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Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

The coffee farming sector in Gunungmanik Village, Indonesia, plays a significant role in the local economy. However, the monitoring and management of coffee crops remain largely manual and conventional, making it difficult for farmers to respond quickly to environmental threats such as drought, pests, or sudden temperature shifts. This research presents the development of iotgm.id, a web-based monitoring system integrated with Internet of Things (IoT) devices designed to provide real-time environmental data for coffee plantations. The system measures key parameters including temperature, soil moisture, and motion detection (as a proxy for pest activity), and delivers this data via a user-friendly web interface. It also features digital farm record management, real-time alerts for abnormal conditions, and data visualization through interactive dashboards. Field testing with local farmers showed that the system improves decision-making, speeds up responses to environmental changes, and reduces the need for direct field visits. Unlike earlier systems that often required technical expertise or focused on single parameters, this system offers multi-parameter monitoring and is accessible to farmers without advanced digital literacy. The system bridges the gap between sophisticated agricultural technologies and practical field-level application. It contributes to the adoption of precision agriculture in rural areas, offering a scalable model for broader implementation in similar contexts

The food security program involving chili cultivation in Pentur Village is hindered by inefficiencies in water use and suboptimal plant growth, primarily due to traditional irrigation methods that fail to consistently maintain ideal soil moisture and temperature. This issue is exacerbated by unpredictable environmental shifts, such as fluctuating weather patterns, and a lack of precise irrigation control stemming from technological limitations. To address this, a system for monitoring and regulating chili plant irrigation using IoT technology was developed. This system employs humidity and temperature sensors connected to an IoT platform like Blynk, enabling real-time observation of plant and environmental conditions. Data on soil moisture, air temperature, and humidity are stored in a database, and irrigation is automated based on soil moisture levels. The goal is to enhance water efficiency, minimize risks associated with over or under-watering due to environmental variations, and improve both yield and quality of the chili crop. This IoT-based system aims to simplify chili plant management for Pentur Village farmers and significantly boost agricultural output.

Hydroponic plant cultivation is booming, but stock and sales are hard to predict. Poor prediction can cause farmers to overstock and lose money. This study suggests a framework that uses several machine learning models, including Linear Regression (LR), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting. "Ensemble Learning," which combines these models, should yield more accurate and generalizable results than a single model. This framework is assessed using historical hydroponic plant sales data and related factors like price, weather, and market trends. The model's performance is measured by the difference between predictions and actual values using RMSE and MAE metrics. This framework should improve hydroponic plant stock and sales predictions. Farmers can make better production, inventory, and harvest distribution decisions. Besides reducing financial losses, this reduces food waste and improves food security.

Milk consumption and the need for beef cattle for food purposes or sacrificial purposes continue to increase from year to year. This increase is in line with the increasing level of the economy and awareness of the need for nutritious food. However, the increase in demand still faces many production constraints due to the difficulties faced by farmers, the decline in cattle production from year to year in Indonesia. This causes Indonesia to have to import beef for 35.95% of the total national beef consumption. With many factors that affect the decline in the production rate of dairy cows or beef cattle every year, one of them is air pollution and poor air quality in cages. Along with the development of technology, a monitoring and detection system for air cleanliness in cattle pens was created, using the MQ-7 sensor as the concentration of carbon monoxide, the MQ-135 sensor as the concentration of ammonia gas, the MQ-2 sensor as the smoke detection, and When the air pollution value of each sensor input is according to the air pollution standard index (ISPU), the outputs namely indicator lights, buzzers and blowers will be on, then there will be temporary air neutralization in the cage. This tool is made easier by internet of things technology, so that it can send notifications via telegram and can see the level of air pollution levels through the web.

