Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food security. Detection, identification, quantification, and diagnosis of plant diseases are crucial parts of precision agriculture and crop protection. Modernizing agriculture and improving production efficiency are significantly affected by using computer vision technology for crop disease diagnosis. This technology is notable for its non-destructive nature, speed, real-time responsiveness, and precision. Deep learning (DL), a recent breakthrough in computer vision, has become a focal point in agricultural plant protection that can minimize the biases of manually selecting disease spot features. This study reviews the techniques and tools used for automatic disease identification, state-of-the-art DL models, and recent trends in DL-based image analysis. The techniques, performance, benefits, drawbacks, underlying frameworks, and reference datasets of more than 278 research articles were analyzed and subsequently highlighted in accordance with the architecture of computer vision and deep learning models. Key findings include the effectiveness of imaging techniques and sensors like RGB, multispectral, and hyperspectral cameras for early disease detection. Researchers also evaluated various DL architectures, such as convolutional neural networks, vision transformers, generative adversarial networks, vision language models, and foundation models. Moreover, the study connects academic research with practical agricultural applications, providing guidance on the suitability of these models for production environments. This comprehensive review offers valuable insights into the current state and future directions of deep learning in plant disease detection, making it a significant resource for researchers, academicians, and practitioners in precision agriculture.

The goal of the current study was to create and assess the effectiveness of a hand-pulled ergonomically designed flame weeder. The developed weeder was tested in the field at three operating pressures (20, 30 and 40 Psi) and forward speeds (1.00, 1.25 and 1.50 km/h) to study their effects on plant damage, survival rates, weight preservation rates, weed management effectiveness, soil temperatures, and gas and energy consumption. Thereafter, at optimized values of forward speed and operating pressure, a comparative assessment of flame weeding with traditional methods (mechanical and manual weeding) was done in terms of weed control effectiveness, operational time, energy consumption, and cost of operation. Results showed that the optimal performance of the designed flame weeder was achieved when operated at a speed of 1 km/h and an operating pressure of 40 psi. The survival rate, weight preservation rate, weed control efficiency, change in soil temperature, recovery rate, plant damage, gas consumption, and energy consumption were observed to be 27.3 %, 32.5 %, 91.1 %, 40.74 °C, 8.5 %, 2.2 %, 4.05 kg/h, and 2500.24 MJ/ha, respectively, at optimized values of forward speed (1.00 km/h) and operating pressure (40 Psi). The actual field capacity, field efficiency and operating cost of the flame weeder were 0.0755 ha/h, 94.94 %, and 3620.81 ₹/ha, respectively. Hand weeding had the best level of weed control effectiveness, but it was a laborious, time-consuming process. When compared to manual weeding, flame weeding was 50.42 % cheaper and 94.82 % faster.

The discrete element modelling (DEM) is the popular modelling technique devoted to the modelling of discontinuous and granular materials. Calibration of DEM model parameters remains to be one of the key challenges in DEM material modelling. The proposed research focuses on a novel DOE approach known as definitive screening design (DSD) for the parameterization and screening of the most influential DEM parameters of the hysteretic spring and linear cohesion contact models using cone penetration tests. Furthermore, the most influential DEM parameters were optimized using another design of experiments (DOE) approach known as central composite design (CCD). The results of DSD showed that effect of yield strength of the particles on cone index (CI) appears to be influenced by the energy density. Other parameters such as static friction factors (soil–soil and soil–steel), energy density, and stiffness factor also showed positive effect on the cone index (CI). The parameters yield strength, coefficient of static friction (soil–soil), coefficient of static friction (soil–steel), energy density and stiffness factor were selected for the optimization through central composite design, and all remaining parameters were screened out owing to their inability to influence CI. The optimized values of the yield strength, coefficient of static friction (soil–soil), coefficient of static friction (soil–steel), energy density and stiffness factor are \(1\times {10}^{6}\) MPa, 0.68, 0.35, 20 kPa and 0.7, respectively. The developed model when exposed to the validation provided reasonable agreement between observed and simulated draft value with relative error varying between 1.7 and 7.21%. The developed model is also capable of predicting soil furrow and ridge profiles with relative error varying between 4.15 and 21.19 and 0.47 and 6.38%, respectively.

