Tomato Farmers Research Articles

Effects of Different Nitrogenous Fertilizers and Potassium Doses on Yield, Yield Components, and Fruit Quality of Tomato Grown in Alkaline and Acidic Soils

Different nitrogen fertilizers and potassium applications have an important impact on the yield and yield components and fruit-quality parameters of tomatoes in both alkaline and acidic reaction soils. This study investigated the effects of different nitrogenous fertilizers and potassium doses on the yield, yield components, and fruit quality of tomatoes grown on alkaline and acidic soils. The experiment was carried out in the research greenhouse of the Agriculture Faculty, at Selcuk University. It was performed by using two different soil types, three nitrogen fertilizers [ammonium sulfate (AS), ammonium sulfate with inhibitor (AS+inh.), and calcium nitrate (CaNit)], and three different concentrations of potassium sulfate (0, 240, and 480 mg K2O kg-1). We conducted the study by using a randomized experimental plot design with four replications. According to the research results, the highest fruit yield in alkaline soil was 'inh. AS and 480 mg K2O kg-1', while the highest fruit yield in acid soils was obtained with 'calcium nitrate and 240 mg K2O kg-1'. Furthermore, different nitrogen sources and potassium doses significantly affected tomato yield components such as stem diameter, plant height, number of fruits per plant, fruit weight, fruit diameter, fruit hardness, fruit pH, and Brix values. According to the results of the completed soil analysis, the applications demonstrated the importance of accurate and balanced fertilization for high-yield and quality tomato production in different soils. In conclusion, this study highlights the importance of selecting appropriate nitrogen fertilizers and potassium doses to increase tomato yield and yield components in alkaline and acidic soils. It provides valuable insights for tomato farmers and researchers seeking to improve tomato cultivation techniques in different soil conditions.

Design and Development of IoT Smart Drip Irrigation and Fertigation Prototype for Small and Medium Scale Farmers. Acase Study of Tomato Farmers in Tanzania

Design and Development of IoT Smart Drip Irrigation and Fertigation Prototype for Small and Medium Scale Farmers. Acase Study of Tomato Farmers in Tanzania

Impact of postharvest handling technologies on income of tomato (Solanum Lycopersicum L.) smallholder farmers in Mvomero, Morogoro, Tanzania

This study investigates how smallholder tomato farmers in Mvomero District, Tanzania, utilize postharvest handling technologies (PHTs) and analyzes the factors influencing their adoption. We also assess the impact of PHTs on farmer income. Using a cross-sectional survey of 201 farmers, the study finds that a variety of PHTs are used, including lower-energy cool storage, Coolbot™ units, zero-energy cool chambers, and improved containers. Probit and ordinary least squares models reveal that factors such as farmer age, male gender, access to specific PHTs (Coolbot™, zero-energy chambers, improved containers), extension services, credit access, and shade use all significantly influence the adoption of PHTs. The study suggests that policymakers should design interventions considering different income levels. Strengthening extension services by increasing the number of workers and providing them with adequate resources is crucial to promote PHT adoption among tomato farmers, ultimately leading to increased income.

Utilization of Artificial Neural Network for Assessment of Relationship Marketing Influence of Tomato (Solanum lycopersicum) Farmers

Utilization of Artificial Neural Network for Assessment of Relationship Marketing Influence of Tomato (Solanum lycopersicum) Farmers

ASSESSMENT OF THE CONTRIBUTION OF AGRICULTURAL EXTENSION SERVICE DELIVERY TO TOMATO FARMERS’ LIVELIHOOD IN KADUNA STATE, NIGERIA

This study aimed to assess the contribution of Agricultural extension service delivery to tomato farmers’ livelihood in Kaduna State, Nigeria. A multi-stage sampling procedure was employed in selecting 214 tomato farmers. Data were collected using questionnaire and analyzed Z-test estimation analysis. The findings revealed that there was a significant increase (Z-value = 9.336) in the yield of tomato production at 1% level of probability due to agricultural extension services delivery. Based on the income and level of living of the tomato farmers, the results show that income (Z-value = 2.643) and level of living (Z-value = 2.880) were significant at 1% probability level. It is concluded that there was an improvement in yield, income, and level of living of tomato farmers as a result of utilizing extension service activities in the study areas. The study therefore recommends that to build on the success of participating farmers in tomato production, ensuring the long-term sustainability of agricultural extension services is crucial, thereby solidifying the gains made and supporting continued progress in the sector.

