Different Types Of Crops Research Articles

Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images

Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful in a variety of application areas like agriculture, even at a minor level. Band selection (BS) refers to the process of selecting the most relevant bands from a hyperspectral image, which is a necessary and important step for classification in HSI. Though numerous successful methods are available for selecting informative bands, reflectance properties are not taken into account, which is crucial for application-specific BS. The present paper aims at crop mapping for agriculture, where physical properties of light and biological conditions of plants are considered for BS. Initially, bands were partitioned according to their wavelength boundaries in visible, near-infrared, and shortwave infrared regions. Then, bands were quantized and selected via metrics like entropy, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) from each region, respectively. A Convolutional Neural Network was designed with the finer generated sub-cube to map the selective crops. Experiments were conducted on two standard HSI datasets, Indian Pines and Salinas, to classify different types of crops from Corn, Soya, Fallow, and Romaine Lettuce classes. Quantitatively, overall accuracy between 95.97% and 99.35% was achieved for Corn and Soya classes from Indian Pines; between 94.53% and 100% was achieved for Fallow and Romaine Lettuce classes from Salinas. The effectiveness of the proposed band selection with Convolutional Neural Network (CNN) can be seen from the resulted classification maps and ablation study.

Elements Absorption Mechanisms by Root Systems of Different Types of Crops

Elements Absorption Mechanisms by Root Systems of Different Types of Crops

Multi-parametric multiple kernel deep neural network for crop yield prediction

A rapid, precise and credible prediction of crop yield at a wider scale is more important than ever for crop management, the measurement of food production, food trading and policymaking to address the challenges of environmental change, increased population and food demand. Deep Learning (DL) models are recently well-known for predicting crop yields. Multi-parametric Deep Neural Network (MDNN) is a DL model employed to estimate the crop yield concerning multiple parameters such as climate and soil. A Growing-Degree Day (GDD) has been used to determine the impact of climate change on crop yield. The determined values along with the climate and soil parameters have been learned by the DNN to estimate the yield quality. The MDNN performs well with the huge volume of data and it is not suitable for the medium scale of data. The learning ability of MDNN is improved in this paper by proposing a Multi-parametric Multiple Kernel DNN (MMKDNN) to provide better crop yield prediction for medium-scale data. The effectiveness of the model built in the last hidden layer is solely determined by the intermediate representations of the input. The intermediate representation of the input in a neural network is combined through multiple kernel learning. The MMKDNN with increasing complexity representations is preserved in this way, but the output calculation, i.e. crop yield prediction, is free to use all of the network's knowledge. For five different types of crops, the experiments are conducted to assess the efficiency of the MMKDNN.

Cyantraniliprole impairs reproductive parameters by inducing oxidative stress in adult female wistar rats

Cyantraniliprole is a synthetic insecticide used to control pests of up to 23 different types of crops. It is able to modulate ryanodine-like calcium channels, which are widely found in the organism of insects and mammals. The objective of this research was to verify the possible reproductive effects of adult female Wistar rats exposure to cyantraniliprole. Animals (67 days old) were exposed to the chemical at doses of 10 or 150 mg/kg/day, orally, for 28 consecutive days (control animals received only the vehicle). Vaginal secretions were collected during the exposure period to assess the regularity of the estrous cycle; the liver, kidneys, pituitary gland, adrenal gland, uterus, and ovaries were collected and weighed; reproductive organs were assessed for histopathological evaluation and for biochemical markers of oxidative stress and progesterone plasma level was measured. Both doses caused negative changes in the morphology and redox system of the uterus and ovaries. Animals exposed to 10 mg/kg also exhibited higher level of plasma progesterone, elevated levels of lipid peroxidation in reproductive organs, increased superoxide dismutase activity in the uterus and glutathione peroxidase activity on the ovary, while the 150 mg/kg group exhibited an increment in estrous cycle length and diminished uterine glandular epithelium. Based on these results, we conclude that cyantraniliprole may have acted as an endocrine disruptor, and its effects are mediated by oxidative stress.

