Insecticide applications are typically being carried out with traditional manual spraying equipment in redgram, which leads to inadequate control of insects due to higher crop height. The modern deployment of tractor-drawn spray machines causes serious damage to the crop. In this connection, unmanned aerial vehicle (UAV) spray technology has great potential for precise insecticide application in redgram crops. One of the important machine parameters influencing droplet deposition and drift characteristics in UAV sprayers is downwash airflow generated by a multi-rotor propeller. A field experiment was carried out at the redgram research field (N11.01, E76.92), Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, during 2021–2022 to study the spray drift and deposition characteristics of an autonomous UAV sprayer. The Imidacloprid (a.i. 17.8SL) insecticide mixed with water in a ratio of 1 mL per liter was sprayed with a UAV sprayer. Water-sensitive paper samples were kept at upper, middle, and bottom positions on the leaves, and data were analyzed for the spray droplet size, deposition rate, droplet density, and area coverage both in target and non-target areas using Spray Deposit Scanner software. UAV spray droplet deposition rate (2.93 ± 0.17, 2.01 ± 0.08, and 2.21 ± 0.162.38 μL cm⁻²), droplet density (47 ± 4.04, 53 ± 3.61, and 52 ± 8.74 droplets cm⁻²), and area coverage (15.72 ± 0.39, 16.60 ± 0.71, and 14.99 ± 0.39%) were highest in the upper layer as compared to the middle layer (droplet deposition rate: 1.21 ± 0.08, 1.07 ± 0.03, and 0.77 ± 0.02 μL cm⁻²; droplet density: 42 ± 2.52, 43 ± 8.50, and 38 ± 2.52 droplets cm⁻²; area coverage: 10.95 ± 0.81, 11.22 ± 0.56, and 8.57 ± 0.44%) and bottom layer (droplet deposition rate: 0.41 ± 0.06, 0.35 ± 0.03, and 0.33 ± 0.03 μL cm⁻²; droplet density: 22 ± 4.36, 17 ± 3.51, and 19 ± 4.51 droplets cm⁻²; area coverage: 2.78 ± 0.29, 2.95 ± 0.45, and 2.46 ± 0.20%, respectively). In the spray drift test, there was a higher droplet deposition rate (1.63 ± 0.09, 1.93 ± 0.05, and 1.82 ± 0.06 μL cm⁻²), area coverage (14.40 ± 0.07, 17.54 ± 0.36, and 16.42 ± 0.30%), and droplet density (46 ± 3.61, 54 ± 2.08, and 45 ± 3.21 No’s cm⁻²) in the target area as compared to the non-target area (droplet deposition rate: 0.88 ± 0.02, 0.46 ± 0.03, 0.22 ± 0.05, and 0.00 μL cm⁻²; droplet density: 23 ± 1.53, 11 ± 2.08, 6 ± 1.53, and 0.00 droplets cm⁻²; area coverage: 7.58 ± 0.34, 4.41 ± 0.19, 2.16 ± 0.05, and 0.00%, respectively), which may have been due to the downwash airflow produced by the multi-rotor propeller of the UAV sprayer. Finally, the UAV-based spraying technology results showed that the downwash air produced by the six-rotor propeller improved the penetrability of insecticide to crop leaves and led to a higher droplet deposition rate, droplet density, area coverage, and droplet penetrability on the upper layer, middle layer, and bottom layer of the plants.

Background: Now-a-days, use of machines is becoming important due to shortage of labour for field operations. One such case is seed drill sowing which warranted enough seed size for easy sowing. Pelleting of seed is one of the techniques through which the size of the seed can be increased thereby it can be sown easily by seed drill. Green gram is one of the important pulses which needs pelleting technique for mechanized sowing. Methods: The experiments on standardization of seed pelleting technique and evaluating under field conditions were conducted in the Department of Seed Science and Technology, Seed Centre, Tamil Nadu Agricultural University, Coimbatore, India during 2019-2022. The green gram var. CO 8 seeds were primed and pelleted with TNAU pelleting mixture. Then, the pelleted seeds were sown by pneumatic seed drill and evaluated for its field performance. Result: The results showed that the green gram seeds primed with MnSO4 @ 0.5% and pelleted with 7 layers of TNAU pelleting mixture along with imidacloprid 70 WS @ 7 g/kg have recorded increased seedling vigour without affecting the seed germination. Also, the seeds sown by seed drill have recorded with better plant growth traits and higher yield. Also, single seed placement was achieved by seed drill sowing and about 25% seeds can be saved.