An enhanced lightweight T-Net architecture based on convolutional neural network (CNN) for tomato plant leaf disease classification.

Tomatoes are a widely cultivated crop globally, and according to the Food and Agriculture Organization (FAO) statistics, tomatoes are the third after potatoes and sweet potatoes. Tomatoes are commonly used in kitchens worldwide. Despite their popularity, tomato crops face challenges from several diseases, which reduce their quality and quantity. Therefore, there is a significant problem with global agricultural productivity due to the development of diseases related to tomatoes. Fusarium wilt and bacterial blight are substantial challenges for tomato farming, affecting global economies and food security. Technological breakthroughs are necessary because existing disease detection methods are time-consuming and labor-intensive. We have proposed the T-Net model to find a rapid, accurate approach to tackle the challenge of automated detection of tomato disease. This novel deep learning model utilizes a unique combination of the layered architecture of convolutional neural networks (CNNs) and a transfer learning model based on VGG-16, Inception V3, and AlexNet to classify tomato leaf disease. Our suggested T-Net model outperforms earlier methods with an astounding 98.97% accuracy rate. We prove the effectiveness of our technique by extensive experimentation and comparison with current approaches. This study offers a dependable and understandable method for diagnosing tomato illnesses, marking a substantial development in agricultural technology. The proposed T-Net-based framework helps protect crops by providing farmers with practical knowledge for managing disease. The source code can be accessed from the given link.

ASSESSMENT OF SOCIO-ECONOMIC AND INSTITUTIONAL FACTORS INFLUENCING FARMERS’ UTILIZATION OF AGRICULTURAL EXTENSION SERVICE IN KADUNA STATE, NIGERIA

This study assessed the socioeconomic and institutional factors influencing farmers’ utilization of agricultural extension services in Kaduna State, Nigeria. A multi-stage sampling procedure was employed for the study. The first stage involved the purposive selection of two Local Government Areas (LGAs), i.e., Soba and Ikara, based on the threshold of tomato production in the study area. Secondly, a random sampling of four communities from the two LGAs. A total of 214 tomato farmers were used for the study which was calculated using Slovian’s formula. Descriptive statistics and Tobit regression analysis were used for the study. The findings showed that tomato farmers were young with an average of 38 years and relative experience of 15 years in tomato farming and had an average of 9 persons per household. The result of farmers’ utilization of extension service delivery shows that the extension delivery method, farmers’ indigenous knowledge and extension contacts were positive and a significant factor with a coefficient of 0.1346 at 1% probability. Farm size and income were positive and significant factors at 10% level in influencing farmers' utilization of agricultural extension services. The study recommends that structured training should focus on planting techniques, proper fertilizer application, agrochemical usage, mulching, weed control, and efficient harvesting. Therefore, efforts should be made by extension agents to organize field demonstrations and practical workshops that will equip farmers with hands-on skills to optimize yields and improve farm management practices.

Economic impact of planting dates and cultivars on tomato farming in Southern Telangana

Economic impact of planting dates and cultivars on tomato farming in Southern Telangana

Comparative Economics of Open and Protected Tomato Farming in Mid-Hill Zone of Himachal Pradesh

The study analyzed the comparative economics and profitability of tomato cultivation in the mid-hill zone of Himachal Pradesh under both open and protected conditions. The cost of cultivation for 100 m² was higher under protected conditions (?8,678.18) compared to open conditions (?1,358.87). Similarly, gross and net returns were significantly higher under protected cultivation (?22,686 and 14,007.82) than under open cultivation (?3,794.05 and 2,435.19). The break-even point for protected cultivation (?6,258.92) was also considerably higher than for open cultivation (?221.24). The margin of safety for protected cultivation was ?16,427.08 (27.59 per cent), compared to ?3,573.84 (5.82 per cent) for open cultivation. The study recommended promoting modern techniques and providing regular training for polyhouse farmers, given the capital-intensive nature of protected cultivation.

