la huella hídrica agrícola del DR 011, alto río Lerma

In the search for indicators that help measure the impact of human activities on the environment and natural resources, there is one that is very useful as an indicator of demand for global water resources. Estimating the water footprint of agricultural production allowed the identification of crops that can reduce it in favor of increasing water use efficiency. Hoekstra et al. (2011) methodology was used to estimate the water footprint of agricultural products in Irrigation District 011. It was found that of 14 crops in the district, in the average total water footprint in the irrigation modules (dam3 t-1), those of peanuts, beans, and nopal report the highest levels (1.7, 1.6, and 1.8, respectively), while those of lettuce, husk tomato, and carrot crops are the lowest (0.15, 0.29, and 0.25, respectively). Of the water footprint of total agricultural production (dam3), it was observed that corn participates with 43.4%; however, it accounted for 52.8% of total production. The peanut and alfalfa crops in module 05 are economically unaffordable, with high blue water costs per tonne ($8 623.00 and $11 914.00); nevertheless, they occupy 1% of the planted area. The variation of the water footprint of crops among the irrigation modules obtained helps identify the agricultural practices that contributed to increasing yields and optimizing the application of irrigation, consequently providing greater economic benefits to producers.

Investigating a Spectrum of Machine Learning Methods for Leaf Disease Detection in Pepper, Potato, and Tomato

An analysis of machine learning (ML) techniques such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF) and Naive Bayes (NB) was done to detect diseases among pepper, potato, and tomato crops to develop an algorithm to extract multiple features such as kurtosis, GLCM energy, skewness etc and to find region of interest (ROI). First, the sample images were resized and converted into greyscale. Then k-means clustering was used for segmentation. The cluster with the highest contrast or most significant visual features was chosen as the ROI and then passed to the machine learning models to predict the accuracy of finding the diseases. For experimentation purpose 20,639 leaf images of potato, tomato, and pepper plant is taken. From the dataset, 70% were used for training, while 30% of these images were used for testing. The data (biased and unbiased) was passed to these models in shuffled and un-shuffled orders, and it was found that Random Forest gave the highest accuracy i.e. 94.2% in the case of biased data and 89.90% with unbiased data. Hence, it is observed that the proposed algorithm works well for large number of images.

Direct effects of Xenorhabdus spp. cell-free supernatant on Meloidogyne incognita in tomato plants and its impact on entomopathogenic nematodes

Entomopathogenic Xenorhabdus spp. bacteria, symbiont of the nematode Steinernema spp., shows potential for mitigating agricultural pests and diseases through bioactive compound production. The plant-parasitic nematode (PPN) Meloidogyne incognita affects the yield and quality of numerous crops, causing significant economic losses. We speculate that Cell-Free Supernatants (CFS) from Xenorhabdus spp. could reduce the impact of the root-knot nematode (RKN) M. incognita without negatively affecting entomopathogenic nematodes (EPNs), which are considered beneficial organisms. This study explored the activity of seven CFS against M. incognita (two populations, AL05 and Chipiona) and their possible effects on EPNs. The in vitro impact of CFS at 10 %, 40 %, and 90 % concentrations on nematode motility at four and 24 h were tested on the PPN M. incognita and two EPNs, S. feltiae and H. bacteriophora. Additionally, EPN viability and virulence were evaluated at two and five days. On the other hand, tomato plant-mesocosm experiments examined the activity of four CFS on M. incognita reproductive capacity and EPN virulence. In vitro exposure of M. incognita to 90 % concentration of CFS resulted in reductions of activity over 60 % after four hours of expossure in four out of seven CFS. In the in vitro evaluation of two species of EPNs, none of the CFS affected the activity across any tested doses after four hours of exposure nor after 24 h. Plant-mesocosm experiments showed that CFS application significantly reduced RKN galls, egg masses, and galling index. However, the virulence of both EPN species decreased 15 days after application, with a significant impact on S. feltiae. Overall, these findings suggest that CFS could be used as a bio-tool against M. incognita in tomato crops, mitigating its impact on plant growth. However, this study also highlights the necessity of investigating the effects of CFS on non-target organisms.