Modeling soil erodibility and critical shear stress parameters for soil loss estimation

The rate of soil erosion from agricultural fields is driven by climate erosivity and by soil erodibility or resistance, commonly represented through the interrill (Ki) and rill erodibility (Kr), and critical shear stress (τc) parameters. These parameters are affected by factors showing high variability in time and space, such as soil properties, land use, and management practices. Estimating the site-specific time-varying values for Ki, Kr, and τc can require laborious field or laboratory experiments and detailed datasets. The objective of this research was to develop a non-linear regression model for estimating temporal adjustments for Ki, Kr, and τc considering different types of crops and management practices. One thousand sampling sites were randomly selected across the Kaskaskia watershed in Illinois, and the needed parameters to develop the regression models were extracted from the two-year simulation results of the Water Erosion Prediction Project (WEPP) model on a daily basis. The predicted values of the regression models showed good agreements with the sample data, with the coefficient of determination (r2) ranging from 0.62 to 0.98, capturing the spatio-temporal variations of the three variables under different crops and management practices. In addition, daily soil loss estimations using the predicted adjustments showed good agreements with the WEPP simulations, with r2 ranged from 0.98 to 0.99, confirming their possible use in soil erosion models. This is especially useful as the regression models require only a few explanatory variables, which are available at high resolution across the United States. Therefore, the models will facilitate estimating spatio-temporal variations of interrill and rill erodibility, and critical shear stress and thus a new platform of large-scale soil erosion models can be developed using readily available environmental datasets.

Assessment of soil quality in agroecosystems based on soil fauna

Soil arthropods respond sensitively to land management practices and correlate with beneficial soil functions. The aim of this research was to determine soil quality using the QBS index in different types of crops and influence of soil variables (pH soil, soil moisture, potassium, phosphorus and nitrogen) on soil arthropods. Between the years 2018 and 2020, we studied different types of crops (Brassica napus, Pisum sativum, Triticum aestivum, T. spelta, Zea mays, Grass mixture and Hordeum vulgare) and recorded 14 taxa. Our results suggest a higher QBS index value in crops grass mixture, Pisum sativum, Triticum aestivum, T. spelta. The EMI value grew with increasing values of soil moisture, soil pH, phosphorus, potassium and nitrogen; indicating the presence of soil arthropods occurring in higher quality soil. Our results suggest that agricultural intensification affects soil arthropods, which are important for the production of biomass, which also affects crop yields.

Assessment of soil quality in agroecosystems based on soil fauna

Soil arthropods respond sensitively to land management practices and correlate with beneficial soil functions. The aim of this research was to determine soil quality using the QBS index in different types of crops and influence of soil variables (pH soil, soil moisture, potassium, phosphorus and nitrogen) on soil arthropods. Between the years 2018 and 2020, we studied different types of crops (Brassica napus, Pisum sativum, Triticum aestivum, T. spelta, Zea mays, Grass mixture and Hordeum vulgare) and recorded 14 taxa. Our results suggest a higher QBS index value in crops grass mixture, Pisum sativum, Triticum aestivum, T. spelta. The EMI value grew with increasing values of soil moisture, soil pH, phosphorus, potassium and nitrogen; indicating the presence of soil arthropods occurring in higher quality soil. Our results suggest that agricultural intensification affects soil arthropods, which are important for the production of biomass, which also affects crop yields.

The Composition and Seasonal Variation of Epigeic Arthropods in Different Types of Agricultural Crops and Their Ecotones

Changes in the structures of epigeic arthropods reflect changes in the ecological status of their habitats. The aim of this research was to assess the influence of ecotones and environmental variables (pH soil, soil moisture, potassium, phosphorus, nitrogen) on the abundance of epigeic arthropods. Between 2018 and 2020, an investigation of different types of crops (Brassica napus L., Pisum sativum L., Triticum aestivum L., T. spelta L., Zea mays L., grass mixture) recorded 31,315 individuals belonging to 14 taxonomic groups and their ecotones, using pitfall traps. The abundance of epigeic arthropods was affected by moisture, pH of soil, and levels of phosphorus, potassium and nitrogen. We observed a greater average number of individuals in the ecotones than in the fields in the months of September and October and in Triticum aestivum crops. During other months and for the remaining crops, we did not notice this difference. This contributed new information regarding the ecotone rule. Our results suggest that agricultural intensification affects epigeic arthropods in the crop Triticum aestivum, which is important for the production of biomass and also affects crop yields.