The geometric features and operational parameters of rotary blades on rotary tillers significantly impact their performance characteristics. The sweepback angle is a geometric feature of the 'L'-shaped rotary blade that has remained unexplored in previous studies. This study aimed to analyze the effect of geometrical and operational parameters on the performance dynamics of the 'L'-shaped rotary blade. The investigation was conducted using the discrete element method (DEM) and further validated through experiments conducted in a soil bin. The simulation experiment was conducted by dividing the particle bed into horizontal particle zones. The effect of the geometrical (sweepback angle) and operational parameters (forward speed, rotational speed, and depth) on the power requirement, disturbance intensity, and mixing index was studied. The novel method was adopted to determine the mixing capability of rotary blades in terms of sub-domain mixing index (SMI) and overall mixing index (OMI). The results revealed that the power requirements for a sweepback angle of 18° were 26.39% and 16.50% lower than those for sweepback angles of 6° and 12°, respectively. The sweepback angle tends to have the least effect on the overall mixing index compared to operational parameters. The average particle velocity decreased by 22.19% and 29.60% with sweepback angles of 12° and 18°, respectively, compared to the sweepback angle of 6°. The relative error during the experiment varied between 1.29% and 13.51%. It was concluded that the sweepback angle was found to be a feasible option for reducing the power requirement with good mixing indices.

In many Indian regions, paddy wheat is the main crop rotation and facing the problem of straw incorporation for seed bed preparation in short period. The handling of straw in combine harvested paddy fields is a significant issue in the paddy wheat rotation. In order to solve this issue, efforts were carried out to cut paddy straw into small pieces by the newly proposed counter-rotating blades, followed by the simultaneous incorporation of a rotary tiller into the soil. Therefore, a tractor operated chopping cum tilling mixing machine was developed. In the study, three different blades (straw management system (SMS) Serrated, cutter bar and SMS plain) were tested in the terms of torque and required to chop the straw. SMS serrated blade was best suitable for the chopping mechanism as it required minimum cutting torque for the straw bunches. The developed chopping cum tilling mixing machine was tested at three different levels of forward speed (1.77, 2.3, and 3 km h⁻¹), moisture content at three levels (35 ± 3.4, 24 ± 2.2 and 17 ± 2.6%) with fix rotary tiller rotational speed of 810 rev min⁻¹. Optimum operating condition of the machine was obtained at a forward speed of 1.9 km h⁻¹ and a moisture of 24%. At these optimized values, the mixing index (96%), mean weight diameter (7.9 mm), bulk density (1230 g cc⁻¹) and fuel consumption (3 l h⁻¹) were 96%, 7.9 mm, 1230 g cc⁻¹ and 3.0 l h⁻¹ respectively. The developed machine performs three operations i.e., chopping, tilling, and mixing in single pass for effective in-situ straw management.

A healthy orchard necessitates well-balanced nutrition. In order to achieve a high yield, fertilizer application in the proper amount and position is important. Existing fertilizer application methods (band placement, pellet application, and ring basin method) have limitations such as excessive fertilizer application, soil acidity, nutrient imbalance, soil structure damage, bulk density rise, and more. A spot fertilizer applicator that can dispense the proper amount of fertilizer at right site can reduce fertilizer waste, lowering pollution and input costs. As a result, a novel grooved belt type metering system was designed with an autonomous plant detection-based spot fertilizer application. The fertilizer placement and consistency of amount dispensed per plant of the applicator were assessed in the lab. The independent parameters were metering belt groove volume (50, 100 and 150 cm³), metering belt speed (8, 9 and 10 m.min⁻¹), plant spacing (45, 60, 75 and 90 cm) and forward speed (2, 2.5 and 3 km.h⁻¹). Forward speed had a considerable impact on the band length, whereas groove volume and belt speed had a substantial impact on the lateral placement parameters. Because plant spacing had no significant effect and the means of real and measured fertilizer application amounts were equal, the machine was found suitable for applying fertilizer with any plant spacing. Using a full factorial experimental design, the optimal values for independent parameters such as groove volume, plant spacing, belt speed, and forward speed were assessed 50 cm³, 78.4 cm, 8.74 m.min⁻¹, and 2.72 km.h⁻¹, respectively with a high desirability of 0.971. In comparison to mechanical sensing type available spot fertilizer applicators, the developed spot fertilizer applicator required half the sensing and actuation time and had three times less fluctuation in fertilizer dosing.