Most dairy farmers in our country still rely on human labor to monitor the condition of their farm areas. The current problem is the manual security system for barns, meaning that the barns need to be constantly checked around the area at all times. This is especially risky at night, when livestock theft is more likely to occur. This research implements PIR sensor technology to monitor movements around the barn, MC-38 magnetic sensors for gate security, and utilizes Android smartphones as devices to display barn data processing results, allowing real-time monitoring of barn security. The research is conducted using the Research and Development (R&D) method by Borg & Gall, encompassing only six steps. The results of the system evaluation by experts show a score of 3, falling between 2.1 – 3.0, categorized as Valid. User validation results show a score of 27.2, also falling between 2.1 – 3.0, categorized as Effective.

Abstract -  Corn plants are one of the plants that are easy to grow and relatively fast to get results but also a plant that is vulnerable to pests and diseases. The symptoms that arise require an expert or agricultural expert to be able to diagnose. Limited time and expert labor is one of the causes of delayed diagnosis, causing a decrease in farmers' yields. This is the basis for making this expert system by adopting knowledge from agricultural experts about the symptoms of corn disease using the Forward Chaining method. To support the research conducted, researchers used the Research and Development (R&D) research method and the design used the Object Oriented Programming (OOP) method using the Unified Modeling Language (UML). Software is built using the PHP programming language, MySQL as a database, and Bootstrap as an interface design with a web-based system.    

Rice plants (Oryza sativa L.) are rice-producing plants which are a source of carbohydrates for most of the world's population. Almost 95% of Indonesia's population consumes rice as a staple food, so every year the demand for rice increases as the population increases. Therefore, farmers must choose quality seeds. In this era of fast and efficient technological progress, this is a very good thing for all progress in various fields. more and more fields of knowledge are developing, one of which is the existence of a decision-making system. a set of model-based procedures for processing and valuing data to help managers make decisions. This decision-making system uses several variables as input consisting of: type of variety, seed shape, seed color, root, seed age. The method used by the author is Fuzzy Tsukamoto. In the Tsukamoto method, it is explained that each consequence in IF-Then must be explained with a fuzzy set that has a membership function that does not change or is monotonous and for programming it uses PHP. The results obtained from the research that the authors conducted were in the form of a decision-making system to get the best seed yields.

The combination of activities carried out by humans and technology is related to administrative systems that create information from organized work processes. PT. Farmhouse Production in the administration of data management is carried out with the help of Microsoft Excel and documents are written, and copied in the traditional way which results in the accumulation of accumulated paper resulting in complexity and difficulty in finding the required documents. Therefore, a balance of human resources, database, hardware, software, and a new strategy is needed to overcome the problems that can burden the company by realizing a website that is easy to apply so that any file is found quickly by paying attention to supervision, control, and methodical organization using PIESCES analysis as a method used to detect deficiencies and weakness in the old system. With this analysis, the new system is the right support in managing data that is collected in an orderly manner and stored neatly in large storage without being randomized and the work is more strategic because the information generated is valuable to the recipient or leader.

In the current era of globalization, the use of computer technology has been widely used, including in the operations of non-formal organizations such as farmer women's groups (KWT), one of which is in administration, activities, schedules, sales. agenda, and reporting is still manual, namely with bookkeeping records and does not yet have a system for this. Given these problems, the right solution is to create a web-based information system for women farmer groups (kwt). In analyzing and designing, the author uses a software development method that uses the V-Model method, which is an extension of the waterfall model. It is called expansion because the stages are similar to those in the waterfall model. If in the waterfall model the process is carried out linearly, then in the V model the process is branched. In this V model, the relationship between the software development stage and the testing phase is described. The purpose of this research is to produce an information system that can make it easier for the kwt to do administration. activity. timetable. sale. agenda. and reporting without recording with the books.

Every sale of goods, sales report the number of goods sold to the warehouse admin. Because of the many types of goods being sold, sometimes the warehouse admin forgets about which items are almost out of stock and should be ordered. The aim of this research is to produce a goods ordering system with the EOQ and ROP control methods to overcome the occurrence of stockouts. The author uses the research object of employees who work at PT Kimia Farma Plant, namely sales, warehouse department, admin and HRD. With this system, the warehouse admin can see the number of items in real time. The EOQ method generates a report on the number of economic purchases (EOQ), the total costs that must be incurred by the company to reorder an item, the duration of the EOQ, up to the reorder point (ROP).

