Internet and industrial revolution 4.0 brought influence in society, especially in agriculture, the changes that occurred were in farming activities by applying appropriate technology. This activity is much in demand, especially the planting irrigation process. However, the process of irrigation or irrigation that has used appropriate technology still has some shortcomings such as pumping and control hardware systems that are still stand-alone using a timer, so the automation and monitoring process is still semi-manual. As done in the cultivation of janggelan (black grass jelly), which focuses more on irrigation governance, because the janggelan requires water intake and fertilizer nutrition which is always monitored. In order for the device to function optimally, it is made a miniature tool based on the internet of things to facilitate control and monitoring via a smartphone. In order for the miniature tool can be integrated and function in real-time with the Internet of things, then all devices (YL 69 sensors, Relay, Board NodeMCU esp 8266 are given C language coding commands, via the Arduino ID interface. synchronization between the user interface, the miniature devices, and Internet networks running normally. IoT devices and internet networks can run normally. Sensor data is able to give the pump selenoid command to turn on under humidity conditions of less than 65%, while the pump is off at 40% humidity. The success rate of data sent on Google Firebase with an average total ping of 70.28% was all successful. .

Cabai, sawi, tomat, selalu jadi tanaman favorit petani, walaupun butuh banyak air dan pekerja. Adaptasi kondisi itu dengan teknik bercocok tanam smart agriculture system (SAS) yang melibatkan teknologi seperti irigasi otomatis yang mengatur penyiraman hanya berdasarkan rutinitas tanpa memperhatikan kondisi lahan. Pengendalian seperti ini saat musim pancaroba bisa mengakibatkan kebusukan akar serta memicu penyakit fungisarium pada tanaman cabai. Solusinya, coba dihadirkan sistem yang melibatkan kecerdasan buatan seperti logika fuzzy berbentuk sebuah embedded system  dengan pantauan internet of things (IoT). Logika fuzzy secara komputasi matematis akan mengatur irigasi berdasarkan kondisi kelembapan dan suhu lahan. Dimulai dengan tahap Fuzzyfikasi untuk memetakan input nilai suhu dan kelembapan dari sensor. Dilanjut dengan pembuatan Inference engine di mikrokontroler NodeMcu 8266 untuk mengartikan pernyataan rule fuzzy berbentuk agregasi kondisi minimum dengan operator AND, kemudian dikombinasikan dengan nilai set tunggal 0 dan 1 pada fuzzy sistem menjadi respon aktuator yang sesuai. Setelah keseluruhan sistem dijadikan purwarupa, maka dilakukan testing pengujian untuk mengetahui seberapa baik kode program fuzzy dapat merespon perubahan ekosistem lahan budidaya agriculture yang disimulasikan. Penelitian ini mendapati hasil bahwa kode program loggika fuzzy yang ditanamkan pada mikrokontroler nodeMCU8266 berhasil mengatur durasi penyemprotan yang dilakukan pompa sebagai respon berbagai simulasi kondisi lingkungan dalam waktu 3,6 detik.

Aglonema or sri fortune has various types with various shapes, patterns and colors. Various types and more and more due to the many crossing processes carried out by owners and lovers of aglonema plants. For ordinary people who do not have insight into aglonema, it will be difficult to distinguish aglonema plants because the shapes, patterns and colors have similarities. It takes a Teachable Machine system with a complex but more sophisticated method that is able to recognize plants with a higher level of accuracy. The machine learning process is carried out on a computer to identify image data into classification results in the form of predictions. Tensorflow lite is a machine learning library specially designed for object recognition. Therefore, researchers are encouraged to create an Android-based mobile application that is able to recognize aglonema plants quickly, easily and accurately.

The ideal conditions for the oyster mushrooms growth are at a humidity of 65-75% and 29-31C during incubation, while the growth of stems should be at a humidity of 70-90% 29-32C. This ideal ecosystem is maintained by aeration and manual watering. Still, the results are not optimal in preventing damage to the mycelium during the incubation period, resulting in a decrease in crop yields. Automatic control has not created ideal conditions because air temperature and humidity regulation are only based on fans and sprayers that do not directly affect air conditions. Therefore, we need a method to manipulate the mushroom greenhouse space ecosystem, namely fuzzy logic, the application of fuzzy logic integrated with sensors, actuators, and microcontrollers with the Internet of Things to solve this problem. The results of the installation of fuzzy logic in the mushroom's greenhouse in this system can be seen from the fan's modulation response and the pump's duration. The test results of this control feature can manipulate temperature and humidity. Therefore, the oyster mushroom greenhouse produces an ideal state of 29.8C, the humidity of 68.97% RH, and the production has been proven to be optimal with an average daily harvest of 3.8kg.

Chili, mustard greens, and tomatoes have always been farmers' favored crops, despite their high water and labor demands. Adapt to these conditions by utilizing smart agriculture systems (SAS) agricultural techniques that involve technology such as automatic irrigation that regulates watering based solely on routine, regardless of land conditions. This type of control during the transitional season can lead to root rot and fungisarium disease on chile plants. In the form of an embedded system with internet of things (IoT) monitoring, a system incorporating artificial intelligence such as fuzzy logic is proposed as a solution. Fuzzy logic will regulate irrigation based on the land's humidity and temperature using computational mathematics. Beginning with the fuzzyification stage to map the sensor's temperature and humidity input values, fuzzy logic is applied. The creation of an inference engine in the NodeMcu 8266 microcontroller to interpret fuzzy rule statements in the form of aggregation of minimum conditions with the AND operator, followed by the combination of a single set value of 0 and 1 in the fuzzy system to produce an appropriate actuator response After the entire system has been prototyped, testing is conducted to determine the responsiveness of the fuzzy program code to changes in the simulated agricultural cultivation land ecosystem. This study found that the fuzzy logic program code embedded in the nodeMCU8266 microcontroller effectively controls the spraying duration of the pump in response to various simulated environmental conditions within 3.6 seconds.