Skill‐Honey Badger Optimisation Algorithm‐Enabled Deep Convolutional Neural Network for Multiclass Leaf Disease Detection in Tomato Plant

ABSTRACTIn today's life, agriculture holds considerable importance in human life and the economy of a nation. Agriculture, including tomato farming, plays a vital role as one of the most extensively consumed vegetables worldwide. However, tomato crops are very prone to diseases, leading to reduced production and economic down in agricultural fields. To solve these issues, an effective method is proposed named Skill‐Honey Badger Optimisation Algorithm‐enabled deep convolutional neural network (CNN) (SHBOA_DeepCNN) for detecting leaf disease in tomato plants. In this method, the input is primarily preprocessed by utilising Savitzky–Golay (SG) filtering. Then, segmentation is performed by utilising Dense‐Res‐Inception Net (DRINet), which is trained by using devised SHBOA. The proposed SHBOA is designed by incorporating the Skill Optimisation Algorithm (SOA) and Honey Badger Algorithm (HBA). Subsequently, image augmentation is performed on segmented images by using two augmentation techniques, namely, colour augmentation and position augmentation. At last, multiclass leaf disease detection is performed using DeepCNN, which is trained by devised SHBOA. The experimental analysis of the devised SHBOA_DeepCNN method showed a high accuracy of 91.91% and a true positive rate (TPR) of 90.24%. Moreover, it achieved a minimum false positive rate (FPR) of 7.38%. The code of the article is available at “https://github.com/Amisra‐98/SHBOA_DeepCNN.git”.

Socio-Economic Factors Influencing the Adaptation Strategies of Tomato Producers’ to Climate Change in North West, Nigeria

This work evaluated socio-economic factors influencing the adaptability strategy of tomato producers to climate change in the North West, Nigeria. Primary data utilized were based on well-designed questionnaire distributed to 100 tomato producers. Data were analyzed using descriptive statistics, using an adaptability strategy use index, multinomial Logit model, and principal component model. The outcome shows that the average age of tomato producers approximates 44 years. They are literate with an average of 12 years of school education. About 51% of tomato producers had access to credit, and they are smallholder farmers with an average of 0.81 hectares of farm land. The major climate change adaptation strategies used by tomato producers and their corresponding indexes include the planting of heat and drought tolerant crops (0.9725), use of improved seeds (0.9682), mulching (0.8981), soil and water conservation technique (0.8321), and use of organic manure (0.7931). The significant predictors influencing the choice of low climate change adaptation strategy users include years of school education, age, experience in tomato farming, and income earned from tomato farming. The significant predictors influencing high climate change adaptation strategy users include years of school education, and income earned from tomato farming. The major challenges faced by tomato producers and their corresponding Eigen values include a lack of improved seeds (8.6935), lack of technology (5.93660), lack of processing and storage facilities (3.9274), and lack of capacity building for producers (3.5173). Improved seeds, irrigation facilities, and farm technologies should be made available to tomato producers to combat threats of climate variability and increase productivity.

Pendapatan dan Kelayakan Usahatani Tomat (Solanum lycopersicum L.) di Kecamatan Merdeka Kabupaten Karo

This research aims to determine the level of income and feasibility of tomato farming in Merdeka District, Karo Regency. Determining the research location was carried out purposively. The number of sample farmers was 34 people using the Slovin method. The research results show that on average farmers manage 0.46 Ha of farming land. The largest component of production costs incurred by farmers in tomato farming in Merdeka District is pesticide costs amounting to IDR 13,302,852.92 or 31.76% of the total production costs. Meanwhile, the smallest cost component is the rope cost of IDR 600,000 or 1.43%. The average income of tomato farmers per planting season is IDR. 58,008,029.4 or Rp. 9,668,005 per month. Feasibility of farming with the R/C Ratio value per farmer in tomato farming is 2.385 and the B/C Ratio value is 1.385 which shows that the farming is economically feasible to operate. Keywords: farming, tomatoes, costs, income, feasibility

Implementation of hybrid tomatoes as an effort to increase tomato harvest production

Tomatoes are a horticultural commodity that is widely cultivated, especially in Bandung Regency. Tomato plants are widely cultivated because they have a fairly high economic value and are in demand by the community because of their many health benefits, among them are vitamin and antioxidant content. Currently, tomato production is declining, so education and assistance are needed to increase the potential for results. The target of this activity is tomato farmers in four sub-districts in Bandung Regency. The purpose of this activity is to increase farmers' knowledge in cultivating hybrid tomatoes at various altitudes. The activity started with counseling, field practice, and evaluation of the results of the activity. As a result of this activity, farmers have additional knowledge in appropriate technology for tomato plants in their respective locations. After the evaluation process, tomato production increased significantly, both in terms of quantity and quality, so it had a positive impact on the economic welfare of farmers.