Learning Media applications that use an Android-based material delivery system are expected to facilitate the delivery of material, where previously material was only found in books and the internet. Learning materials that only come from books reduce the reader's understanding because there are only text and pictures. This is what underlies the selection of the thesis title "Application of Android-Based Hydroponic Vegetable Growing Learning Media Application" in the hope that it can be a solution to help people who take part in the training understand material about growing vegetables by hydroponics.Hydroponics is an agricultural activity carried out by using water as a medium to replace soil. So, hydroponics can be interpreted as a work or management of water as a medium for plant growth without using soil media as a planting medium and taking the required mineral nutrients from a nutrient solution dissolved in water. The learning media application for growing hydroponic vegetables based on Android is limited to material on hydroponics, planting media and tools used in hydroponics, the steps needed for hydroponic farming, systems or techniques that exist in hydroponics.

Agriculture is an activity of managing biological natural resources with the help of technology, capital, labor and management to produce agricultural commodities which include food crops, horticulture, plantations and or livestock in an agro-ecosystem. Most of the Indonesian people's livelihoods are as farmers, so the agricultural sector is very important to develop in this country. Poktan Beno Raharjo in Glonggong Village, Madiun Regency has a problem, namely the absence of an information system that is useful for managing agricultural production data, managing farmer data, managing village granary data, recording planting of food crops and horticultural crops, managing subsidized fertilizer assistance. The mobile-based Poktan information system was created using the dart programming language with the flutter framework using the waterfall method and testing using the black box testing method.

By looking at current technological developments that already use automatic sensors, the application of the concept to this system can be a solution for dealing with ornamental fish farming owners. In this research, a tool is made that can perform feeding, checking temperature, water turbidity, feed level, water level and water pump control, from this information will be sent to android by firebase automatically monitored and controlled anywhere via android. This tool is designed with ease of maintenance and is an extension of the existing tools. IoT technology is used to monitor online, based on Firebase and Nodemcu Esp8266. In this study, researchers used the research method of the Borg and Gall (1987) model from 10 steps to 6 steps. The results of this study were carried out by an expert validation test to get a value of 30 which means "Very Good (Valid)". While the effectiveness test by 10 users plus by experts and employee users get a total score of 35.16 which means the effectiveness test is classified as "Very Good (Valid)" it can be said that the new system is more effective than the old system.

Farmers often have difficulty in choosing the pesticides to use. Where pesticide products are very widely circulated in the market and offer various advantages of each product, but farmers often experience incompatibility with what has been offered by each product. The inappropriate use of pesticides on shallots used by farmers can affect the yield of shallots. The WASPAS assessment criteria in the selection of pesticides on shallots consisted of price criteria weighing 40%, size criteria weighing 10%, area criteria weighing 30% and expiration criteria weighing 20%. The results of the recommendation for the selection of pesticides on shallots were obtained from the highest Q_i value. Of all the pesticides on shallot plants above, the highest Q_i value is Agrithane with a Q_i value of 0.897. Agrithane has the highest value because it has the lowest price where the price criterion has the highest percentage weight.

Irrigation is a land irrigation system by damming water sources. Irrigation as the provision, regulation and disposal of water to support agricultural needs. Irrigation is generally used by farmers, especially farmers in rice fields to irrigate and provide water supply to agricultural land, monitoring water stock in rice fields must be done frequently because rice water needs must be balanced with a lack of water supply or excess water supply in rice fields is not good for rice growth . So far, farmers have monitored irrigation channels manually, the location of the rice fields from the farmer's house often causes rice plants to experience scarcity because the floodgates are not opened when the water supply is running low or experiencing water supply. excess because the floodgates are opened for too long, this will greatly disturb the growth of rice plants. Making a monitoring and control system for irrigation channels using a water level sensor installed on the ESP 8266 microcontroller (Wemos D1), the system uses a programming language c. This tool can be used to open the irrigation door if the water supply in the rice fields is below the minimum limit and will close the water gate when the water supply has reached the maximum limit, the servo motor is used to open and close the floodgates. . . The monitoring results obtained will be displayed to users in real time through the android application interface and will be stored in the form of a text file on the storage media.