Crop Concentration and Crop Diversification in Birbhum District of West Bengal: Recent Situations

Background: Crop concentration focuses on the pre-dominance of certain crops over other crops in a specific area under study. On the other hand, crop diversification refers to the various types of crops grown in a specific time period under the study area. If the numbers of different types of crops grown in an area during a specified time period is more, the area will have the higher crop diversification and vice versa. So, crop concentration and crop diversification reveal changing pattern of competition among various crops for getting prominence over other crops. Methods: The present study was initiated to have an analysis of present situation regarding the crop concentration and crop diversification in Birbhum district of West Bengal over the years of 2012-13, 2013-14, 2014-15, 2015-16 and 2016-17 through interpretation of the data related with the area, production and productivity data of the major crops of the district obtained from the Govt. of India data source. (aps.dac.gov.in/APY/Public­_Report1.apx). In this study, the crop concentration of Birbhum district is delineated with the help of the crop concentration Index of Bhatia (1965) and crop diversification is assessed with the help of the Gibbs and Martin Index of diversification (1962). Result: The present study clearly finds out that the crop concentration index of Birbhum district is low as the mean of the Indices is 34.01 with corresponding standard deviation (SD) of 0.2997 and co-efficient of variance (CV) of Indices is as low as 0.0088 over a period of five years from 2012-13 to 2016-17. This study also reveals that in Birbhum district only 2 crops having more than 10 per centage point share in the gross cropped area (GCA) of the district provides the farmers a wide scope of farming various different crops or group of crops. The present study also points out that the district has a fairly diversified pallet of crops as the Mean value of crop diversification indices is 00.64754 with a standard deviation of as negligible as 00.0001 with a minute CV of Indices of 00.00016 for the time period of 5 years i.e. from 2012-13 to 2016-17, having potential for further well planned diversification of the crops.

Designing photovoltaic arrays for groundwater pumping for remote agriculture using synthetically generated daily rainfall and irrigation needs

PurposeThis study aims to provide a new method for precisely sizing photovoltaic (PV) arrays for standalone, direct pumping PV Water Pumping (PVWP) systems for irrigation purposes.Design/methodology/approachThe method uses historical weather data and considers daily variability in regional temperatures and rainfall, crop evapotranspiration rates and seasonality effects, all within a nonparametric bootstrapping approach to synthetically generate daily rainfall and crop irrigation needs. These needs define the required daily supply of pumped water to achieve a user-specified level of reliability, which provides the input to an intuitive approach for PV array sizing. An economic comparison of the costs for the PVWP versus a comparably powered diesel generator system is provided.FindingsPumping 22.8646 m³/day of water would meet the pasture crop irrigation needs on a one-acre (4046.78 m²) tract of land in South Florida, with 99.9% reliability. Given the specified assumptions, an 8.4834 m² PV array, having a peak power of 1.1877 (kW), could provide the 1.2347 (kWh/day) of hydraulic energy needed to supply this volume over a total head of 20 meters. The PVWP system is the low-cost option when diesel prices are above $0.90/liter and total installed PV array costs are fixed at $2.00/Watt peak power or total installed PV array costs are below $1.50/Watt peak power and diesel prices are fixed at $0.65/liter.Originality/valueBecause the approach is not dependent on the shapes of the sampling distributions for regional climate factors and can be adapted to consider different types of crops, it is highly portable and applicable for precisely determining array sizes for standalone, direct pumping PVWP systems for irrigating diverse crop types in diverse regions.

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.

Spatial Distribution Characteristics and Risk Assessment of Cadmium Pollution in Soil-crops system of an E-waste Dismantling Area

With the rapid development of electronic technology, soil heavy metal contamination caused by electronic waste dismantling activities has attracted the attention of many researchers. To investigate the contamination status and spatial distribution of Cd in soil-crop systems around an e-waste dismantling area, 171 pairs of soil and crop samples were collected for analysis. The concentrations of cadmium in root vegetable soil, leaf vegetable soil, solanaceous vegetable soil, and orchard soil were (1.292±0.647), (1.010±0.201), (0.921±0.125), and (0.861±0.135) mg·kg-1, respectively. The average values of cadmium in these four soil types were 10.0, 7.8, 7.1, and 6.3 times the background values of soil Cd in Zhejiang Province, respectively, and 4.31, 3.4, 3.07, and 2.72 times the risk screening values for soil contamination of agricultural land, clearly indicating cadmium accumulation in the soil. However, only a small percentage of crops contained cadmium levels that exceeded food safety limits. Moreover, different types of crops showed different capacities for cadmium enrichment and can be ranked accordingly: leaf vegetables > root vegetables > solanaceous vegetables > fruits. The single factor pollution index and the potential ecological risk assessment revealed severe Cd contamination in the study area, with a high potential ecological risk. Cadmium exposure posed a higher health risk for children than for adults. However, the single heavy metal cadmium pollution index does not indicate a threat to local residents at this time. Moran's I index and kriging interpolation results revealed that Cd has significant spatial autocorrelation, with high values mainly concentrating around the e-waste dismantling area, indicating a significant correlation with e-waste dismantling activities.

Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel

The overarching aim of this research was to develop a method for deriving crop maps from a time series of Sentinel-2 images between 2017 and 2018 to address global challenges in agriculture and food security. This study is the first step towards improving crop mapping based on phenological features retrieved from an object-based time series on a national scale. Five main crops in Israel were classified: wheat, barley, cotton, carrot, and chickpea. To optimize the object-based classification process, different characteristics and inputs of the mean shift segmentation algorithm were tested, including vegetation indices, three-band combinations, and high/low emphasis on the spatial and spectral characteristics. Four known vegetation indices (VIs)-based time series were tested. Additionally, we compared two widely used machine learning methods for crop classification, support vector machine (SVM) and random forest (RF), in addition to a newer classifier, extreme gradient boosting (XGBoost). Lastly, we examined two accuracy measures—overall accuracy (OA) and area under the curve (AUC)—in order to optimally estimate the accuracy in the case of imbalanced class representation. Mean shift best performed when emphasizing both the spectral and spatial characteristics while using the green, red, and near-infrared (NIR) bands as input. Both accuracy measures showed that RF and XGBoost classified different types of crops with significantly greater success than achieved by SVM. Nevertheless, AUC was better able to represent the significant differences between the classification algorithms than OA was. None of the VIs showed a significantly higher contribution to the classification. However, normalized difference infrared index (NDII) with XGBoost classifier showed the highest AUC results (88%). This study demonstrates that the short-wave infrared (SWIR) band with XGBoost improves crop type classification results. Furthermore, the study emphasizes the importance of addressing imbalanced classification datasets by using a proper accuracy measure. Since object-based classification and phenological features derived from a VI-based time series are widely used to produce crop maps, the current study is also relevant for operational agricultural management and informatics at large scales.

Crop Classification of Satellite Imagery Using Synthetic Multitemporal and Multispectral Images in Convolutional Neural Networks

The demand for new tools for mass remote sensing of crops, combined with the open and free availability of satellite imagery, has prompted the development of new methods for crop classification. Because this classification is frequently required to be completed within a specific time frame, performance is also essential. In this work, we propose a new method that creates synthetic images by extracting satellite data at the pixel level, processing all available bands, as well as their data distributed over time considering images from multiple dates. With this approach, data from images of Sentinel-2 are used by a deep convolutional network system, which will extract the necessary information to discern between different types of crops over a year after being trained with data from previous years. Following the proposed methodology, it is possible to classify crops and distinguish between several crop classes while also being computationally low-cost. A software system that implements this method has been used in an area of Extremadura (Spain) as a complementary monitoring tool for the subsidies supported by the Common Agricultural Policy of the European Union.

Scientific bases of principles estimating a state of the vegetation cover in steppe agrocenoses using innovative methods of smart agriculture

The study’s main target is to actualize notions about spatial heterogeneity of steppe agrocenoses in conditions of the current climatic and anthropogenic changes, scientific grounds of instrumental methods estimating a state of the vegetation cover, and to identify optimal phytometric parameters of high productive agrocenoces of crops. Industrial winter wheat plantings and demonstrative seeds of spring wheat, sunflower, barley, maize, soya, winter rye, and winter wheat on the chernozem in Orenburgskaya oblast were objects of the study. High technological agrocenoses of spring wheat on chernozem in Samarskaya oblast and winter wheat on dark-chestnut soils in Saratovskaya and Volgogradskaya oblasts were used in the research. Normalized Difference Vegetation Index (NDVI) of crops was measured by a portable device-Green Seeker Handheld Crop Sensor, Model HCS – 100. The assimilating area of plants was estimated by the gravimetric method. Generally accepted practices of the statistical analysis were used in the processing of digital material. As a result of the study, spatial differences in the vegetation cover state of field agrocenoses were detailed by its instrumental examination. It was ascertained that the vegetation cover’s heterogeneity began to show at the start of vegetation and did not level itself. In the following stages, we could see that proved reasonability to examine plantings instrumentally starting from the early stages when the technical correction was possible. Detection of optimal phytometic parameters of agrocenoses of different types of crops in regions of the steppe zone in the European part of Russia by NDVI evaluation on stages of plant development in zonal climatic and soil conditions under high levels of agro technologies was offered to estimate a state of the current plantings and to make technological solutions correcting the course of vegetation.