Cherry Tomato Detection for Harvesting Using Multimodal Perception and an Improved YOLOv7-Tiny Neural Network

Robotic fruit harvesting has great potential to revolutionize agriculture, but detecting cherry tomatoes in farming environments still faces challenges in accuracy and efficiency. To overcome the shortcomings of existing cherry tomato detection methods for harvesting, this study introduces a deep-learning-based cherry tomato detection scheme for robotic harvesting in greenhouses using multimodal RGB-D perception and an improved YOLOv7-tiny Cherry Tomato Detection (YOLOv7-tiny-CTD) network, which has been modified from the original YOLOv7-tiny by eliminating the “Objectness” output layer, introducing a new “Classness” method for the prediction box, and incorporating a new hybrid non-maximum suppression. Acquired RGB-D images undergo preprocessing such as color space transformation, point cloud normal vector angle computation, and multimodal regions of interest segmentation before being fed into the YOLOv7-tiny-CTD. The proposed method was tested using an AGV-based robot in a greenhouse cherry tomato farming facility. The results indicate that the multimodal perception and deep learning method improves detection precision and accuracy over existing methods while running in real time, and the robot achieved over 80% successful picking rates in two-trial mode in the greenhouse farm, showing promising potential for practical harvesting applications.

Hydroxycinnamic Acid Oligomers-Based Biostimulant Nurspray® Enhances Tomato Yield, Fruit Quality, and Reduces Blossom-End Rot

Tomato production faces challenges such as high input costs, pests, diseases, and climate change. Biostimulants like Nurspray®, based on hydroxycinnamic acid oligomers, offer a promising solution. This study evaluated the impact of different application timings of Nurspray® on tomato yield and fruit quality under open-field conditions. Four treatments were tested: T1 (control), T2 (Nurspray® at BBCH 19–51), T3 (Nurspray® at BBCH 19–51–61), and T4 (Nurspray® at BBCH 19–23–51). The results showed significant improvements in root development, canopy cover, and flowering for T3 and T4, with root systems increasing by up to 103% in T4 compared to the control. Fruit yield and quality were enhanced, with a reduction in blossom-end rot (BER) incidence to 0% in T3 and T4, compared to 5% in the control. Additionally, T4 produced the highest dry matter content in fruits. The optimal application strategy was three doses of Nurspray® at BBCH 19–23–51, enhancing both yield and fruit quality. This study highlights the potential of Nurspray® as an organic biostimulant in sustainable tomato farming.

RISIKO USAHATANI TOMAT (LYCOPERSICUM ESCULENTUMMILL) DI KECAMATAN LEMBAH GUMANTI KABUPATEN SOLOK (Risks of Tomato (Lycopersicum Esculentum) Farming in Lembah Gumanti District Solok Regency)

This study aims to determine analyze the characteristics of tomato farmers, analyze the income of tomato farmers and analyze the risk level of tomato farming in Lembah Gumanti District, Solok Regency. The method used in this study is quantitative descriptive The analysis used is income, variety, standard deviation, coefficient of variation, and lower limit. The respondents who were sampled were 40 farmers with a population of 423 farmers and were selected randomly (proportional random sampling). The results showed that the characteristics of tomato farmers have a productive age of 25 to 48 years, a low level of education, farmers who already have long farming experience. The amount of cash costs incurred by farmers is IDR 6,456,733.33 / MT and the calculated costs are IDR. 4,919,309.63/MT with revenue of Rp 23,608,331.25/MT so that cash income is Rp 17,209,386.82/MT and total income is Rp 12,290,077.19/MT. Tomato farming has a production risk level with a KV value of 10.39, price risk with a KV value of 18.56, and income risk with a KV value of 13.36 with a lower income limit of Rp 10,450,763.47 where this figure will indicate the amount of loss that will be experienced by tomato farmer

FACTORS INFLUENCING ADOPTION OF IMPROVED TOMATO SEED VARIETIES AMONG SMALL-SCALE TOMATO FARMERS IN SOUTH WEST ZONE OF NIGERIA

