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This investigation puts forward a novel intelligent farming platform that merges Internet of Things (IoT) capabilities with predictive modeling and self-regulating command systems to streamline watering processes and amplify resource productivity. The suggested design employs soil wetness, thermal, and atmospheric moisture probes interfaced with an Arduino-centered microcontroller unit, allowing irrigation to be triggered autonomously according to live sensor readings. Agricultural operators can remotely oversee and adjust watering cycles through portable devices, delivering both exactness and operational simplicity. The predictive model fuses historical data with current measurements to anticipate future irrigation demands, guaranteeing moisture delivery exclusively when truly required. Empirical on-farm assessments indicated a 30 % decline in water expenditure versus standard irrigation techniques, whilst upholding ideal earth moisture parameters. In addition, the framework markedly diminished workforce and power expenses, bolstering overall functional capability. By confronting the issue of hydrological shortage and advancing conscientious resource stewardship, this intelligent platform possesses the capability to enhance harvest volumes, curtail greenhouse gas discharges, and further eco-conscious cultivation methodologies. Within water-limited geographies, deploying this technological solution constitutes a pivotal stride towards enduring agricultural viability, indicating considerable scope for extensive implementation.

The push toward precision agriculture is increasingly driven by environmental considerations alongside economic pressures, fueling progress in variable-rate fertilization (VRF) technology. Although centrifugal fertilizer spreaders offer benefits such as mechanical simplicity, low cost, and high throughput, non-homogeneous spread patterns are becoming a primary obstacle to their broader use. An urgent investigation is therefore required into how particles behave and what variability patterns emerge when centrifugal VRF spreaders operate under multi-pass overlapped application, to boost both evenness of coverage and field accuracy. This paper examined and assessed the operational characteristics of a custom-built centrifugal VRF spreader that uses real-time crop status data for rice and wheat, applying the collection tray protocols detailed in ISO 5690 and ASAE S341.2 (1999). Distribution uniformity was evaluated by calculating the coefficient of variation (CV) from weighed fertilizer captured in standardized trays, thereby gauging the evenness of the applied spread profile. The obtained data indicated effective swath widths of 21.05, 22.58, and 23.67 m corresponding to target application dosages of 225, 300, and 375 kg/ha, respectively. In practice, multi-pass, overlapped spreading tended to deliver application amounts exceeding the intended rates, with within-swath CVs of 11.51, 9.25, and 11.28% for each respective dosage level. Field-based assessments of multi-pass, overlapped operations recorded a mean discrepancy of 4.54% between delivered and target rates, along with a mean lateral distribution CV of 11.94% across the implement’s working span, results that align with the performance criteria for transverse particle placement using centrifugal broadcasters. The insights generated by this work provide a conceptual reference point for the design refinement and technological evolution of centrifugal VRF equipment, carrying considerable applied and societal value in hastening their integration into everyday precision farming practice.

Fungi represent a highly nutritious dietary staple that can be essential for supplying cost-effective protein, vitamins, and minerals to populations around the globe; however, their successful cultivation requires extensive training and specialized knowledge to carefully calibrate a range of environmental variables. Internally displaced populations located in Northern Syria depend on extremely limited traditional oyster mushroom farming, which falls short of meeting local consumption requirements. Numerous international and local non-governmental organizations (NGOs) supporting Syrian refugees run mushroom-farming programs. They have encountered considerable difficulties and obstacles in mushroom farming among the intended participants. Thus, the two core questions steering this investigation are: (1) How might organic mushroom farming be advanced via a reliable and low-cost intelligent cultivation system? (2) In what ways can organic mushroom farming methods be streamlined to assist internally displaced persons and Syrian refugees? This investigation examines the mechanization of mushroom farming by designing and implementing an intelligent oyster (Pleurotus ostreatus) cultivation system for remote surveillance and adjustment of environmental conditions—temperature, humidity, air quality, and light—within the growing space. Additionally, ready-made and purpose-built, intuitive web interfaces were created to enable growers to survey and operate their farms via the Internet remotely. Consequently, a trustworthy and economically viable intelligent oyster mushroom cultivation system was designed and successfully implemented in this study. The system encompasses remote surveillance and operation via intuitive interfaces. This makes mushroom farming more straightforward not only for displaced populations and refugees but also for mushroom growers in nations with limited financial resources. This research can aid in the elimination of poverty and hunger, in accordance with the United Nations Sustainable Development Goals one and two.

Artificial intelligence is increasingly positioned as a transformative force for climate-smart agriculture, promising simultaneous gains in productivity, efficiency, and sustainability. Across agricultural policy, innovation systems, and digital farming markets, AI is presented as a solution to climate uncertainty, resource scarcity, labour pressures, and food-security demands. This review examines that promise through a critical lens. The dominant narrative surrounding AI-enabled climate-smart agriculture often treats optimization as a neutral technical process. Yet algorithmic systems do not merely improve farming decisions; they also define what counts as efficiency, which outcomes are prioritised, and whose knowledge becomes authoritative. This raises important questions about power, agency, and sustainability. The objective of this critical review is to interrogate the politics of AI in climate-smart agriculture. It synthesises evidence on the tensions between productivity, efficiency, sustainability, farmer agency, data sovereignty, and governance. The review asks how algorithmic optimization reshapes agricultural futures and who benefits from this transformation. The review draws on  peer-reviewed articles published across agricultural innovation, sustainability science, digital agriculture, artificial intelligence, and critical data studies. Rather than reporting new empirical data, it develops a critical synthesis of existing debates and identifies conceptual gaps in current scholarship. The review finds that algorithmic farming is not simply a technical evolution of precision agriculture. It is a contested political terrain in which sustainability goals, commercial interests, data infrastructures, and farmer autonomy are negotiated. A critical research agenda is needed to ensure that AI contributes to equitable, democratic, and ecologically resilient agricultural futures.