Retracted: Soil monitoring and evaluation system using EDL‐ASQE: Enhanced deep learning model for IoT smart agriculture network

SummaryThe enormous growth of the Internet of Things (IoT) network provides abundant support to agriculture and development, which states the future scope of IoT‐based agriculture. In a recent scenario, agriculture IoT can be integrated with sensors, communication protocols, and microcontrollers for automated process executions to increase productivity. Moreover, deep learning effectiveness produces appropriate results and solves several real‐time issues related to agriculture‐based advancements. The proposed system presents the design of an IoT network communication system to estimate the soil conditions. Soil quality is an important factor in modernized agriculture, productivity enhancement, and hydrological cycles. By the soil quality analysis, the accurate prediction is very significant for sensible usage of resources. An enhanced deep learning model for IoT network‐based automated soil quality evaluation observes the complex soil features and meteorological factors with those concerns. Here, the real‐time samples are collected from the local area sensor network for analysis. The deep learning model is developed with big data fitting ability for soil quality prediction. The weight factors (W.F.) are derived for measuring the soil quality accurately. The proposed IoT network‐based agriculture structure allows a flexible approach to different types of crops and implementation in agricultural areas. Experimental results obtained in the laboratory and onsite confirmed the performance and reliability of the system. The result evaluations are carried out based on precision, accuracy, and processing time, and results show that the model achieves better results than compared models.

Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms.

Crop classification in early phenological stages has been a difficult task due to spectrum similarity of different crops. For this purpose, low altitude platforms such as drones have great potential to provide high resolution optical imagery where Machine Learning (ML) applied to classify different types of crops. In this research work, crop classification is performed at different phenological stages using optical images which are obtained from drone. For this purpose, gray level co-occurrence matrix (GLCM) based features are extracted from underlying gray scale images collected by the drone. To classify the different types of crops, different ML algorithms including Random Forest (RF), Naive Bayes (NB), Neural Network (NN) and Support Vector Machine (SVM) are applied. The results showed that the ML algorithms performed much better on GLCM features as compared to gray scale images with a margin of 13.65% in overall accuracy.

Enhanced Convolutional-Neural-Network Architecture for Crop Classification

Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.

The Plant-Growth-Promoting Fungus, Mortierella elongata: Its Biology, Ecological Distribution, and Growth-Promoting Activities

Recent studies show that Mortierella elongata can perform plant-growth promotion across different types of crops, including bahiagrass, corn, tomato, squash, and watermelon. Compared to the well-known species that have plant-growth-promotion abilities (e.g., mycorrhizal fungi, Trichoderma), growers, agents, and stakeholders are not familiar with "Mortierella" despite the important role these fungal taxa play in promoting the growth of their crops. This new 5-page publication of the UF/IFAS Department of Soil and Water Sciences provides a brief overview of Mortierella from biological, taxonomical, ecological, and functional perspectives to help readers learn the biology and potential modes of action of this fungus. Written by Hui-Ling Liao.https://edis.ifas.ufl.edu/ss679

Field research: A comparative analysis between conventional, organic and sustainable methods of tomato production

Agriculture represents one of the main pillars of the Brazilian economy, its importance being related to food security and the generation of job opportunities. However, it is necessary to have a critical reflection on the sustainability of planting. Among the different types of crops, tomatoes stood out as one of the most planted and consumed fruits in the world. This article provides a comparative assessment between three types of tomato planting: conventional, organic and sustainable (TOMATEC®), from soil preparation to commercialization on the market. The work was carried out in the north of the state of Rio de Janeiro, together with groups that produce the fruit in these three types of planting. The methodology was based on an unstructured questionnaire, with free responses, applied to farmers in the region. We believe that this study will contribute to the orientation of society through data obtained from serious information processing criteria. The main results showed, through the sustainable planting system of EMBRAPA (innovation), that it is possible to use pesticides with environmental awareness and produce fruit free of residues. Diseases, in the conventional system, are controlled by the application of fungicides and bactericides. In sustainable planting, a mixture of homemade detergent with soy oil, Bordeaux mixture, cow’s milk, contact fungicides and systemic fungicides is used, and in the organic production system, it is common not to let the disease settle in the plant, through preventive control of soil preparation and protection. In pest control, the conventional system performs the application of insecticides composed of various active principles. In the organic system, the control of insects is privileged by balancing the soil, with this, the plants acquire greater resistance to diseases and pests. In the sustainable system, there is no preventive treatment, but curative. Market fruit prices for conventional planting fluctuate and depend on supply, while tomatoes from organic and sustainable systems do not fluctuate. Organic production does not have the installed capacity to meet market demands. With this, the sustainable system has been gaining space in the market and expanding in the Southeast and South of the country.