Technological developments encourage changes in the way decisions are made. In terms of stopping the application of urea fertilizer, farmers still rely on BWD observations. In this study, using the RnD method by directly observing the research location in the farmer group Gapoktan, in the village of Tosari, Kendan district and analyzing the problem then making a prototype which will be tested by experts. In this study, a tool was made using NodeMCU with TCS3200 sensor to detect color, which was then sent via the internet to the database using IoT technology. In this study, using several trials with different parameters of urea fertilizer use and the results of the TCS3200 sensor can detect colors according to what is on the BWD and send data to the database via the internet. In this system, farmers can recommend the use of suitable urea fertilizers outside a certain area so as to minimize errors in the observation on BWD, this will make the use of urea fertilizer to be the right dose.  

Recent technological developments determine humans to be creative in creating tools that implement technology in it with the aim of facilitating human tasks in daily activities. Inappropriate use of fertilizers can cause losses, both losses on fertilizer, on plants, as well as on the soil and the environment around fertilization. Losses on plants such as unhealthy plant growth and susceptible to disease pests, plant yields are not as expected or low. From the background above, the writer found an idea to create a system or tool that can help farmers in determining the dose of Nitrogen fertilizer in their rice plants, through the storage of the dosage data of each land. The user did five scans, from each scan producing RGB values, each RGB value from five samples was then matched with the BWD dose color indicator. The sample dose results are then sent by the NodeMcu microcontroller to the webserver database. Based on the assessment of the test conducted by 10 Users by Gapoktan Farmers Group of Tosari Kendal Village, the total value of 372 that has been averaged to be 37.2 is between 31 - 40 which is classified in the category of "Very Valid or Very Effective".

Production of freshwater fish farming (ponds) in Kangkung Subdistrict in 2015 reached 4.42 tons or valued at 97.19 million rupiah and the condition is growing more and more. The number of fish seeds is measured conventionally by observing the senses of the eye and causing inaccurate (inaccurate) measurement results. This study offers a solution for counting fish by using a microcontroller based phototransistor technology. Phototransistor has a linear function of the received light intensity that is flowing current (ON) and if there is a condition of light intensity blocked by fish passing through it then the current does not flow (OFF). The trial results of the fish seed count tool have an accuracy of 97% so that it can be used by fish farmers.  

Number of fish seed can be measured by means of conventional observation using eye senses. But when the fish seed measured pretty much it will take a long time (not efficient) and cause incorrect measurement results (inaccurate). The accuracy of calculation of fingerlings is crucial trust between seller and buyer.During this time a sensor is used to detect the amount of fish seed is passed through a transparent hose using a light sensor (Light Dependent Resistor) and photodiode sensors as infra red light receiver. The level of accuracy of the sensor lies in the intensity of the light beam is received so that the sensor will be impaired if airya murky and there are air bubbles pass through the sensor so that the fish count error occurs.This research will be conducted design counters the number of fingerlings using infra red phototransistor sensor with a frequency of 20KHz to fish and then processed using the logic matrix. Results matrix logic into the data input to be processed by a microcontroller as the basis of results of calculation of the amount of fish.Development model studies using models of R & D (research and development) that produce a product in the form of the final product. Experiments were carried out using the type and length of catfish fingerlings with a length of 3 cm to 5 cm. Fish seed count tool has been tested to catfish farmers in the village Siwarak Ungaran City.The trial results calculators fish seeds have difference of 3% of the calculated results Manual or accuracy of calculation results have fish reaches 97%, then a counter of fish seed is acceptable to be used by catfish farmers.