The study examines factors influencing adoption of improve tomato varieties among small-scale farmers in Southwest Nigeria. Data were collected via structured questionnaires and analyzed using descriptive statistics, probit regression, and Likert scales. The findings revealed that 41.4% of the farmers were in their productive years with an average age of 45 years, predominantly male (70.0%), married (81.9%), with small households averaging five members. However, 87.5% had formal education and average of eight years of tomato farming experience. The farmers cultivate an average of 0.63 hectares, indicating small-scale operations. Furthermore, 85.6% of the farmers were not part of cooperatives. The result also shows that 80.0% and 65.0% of the farmers lack access to credit and extension services in the study area. Logic regression model reveal 68.39% probability of adoption of improve tomato seed varieties among farmers. Key factors significantly influencing adoption include age, gender, farming experience, education, access to extension services, market distance, input supply, and specific training. Major challenges include high input cost (73.8%), pests and diseases (55.6%), lack of credit (61.9%), marketing issues (28.8%) and unfavorable weather (18.1%). Therefore, recommendations from the study include strengthening extension services, raising awareness of cooperative membership benefits, and addressing farmers' priorities and needs.

Sustainable energy management for indoor farming in hot desert climates

Achieving food self-sufficiency in hot desert climates requires year-round farming, which is challenging due to extreme weather, water scarcity, and limited arable land. Indoor soil-less farming can mitigate these issues by reducing land and water use but increases operational complexity and electricity needs for cooling, impacting economic sustainability. This paper presents a resource management system using Artificial Intelligence of Things (AIoT) to simplify operations and optimize resources, alongside techno-economic analysis for economic viability. A case study on hydroponic tomato farming in hot deserts demonstrates that beyond a crop yield threshold (24.022 kg/m2), significantly more energy is required for marginal yield increases (e.g., 18% more electricity for a 0.35% yield increase). Despite higher energy use, the techno-economic analysis shows a net present value increase even with unsubsidized electricity. Thus, optimizing energy alongside water and nutrients is crucial for economic sustainability in indoor farming.

Improved Tomato Breeding Lines Adapted to Organic Farming Systems Have Enhanced Flavor, Yield, and Disease Resistance

Most of the tomato (Solanum lycopersicum) varieties currently used in organic farming were bred for conventional farming, often characterized by high-input use. These varieties do not perform as well in low-input organic systems, generating the need to develop varieties that are adapted to organic management systems. This project focused on improving flavor, disease resistance, and yield, all identified as key traits by organic tomato farmers in the Upper Midwest, USA. Ten advanced tomato breeding lines and two check varieties were developed and evaluated for 16 traits in organic high tunnel systems in 2020 and 2021. The line CSDE-F6.47 averaged 6.32 kg/plant and obtained high flavor intensity and overall flavor scores (3.78 and 3.69 out of 5, respectively). The line JBDE-F5.31 was another outstanding line, with a yield of 5.18 kg/plant, with good flavor intensity (3.32) and overall flavor (2.92) scores. Broad-sense heritability of marketable weight per plant was high (0.91), and the genetic variance was also high, which shows the opportunity to continue to increase the marketable weight in lines with excellent flavor. A significant positive correlation was found between overall flavor and °Brix (0.56), and titratable acidity (0.70), indicating that both measurements can be good predictors of overall flavor. The most promising lines will be further evaluated on-farm to evaluate their potential as releasable varieties.

Intercropping of African Marigold with Tomato for Better Productivity, Pest Control and Economic Returns in Arunachal Pradesh, India

The present study highlights about intercropping of African marigold with tomato for better productivity, pest control and economic returns in Arunachal Pradesh, India. This study was carried out during Rabi season of 2017-18 and 2018-19 respectively in Tirap district of Arunachal Pradesh to find out the best line combination of marigold with tomato. Total three treatments were planted with three replications under Randomized block design. T1 recorded minimum infestation of tomato fruit borer (5.47 %), maximum fruit yield (16.32 t/ha), maximum net return (Rs. 1,53,000), Best B:C ratio (3:1) while T3/control recorded least (18.32%, 14.27 t/ha, rs.1,17,780 and 2:43 respectively). It can be suggested to farmers of Arunachal Pradesh that marigold can be used as a trap crop in tomato farming for better production, yield and economics.