Unmanned Aircraft Systems (UAS), also known as Unmanned Aerial Vehicles (UAVs) or drones, can fly autonomously. The application of drones is versatile in many applications such as spraying chemicals, spreading seeds, surveying farmlands, monitoring crops, and managing livestock. Their potential applications are seemingly endless. UAVs can be classified into numerous categories. As seen in Table 14.1, shows the category of UAVs. Figure 14.1 shows the classification of drones based on flying principle. <!----> <!----> <!----> <!---->

To assess the effect of sowing methods and weed management on summer irrigated black gram yield, a field experiment was conducted during the summer season of 2024 in the Department of Pulses at Tamil Nadu Agricultural University, Coimbatore. The treatments were employed in the Split plot design and replicated thrice. The sowing methods and weed management treatments were allotted in the main and subplots, respectively. The findings showed that, among the sowing methods, furrow-irrigated raised beds recorded a lower weed density and concerning weed management, the pre-emergence (PE) application of Pendimethalin 30 % + Imazethapyr 2 % EC (ready mix) and weeding with power-operated weeder at 20 DAS resulted in lesser weeds which leads to higher weed control efficiency (79.88 %) and higher seed yield in both furrow-irrigated raised beds and pre-emergence (PE) application of Pendimethalin 30 % + Imazethapyr 2 % EC (ready mix) and weeding with power-operated weeder at 20 DAS.

Turmeric (Curcuma longa), a major crop in India accounting for 80 % of global production, faces challenges such as pest infestations, diseases, climate change and economic volatility, necessitating precision agriculture strategies to enhance sustainability. Planting is a critical determinant of crop yield and quality; however, conventional manual methods remain labor-intensive and inefficient, underscoring the need for mechanization. Mechanical turmeric planters have been shown to decrease the cost and time associated with planting by 59.52% and 96.57 %, respectively. This review examines key turmeric planting methods, including flat-bed, ridge-and-furrow and raised-bed systems, while analyzing the engineering properties of turmeric rhizomes relevant to planter design. It has been found that ridge planting has produced 86.78 q ha-1 of yield compared to flat-bed planting of 67.26 q ha-1 and raised bed planting significantly reduced disease incidence, with only 9.6 % leaf spot and no rhizome rot observed, compared to 22.5 % leaf spot using the flat bed method. The evolution of planting technology from manual to semi-automatic and fully automatic systems is discussed, along with a critical assessment of metering mechanisms such as vertical rotating discs, multistage rotating cups and auger conveyors. The influence of furrow opener design and operational parameters, including planting speed and accuracy, on field performance was also reviewed. By identifying research gaps related to the impact of rhizome properties on planter efficiency, this study concludes that optimized automatic planters tailored to turmeric’s specific agronomic requirements can enhance planting precision, improve productivity and contribute to sustainable cultivation practices.

Conventional farming guidelines lack detail, providing crop suggestions without location-based advice. This makes it challenging for farmers to make informed decisions about where to plant crops, affecting their productivity and profitability. The reason for tackling this issue lies in the potential of cassava, a crop that can withstand drought conditions to thrive if the right areas are identified accurately. This evaluation has a look at the way in which advanced technologies as well as artificial intelligence (AI) can be used to revolutionize mechanized cassava planting, with the objective of increasing productivity and sustainability. Cassava, a nutrition base for over 500 million people worldwide, is sourced from India, especially in the Tamil Nadu and Kerala regions. However, its existence is subject to certain problems that are linked to the conventional method of planting, like inconsistency in planting depths, wrong sweating of the stem and clogging of the channel, which all in turn serve to lower yields and increase labor costs. The former factors caused by mechanical devices like two-ploughed and three-ploughed planters have already been close to the economies of scale by achieving better planting efficiencies and consistency, but they are still presenting some difficulties. Some of the latest advances, such as tractor-operated single-row stake cutter-planters and rotary dibble-type planters, provide alternatives that are not only cheaper but also have the highest degree of accuracy when it comes to planters. Furthermore, the introduction of IoT (Internet of Things) and machine learning and the use of big data analytics in farming are the new directions for precision agriculture. This review emphasizes that the research that is continually being carried out, the unbeatable innovation or technological development and the farmer education that is expected to be high enough are crucial for the full realization of smart in cassava cultivation. In this way, we will be closer to a more sustainable type of agriculture and improved food security. By embracing these innovations, agriculture can be modernized, empowering farmers to overcome obstacles and achieve increased productivity. The data and findings highlighted in the review demonstrate the effectiveness of mechanization in revolutionizing practices and bolstering global food security.